Fault detection and isolation in GPS receiver autonomous integrity monitoring based on chaos particle swarm optimization-particle filter algorithm

NASA Astrophysics Data System (ADS)

Wang, Ershen; Jia, Chaoying; Tong, Gang; Qu, Pingping; Lan, Xiaoyu; Pang, Tao

2018-03-01

The receiver autonomous integrity monitoring (RAIM) is one of the most important parts in an avionic navigation system. Two problems need to be addressed to improve this system, namely, the degeneracy phenomenon and lack of samples for the standard particle filter (PF). However, the number of samples cannot adequately express the real distribution of the probability density function (i.e., sample impoverishment). This study presents a GPS receiver autonomous integrity monitoring (RAIM) method based on a chaos particle swarm optimization particle filter (CPSO-PF) algorithm with a log likelihood ratio. The chaos sequence generates a set of chaotic variables, which are mapped to the interval of optimization variables to improve particle quality. This chaos perturbation overcomes the potential for the search to become trapped in a local optimum in the particle swarm optimization (PSO) algorithm. Test statistics are configured based on a likelihood ratio, and satellite fault detection is then conducted by checking the consistency between the state estimate of the main PF and those of the auxiliary PFs. Based on GPS data, the experimental results demonstrate that the proposed algorithm can effectively detect and isolate satellite faults under conditions of non-Gaussian measurement noise. Moreover, the performance of the proposed novel method is better than that of RAIM based on the PF or PSO-PF algorithm.

Using ANTS to explore small body populations in the solar system.

NASA Astrophysics Data System (ADS)

Clark, P. E.; Rilee, M.; Truszkowski, W.; Curtis, S.; Marr, G.; Chapman, C.

2001-11-01

ANTS (Autonomous Nano-Technology Swarm), a NASA advanced mission concept, is a large (100 to 1000 member) swarm of pico-class (1 kg) totally autonomous spacecraft that prospect the asteroid belt. Little data is available for asteroids because the vast majority are too small to be observed except in close proximity. Light curves are available for thousands of asteroids, confirmed trajectories for tens of thousands, detailed shape models for approximately ten. Asteroids originated in the transitional region between the inner (rocky) and outer (solidified gases) solar system. Many have remained largely unmodified since formation, and thus have more primitive composition than planetary surfaces. Determination of the systematic distribution of physical and compositional properties within the asteroid population is crucial in the understanding of solar system formation. The traditional exploration approach of using few, large spacecraft for sequential exploration, could be improved. Our far more cost-effective approach utilizes distributed intelligence in a swarm of tiny highly maneuverable spacecraft, each with specialized instrument capability (e.g., advanced computing, imaging, spectrometry). NASA is at the forefront of Intelligent Software Agents (ISAs) research, performing experiments in space and on the ground to advance deliberative and collaborative autonomous control techniques. The advanced development under consideration here is in the use of ISAs at a strategic level, to explore remote frontiers of the solar system, potentially involving a large class of objects such as asteroids. Supervised clusters of spacecraft operate simultaneously within a broadly defined framework of goals to select targets (> 1000) from among available candidates while developing scenarios for studying targets. Swarm members use solar sails to fly directly to asteroids > 1 kilometer in diameter, and then perform maneuvers appropriate for the instrument carried, ranging from hovering to orbiting. Selected members return with data and are replaced as needed.

Sambot II: A self-assembly modular swarm robot

NASA Astrophysics Data System (ADS)

Zhang, Yuchao; Wei, Hongxing; Yang, Bo; Jiang, Cancan

2018-04-01

The new generation of self-assembly modular swarm robot Sambot II, based on the original generation of self-assembly modular swarm robot Sambot, adopting laser and camera module for information collecting, is introduced in this manuscript. The visual control algorithm of Sambot II is detailed and feasibility of the algorithm is verified by the laser and camera experiments. At the end of this manuscript, autonomous docking experiments of two Sambot II robots are presented. The results of experiments are showed and analyzed to verify the feasibility of whole scheme of Sambot II.

Swarmathon 2017

NASA Image and Video Library

2017-04-20

In the Swarmathon competition at the Kennedy Space Center Visitor Complex, students were asked to develop computer code for the small robots, programming them to look for "resources" in the form of cubes with AprilTags, similar to barcodes. Teams developed search algorithms for the Swarmies to operate autonomously, communicating and interacting as a collective swarm similar to ants foraging for food.

NASA's Swarm Missions: The Challenge of Building Autonomous Software

NASA Technical Reports Server (NTRS)

Truszkowski, Walt; Hinchey, Mike; Rash, James; Rouff, Christopher

2004-01-01

The days of watching a massive manned cylinder thrust spectacularly off a platform into space might rapidly become ancient history when the National Aeronautics and Space Administration (NASA) introduces its new millenium mission class. Motivated by the need to gather more data than is possible with a single spacecraft, scientists have developed a new class of missions based on the efficiency and cooperative nature of a hive culture. The missions, aptly dubbed nanoswarm will be little more than mechanized colonies cooperating in their exploration of the solar system. Each swarm mission can have hundreds or even thousands of cooperating intelligent spacecraft that work in teams. The spacecraft must operate independently for long periods both in teams and individually, as well as have autonomic properties - self-healing, -configuring, -optimizing, and -protecting- to survive the harsh space environment. One swarm mission under concept development for 2020 to 2030 is the Autonomous Nano Technology Swarm (ANTS), in which a thousand picospacecraft, each weighing less than three pounds, will work cooperatively to explore the asteroid belt. Some spacecraft will form teams to catalog asteroid properties, such as mass, density, morphology, and chemical composition, using their respective miniature scientific instruments. Others will communicate with the data gatherers and send updates to mission elements on Earth. For software and systems development, this is uncharted territory that calls for revolutionary techniques.

The 'Biologically-Inspired Computing' Column

NASA Technical Reports Server (NTRS)

Hinchey, Mike

2006-01-01

The field of Biology changed dramatically in 1953, with the determination by Francis Crick and James Dewey Watson of the double helix structure of DNA. This discovery changed Biology for ever, allowing the sequencing of the human genome, and the emergence of a "new Biology" focused on DNA, genes, proteins, data, and search. Computational Biology and Bioinformatics heavily rely on computing to facilitate research into life and development. Simultaneously, an understanding of the biology of living organisms indicates a parallel with computing systems: molecules in living cells interact, grow, and transform according to the "program" dictated by DNA. Moreover, paradigms of Computing are emerging based on modelling and developing computer-based systems exploiting ideas that are observed in nature. This includes building into computer systems self-management and self-governance mechanisms that are inspired by the human body's autonomic nervous system, modelling evolutionary systems analogous to colonies of ants or other insects, and developing highly-efficient and highly-complex distributed systems from large numbers of (often quite simple) largely homogeneous components to reflect the behaviour of flocks of birds, swarms of bees, herds of animals, or schools of fish. This new field of "Biologically-Inspired Computing", often known in other incarnations by other names, such as: Autonomic Computing, Pervasive Computing, Organic Computing, Biomimetics, and Artificial Life, amongst others, is poised at the intersection of Computer Science, Engineering, Mathematics, and the Life Sciences. Successes have been reported in the fields of drug discovery, data communications, computer animation, control and command, exploration systems for space, undersea, and harsh environments, to name but a few, and augur much promise for future progress.

Gold rush - A swarm dynamics in games

NASA Astrophysics Data System (ADS)

Zelinka, Ivan; Bukacek, Michal

2017-07-01

This paper is focused on swarm intelligence techniques and its practical use in computer games. The aim is to show how a swarm dynamics can be generated by multiplayer game, then recorded, analyzed and eventually controlled. In this paper we also discuss possibility to use swarm intelligence instead of game players. Based on our previous experiments two games, using swarm algorithms are mentioned briefly here. The first one is strategy game StarCraft: Brood War, and TicTacToe in which SOMA algorithm has also take a role of player against human player. Open research reported here has shown potential benefit of swarm computation in the field of strategy games and players strategy based on swarm behavior record and analysis. We propose new game called Gold Rush as an experimental environment for human or artificial swarm behavior and consequent analysis.

ALI (Autonomous Lunar Investigator): Revolutionary Approach to Exploring the Moon with Addressable Reconfigurable Technology

NASA Astrophysics Data System (ADS)

Clark, P. E.; Curtis, S. A.; Rilee, M. L.; Floyd, S. R.

2005-03-01

Addressable Reconfigurable Technology (ART), conceived for future ANTS (Autonomous Nanotechnology Swarm) Architectures, is now implemented as Autonomous Lunar Investigator (ALI) rovers, a mission concept allowing autonomous exploration of the lunar farside and poles within 10 years.

A Survey of Formal Methods for Intelligent Swarms

NASA Technical Reports Server (NTRS)

Truszkowski, Walt; Rash, James; Hinchey, Mike; Rouff, Chrustopher A.

2004-01-01

Swarms of intelligent autonomous spacecraft, involving complex behaviors and interactions, are being proposed for future space exploration missions. Such missions provide greater flexibility and offer the possibility of gathering more science data than traditional single spacecraft missions. The emergent properties of swarms make these missions powerful, but simultaneously far more difficult to design, and to assure that the proper behaviors will emerge. These missions are also considerably more complex than previous types of missions, and NASA, like other organizations, has little experience in developing or in verifying and validating these types of missions. A significant challenge when verifying and validating swarms of intelligent interacting agents is how to determine that the possible exponential interactions and emergent behaviors are producing the desired results. Assuring correct behavior and interactions of swarms will be critical to mission success. The Autonomous Nano Technology Swarm (ANTS) mission is an example of one of the swarm types of missions NASA is considering. The ANTS mission will use a swarm of picospacecraft that will fly from Earth orbit to the Asteroid Belt. Using an insect colony analogy, ANTS will be composed of specialized workers for asteroid exploration. Exploration would consist of cataloguing the mass, density, morphology, and chemical composition of the asteroids, including any anomalous concentrations of specific minerals. To perform this task, ANTS would carry miniaturized instruments, such as imagers, spectrometers, and detectors. Since ANTS and other similar missions are going to consist of autonomous spacecraft that may be out of contact with the earth for extended periods of time, and have low bandwidths due to weight constraints, it will be difficult to observe improper behavior and to correct any errors after launch. Providing V&V (verification and validation) for this type of mission is new to NASA, and represents the cutting edge in system correctness, and requires higher levels of assurance than other (traditional) missions that use a single or small number of spacecraft that are deterministic in nature and have near continuous communication access. One of the highest possible levels of assurance comes from the application of formal methods. Formal methods are mathematics-based tools and techniques for specifying and verifying (software and hardware) systems. They are particularly useful for specifying complex parallel systems, such as exemplified by the ANTS mission, where the entire system is difficult for a single person to fully understand, a problem that is multiplied with multiple developers. Once written, a formal specification can be used to prove properties of a system (e.g., the underlying system will go from one state to another or not into a specific state) and check for particular types of errors (e.g., race or livelock conditions). A formal specification can also be used as input to a model checker for further validation. This report gives the results of a survey of formal methods techniques for verification and validation of space missions that use swarm technology. Multiple formal methods were evaluated to determine their effectiveness in modeling and assuring the behavior of swarms of spacecraft using the ANTS mission as an example system. This report is the first result of the project to determine formal approaches that are promising for formally specifying swarm-based systems. From this survey, the most promising approaches were selected and are discussed relative to their possible application to the ANTS mission. Future work will include the application of an integrated approach, based on the selected approaches identified in this report, to the formal specification of the ANTS mission.

Distance-Based Behaviors for Low-Complexity Control in Multiagent Robotics

NASA Astrophysics Data System (ADS)

Pierpaoli, Pietro

Several biological examples show that living organisms cooperate to collectively accomplish tasks impossible for single individuals. More importantly, this coordination is often achieved with a very limited set of information. Inspired by these observations, research on autonomous systems has focused on the development of distributed control techniques for control and guidance of groups of autonomous mobile agents, or robots. From an engineering perspective, when coordination and cooperation is sought in large ensembles of robotic vehicles, a reduction in hardware and algorithms' complexity becomes mandatory from the very early stages of the project design. The research for solutions capable of lowering power consumption, cost and increasing reliability are thus worth investigating. In this work, we studied low-complexity techniques to achieve cohesion and control on swarms of autonomous robots. Starting from an inspiring example with two-agents, we introduced effects of neighbors' relative positions on control of an autonomous agent. The extension of this intuition addressed the control of large ensembles of autonomous vehicles, and was applied in the form of a herding-like technique. To this end, a low-complexity distance-based aggregation protocol was defined. We first showed that our protocol produced a cohesion aggregation among the agent while avoiding inter-agent collisions. Then, a feedback leader-follower architecture was introduced for the control of the swarm. We also described how proximity measures and probability of collisions with neighbors can also be used as source of information in highly populated environments.

You Can't Get There From Here! Problems and Potential Solutions in Developing New Classes of Complex Systems

NASA Technical Reports Server (NTRS)

Hinchey, Michael G.; Rash, James L.; Truszkowski, Walter F.; Rouff, Christopher A.; Sterritt, Roy

2005-01-01

The explosion of capabilities and new products within the sphere of Information Technology (IT) has fostered widespread, overly optimistic opinions regarding the industry, based on common but unjustified assumptions of quality and correctness of software. These assumptions are encouraged by software producers and vendors, who at this late date have not succeeded in finding a way to overcome the lack of an automated, mathematically sound way to develop correct systems from requirements. NASA faces this dilemma as it envisages advanced mission concepts that involve large swarms of small spacecraft that will engage cooperatively to acheve science goals. Such missions entail levels of complexity that beg for new methods for system development far beyond today's methods, which are inadequate for ensuring correct behavior of large numbers of interacting intelligent mission elements. New system development techniques recently devised through NASA-led research will offer some innovative approaches to achieving correctness in complex system development, including autonomous swarm missions that exhibit emergent behavior, as well as general software products created by the computing industry.

ANTS: A New Concept for Very Remote Exploration with Intelligent Software Agents

NASA Astrophysics Data System (ADS)

Clark, P. E.; Curtis, S.; Rilee, M.; Truszkowski, W.; Iyengar, J.; Crawford, H.

2001-12-01

ANTS (Autonomous Nano-Technology Swarm), a NASA advanced mission concept, is a large (100 to 1000 member) swarm of pico-class (1 kg) totally autonomous spacecraft that prospect the asteroid belt. As the capacity and complexity of hardware and software, and the sophistication of technical and scientific goals have increased, greater cost constraints have led to fewer resources and thus, the need to operate spacecraft with less frequent contact. At present, autonomous operation of spacecraft systems allows great capability of spacecraft to 'safe' themselves when conditions threaten spacecraft safety. To further develop spacecraft capability, NASA is at the forefront of Intelligent Software Agent (ISA) research, performing experiments in space and on the ground to advance deliberative and collaborative autonomous control techniques. Selected missions in current planning stages require small groups of spacecraft to cooperate at a tactical level to select and schedule measurements to be made by appropriate instruments to characterize rapidly unfolding real-time events on a routine basis. The next level of development, which we are considering here, is in the use of ISAs at a strategic level, to explore the final, remote frontiers of the solar system, potentially involving a large class of objects with only infrequent contact possible. Obvious mission candidates are mainbelt asteroids, a population consisting of more than a million small bodies. Although a large fraction of solar system objects are asteroids, little data is available for them because the vast majority of them are too small to be observed except in close proximity. Asteroids originated in the transitional region between the inner (rocky) and outer (solidified gases) solar system, have remained largely unmodified since formation, and thus have a more primitive composition which includes higher abundances of siderophile (metallic iron-associated) elements and volatiles than other planetary surfaces. As a result, there has been interest in asteroids as sources of exploitable resources. Far more reconnaissance is required before such a program is undertaken. A traditional mission approach (to explore larger asteroids sequentially) is not adequate for determining the systematic distribution of exploitable material in the asteroid population. Our approach involves the use of distributed intelligence in a swarm of tiny spacecraft, each with specialized instrument capability (e.g., advanced computing, imaging, spectrometry, etc.) to evaluate the resource potential of the entire population. Supervised clusters of spacecraft will operate simultaneously within a broadly defined framework of goals to select targets (>1000) from among available candidates and to develop scenarios for studying targets simultaneously. Spacecraft use solar sails to fly directly to asteroids 1 kilometer or greater in diameter. Selected swarm members return to Earth with data, replacements join the swarm as needed. We would like to acknowledge our students R. Watson, V. Cox, and F. Olukomo for their support of this work.

A swarm of autonomous miniature underwater robot drifters for exploring submesoscale ocean dynamics.

PubMed

Jaffe, Jules S; Franks, Peter J S; Roberts, Paul L D; Mirza, Diba; Schurgers, Curt; Kastner, Ryan; Boch, Adrien

2017-01-24

Measuring the ever-changing 3-dimensional (3D) motions of the ocean requires simultaneous sampling at multiple locations. In particular, sampling the complex, nonlinear dynamics associated with submesoscales (<1-10 km) requires new technologies and approaches. Here we introduce the Mini-Autonomous Underwater Explorer (M-AUE), deployed as a swarm of 16 independent vehicles whose 3D trajectories are measured near-continuously, underwater. As the vehicles drift with the ambient flow or execute preprogrammed vertical behaviours, the simultaneous measurements at multiple, known locations resolve the details of the flow within the swarm. We describe the design, construction, control and underwater navigation of the M-AUE. A field programme in the coastal ocean using a swarm of these robots programmed with a depth-holding behaviour provides a unique test of a physical-biological interaction leading to plankton patch formation in internal waves. The performance of the M-AUE vehicles illustrates their novel capability for measuring submesoscale dynamics.

A swarm of autonomous miniature underwater robot drifters for exploring submesoscale ocean dynamics

NASA Astrophysics Data System (ADS)

Jaffe, Jules S.; Franks, Peter J. S.; Roberts, Paul L. D.; Mirza, Diba; Schurgers, Curt; Kastner, Ryan; Boch, Adrien

2017-01-01

Measuring the ever-changing 3-dimensional (3D) motions of the ocean requires simultaneous sampling at multiple locations. In particular, sampling the complex, nonlinear dynamics associated with submesoscales (<1-10 km) requires new technologies and approaches. Here we introduce the Mini-Autonomous Underwater Explorer (M-AUE), deployed as a swarm of 16 independent vehicles whose 3D trajectories are measured near-continuously, underwater. As the vehicles drift with the ambient flow or execute preprogrammed vertical behaviours, the simultaneous measurements at multiple, known locations resolve the details of the flow within the swarm. We describe the design, construction, control and underwater navigation of the M-AUE. A field programme in the coastal ocean using a swarm of these robots programmed with a depth-holding behaviour provides a unique test of a physical-biological interaction leading to plankton patch formation in internal waves. The performance of the M-AUE vehicles illustrates their novel capability for measuring submesoscale dynamics.

Flocking algorithm for autonomous flying robots.

PubMed

Virágh, Csaba; Vásárhelyi, Gábor; Tarcai, Norbert; Szörényi, Tamás; Somorjai, Gergő; Nepusz, Tamás; Vicsek, Tamás

2014-06-01

Animal swarms displaying a variety of typical flocking patterns would not exist without the underlying safe, optimal and stable dynamics of the individuals. The emergence of these universal patterns can be efficiently reconstructed with agent-based models. If we want to reproduce these patterns with artificial systems, such as autonomous aerial robots, agent-based models can also be used in their control algorithms. However, finding the proper algorithms and thus understanding the essential characteristics of the emergent collective behaviour requires thorough and realistic modeling of the robot and also the environment. In this paper, we first present an abstract mathematical model of an autonomous flying robot. The model takes into account several realistic features, such as time delay and locality of communication, inaccuracy of the on-board sensors and inertial effects. We present two decentralized control algorithms. One is based on a simple self-propelled flocking model of animal collective motion, the other is a collective target tracking algorithm. Both algorithms contain a viscous friction-like term, which aligns the velocities of neighbouring agents parallel to each other. We show that this term can be essential for reducing the inherent instabilities of such a noisy and delayed realistic system. We discuss simulation results on the stability of the control algorithms, and perform real experiments to show the applicability of the algorithms on a group of autonomous quadcopters. In our case, bio-inspiration works in two ways. On the one hand, the whole idea of trying to build and control a swarm of robots comes from the observation that birds tend to flock to optimize their behaviour as a group. On the other hand, by using a realistic simulation framework and studying the group behaviour of autonomous robots we can learn about the major factors influencing the flight of bird flocks.

Collaborative Indoor Access Point Localization Using Autonomous Mobile Robot Swarm.

PubMed

Awad, Fahed; Naserllah, Muhammad; Omar, Ammar; Abu-Hantash, Alaa; Al-Taj, Abrar

2018-01-31

Localization of access points has become an important research problem due to the wide range of applications it addresses such as dismantling critical security threats caused by rogue access points or optimizing wireless coverage of access points within a service area. Existing proposed solutions have mostly relied on theoretical hypotheses or computer simulation to demonstrate the efficiency of their methods. The techniques that rely on estimating the distance using samples of the received signal strength usually assume prior knowledge of the signal propagation characteristics of the indoor environment in hand and tend to take a relatively large number of uniformly distributed random samples. This paper presents an efficient and practical collaborative approach to detect the location of an access point in an indoor environment without any prior knowledge of the environment. The proposed approach comprises a swarm of wirelessly connected mobile robots that collaboratively and autonomously collect a relatively small number of non-uniformly distributed random samples of the access point's received signal strength. These samples are used to efficiently and accurately estimate the location of the access point. The experimental testing verified that the proposed approach can identify the location of the access point in an accurate and efficient manner.

Collaborative Indoor Access Point Localization Using Autonomous Mobile Robot Swarm

PubMed Central

Awad, Fahed; Naserllah, Muhammad; Omar, Ammar; Abu-Hantash, Alaa; Al-Taj, Abrar

2018-01-01

Localization of access points has become an important research problem due to the wide range of applications it addresses such as dismantling critical security threats caused by rogue access points or optimizing wireless coverage of access points within a service area. Existing proposed solutions have mostly relied on theoretical hypotheses or computer simulation to demonstrate the efficiency of their methods. The techniques that rely on estimating the distance using samples of the received signal strength usually assume prior knowledge of the signal propagation characteristics of the indoor environment in hand and tend to take a relatively large number of uniformly distributed random samples. This paper presents an efficient and practical collaborative approach to detect the location of an access point in an indoor environment without any prior knowledge of the environment. The proposed approach comprises a swarm of wirelessly connected mobile robots that collaboratively and autonomously collect a relatively small number of non-uniformly distributed random samples of the access point’s received signal strength. These samples are used to efficiently and accurately estimate the location of the access point. The experimental testing verified that the proposed approach can identify the location of the access point in an accurate and efficient manner. PMID:29385042

Swarm autonomic agents with self-destruct capability

NASA Technical Reports Server (NTRS)

Hinchey, Michael G. (Inventor); Sterritt, Roy (Inventor)

2009-01-01

Systems, methods and apparatus are provided through which in some embodiments an autonomic entity manages a system by generating one or more stay alive signals based on the functioning status and operating state of the system. In some embodiments, an evolvable synthetic neural system is operably coupled to one or more evolvable synthetic neural systems in a hierarchy. The evolvable neural interface receives and generates heartbeat monitor signals and pulse monitor signals that are used to generate a stay alive signal that is used to manage the operations of the synthetic neural system. In another embodiment an asynchronous Alice signal (Autonomic license) requiring valid credentials of an anonymous autonomous agent is initiated. An unsatisfactory Alice exchange may lead to self-destruction of the anonymous autonomous agent for self-protection.

Swarm autonomic agents with self-destruct capability

NASA Technical Reports Server (NTRS)

Hinchey, Michael G. (Inventor); Sterritt, Roy (Inventor)

2011-01-01

Systems, methods and apparatus are provided through which in some embodiments an autonomic entity manages a system by generating one or more stay alive signals based on the functioning status and operating state of the system. In some embodiments, an evolvable synthetic neural system is operably coupled to one or more evolvable synthetic neural systems in a hierarchy. The evolvable neural interface receives and generates heartbeat monitor signals and pulse monitor signals that are used to generate a stay alive signal that is used to manage the operations of the synthetic neural system. In another embodiment an asynchronous Alice signal (Autonomic license) requiring valid credentials of an anonymous autonomous agent is initiated. An unsatisfactory Alice exchange may lead to self-destruction of the anonymous autonomous agent for self-protection.

SWARMS: Scalable sWarms of Autonomous Robots and Mobile Sensors

DTIC Science & Technology

2013-03-18

Pasqualetti, Antonio Franchi , Francesco Bullo. On optimal cooperative patrolling, 2010 49th IEEE Conference on Decision and Control (CDC). 2010/12/15 00...exhibits “ global stability” Provided a complete convergence proof for the adaptive version of the range only station keeping problem. Graph Theoretic

Exploring with PAM: Prospecting ANTS Missions for Solar System Surveys

NASA Astrophysics Data System (ADS)

Clark, P. E.; Rilee, M. L.; Curtis, S. A.

2003-03-01

ANTS (Autonomous Nano Technology Swarm of hundreds of picoclass autonomous spacecraft) have many applications. A version designed for surveying and the resource potential of the asteroid belt, called PAM (Prospecting ANTS Mission), is examined here.

In Situ Surveying of Saturn's Rings

NASA Astrophysics Data System (ADS)

Clark, P. E.; Rilee, M. L.; Curtis, S. A.; Cheung, C.

2004-03-01

Saturn Autonomous Ring Array (SARA) mission concept is an application for the Autonomous Nano-Technology Swarm (ANTS) architecture that would perform in situ observations of compositional and dynamic properties of ring particles, a challenge unachievable by previous mission designs.

Development of a frequency-modulated ultrasonic sensor inspired by bat echolocation

NASA Astrophysics Data System (ADS)

Kepa, Krzysztof; Abaid, Nicole

2015-03-01

Bats have evolved to sense using ultrasonic signals with a variety of different frequency signatures which interact with their environment. Among these signals, those with time-varying frequencies may enable the animals to gather more complex information for obstacle avoidance and target tracking. Taking inspiration from this system, we present the development of a sonar sensor capable of generating frequency-modulated ultrasonic signals. The device is based on a miniature mobile computer, with on board data capture and processing capabilities, which is designed for eventual autonomous operation in a robotic swarm. The hardware and software components of the sensor are detailed, as well their integration. Preliminary results for target detection using both frequency-modulated and constant frequency signals are discussed.

Multi-Target Tracking for Swarm vs. Swarm UAV Systems

DTIC Science & Technology

2012-09-01

Uhlmann, “Using covariance intersection for SLAM,” Robotics and Autonomous Systems, vol. 55, pp. 3–20, Jan. 2007. [10] R. B. G. Wolfgang Niehsen... Krause , J. Leskovec, and C. Guestrin, “Data association for topic intensity track- ing,” Proceedings of the 23rd international conference on Machine

ALI (Autonomous Lunar Investigator): Revolutionary Approach to Exploring the Moon with Addressable Reconfigurable Technology

NASA Technical Reports Server (NTRS)

Clark, P. E.; Curtis, S. A.; Rilee, M. L.; Floyd, S. R.

2005-01-01

Addressable Reconfigurable Technology (ART) based structures: Mission Concepts based on Addressable Reconfigurable Technology (ART), originally studied for future ANTS (Autonomous Nanotechnology Swarm) Space Architectures, are now being developed as rovers for nearer term use in lunar and planetary surface exploration. The architecture is based on the reconfigurable tetrahedron as a building block. Tetrahedra are combined to form space-filling networks, shaped for the required function. Basic structural components are highly modular, addressable arrays of robust nodes (tetrahedral apices) from which highly reconfigurable struts (tetrahedral edges), acting as supports or tethers, are efficiently reversibly deployed/stowed, transforming and reshaping the structures as required.

Particle Swarm Optimization

NASA Technical Reports Server (NTRS)

Venter, Gerhard; Sobieszczanski-Sobieski Jaroslaw

2002-01-01

The purpose of this paper is to show how the search algorithm known as particle swarm optimization performs. Here, particle swarm optimization is applied to structural design problems, but the method has a much wider range of possible applications. The paper's new contributions are improvements to the particle swarm optimization algorithm and conclusions and recommendations as to the utility of the algorithm, Results of numerical experiments for both continuous and discrete applications are presented in the paper. The results indicate that the particle swarm optimization algorithm does locate the constrained minimum design in continuous applications with very good precision, albeit at a much higher computational cost than that of a typical gradient based optimizer. However, the true potential of particle swarm optimization is primarily in applications with discrete and/or discontinuous functions and variables. Additionally, particle swarm optimization has the potential of efficient computation with very large numbers of concurrently operating processors.

A Communications Modeling System for Swarm-Based Sensors

DTIC Science & Technology

2003-09-01

6-10 6.6. Digital and Swarm System Performance Measures . . . . . . . . . . 6-21 7.1. Simulation computing hardware...detection and monitoring, and advances in computational capabilities have provided for embedded data analysis and the generation of information from raw... computing and manufacturing technology have made such systems possible. In order to harness this potential for Air Force applica- tions, a method of

Density Control of Multi-Agent Systems with Safety Constraints: A Markov Chain Approach

NASA Astrophysics Data System (ADS)

Demirer, Nazli

The control of systems with autonomous mobile agents has been a point of interest recently, with many applications like surveillance, coverage, searching over an area with probabilistic target locations or exploring an area. In all of these applications, the main goal of the swarm is to distribute itself over an operational space to achieve mission objectives specified by the density of swarm. This research focuses on the problem of controlling the distribution of multi-agent systems considering a hierarchical control structure where the whole swarm coordination is achieved at the high-level and individual vehicle/agent control is managed at the low-level. High-level coordination algorithms uses macroscopic models that describes the collective behavior of the whole swarm and specify the agent motion commands, whose execution will lead to the desired swarm behavior. The low-level control laws execute the motion to follow these commands at the agent level. The main objective of this research is to develop high-level decision control policies and algorithms to achieve physically realizable commanding of the agents by imposing mission constraints on the distribution. We also make some connections with decentralized low-level motion control. This dissertation proposes a Markov chain based method to control the density distribution of the whole system where the implementation can be achieved in a decentralized manner with no communication between agents since establishing communication with large number of agents is highly challenging. The ultimate goal is to guide the overall density distribution of the system to a prescribed steady-state desired distribution while satisfying desired transition and safety constraints. Here, the desired distribution is determined based on the mission requirements, for example in the application of area search, the desired distribution should match closely with the probabilistic target locations. The proposed method is applicable for both systems with a single agent and systems with large number of agents due to the probabilistic nature, where the probability distribution of each agent's state evolves according to a finite-state and discrete-time Markov chain (MC). Hence, designing proper decision control policies requires numerically tractable solution methods for the synthesis of Markov chains. The synthesis problem has the form of a Linear Matrix Inequality Problem (LMI), with LMI formulation of the constraints. To this end, we propose convex necessary and sufficient conditions for safety constraints in Markov chains, which is a novel result in the Markov chain literature. In addition to LMI-based, offline, Markov matrix synthesis method, we also propose a QP-based, online, method to compute a time-varying Markov matrix based on the real-time density feedback. Both problems are convex optimization problems that can be solved in a reliable and tractable way, utilizing existing tools in the literature. A Low Earth Orbit (LEO) swarm simulations are presented to validate the effectiveness of the proposed algorithms. Another problem tackled as a part of this research is the generalization of the density control problem to autonomous mobile agents with two control modes: ON and OFF. Here, each mode consists of a (possibly overlapping) finite set of actions, that is, there exist a set of actions for the ON mode and another set for the OFF mode. We give formulation for a new Markov chain synthesis problem, with additional measurements for the state transitions, where a policy is designed to ensure desired safety and convergence properties for the underlying Markov chain.

Swarming Unmanned Aerial Vehicles (UAVS): Extending Marine Aviation Ground Task Force Communications Using UAVS

DTIC Science & Technology

2015-03-02

balloons , large UAVs, and satellite communications are all employed to mitigate LOS and OTH communication on the battlefield. The Marine Corps’ fleets...Phang, N. S. (2006). Tethered operation of autonomous aerial vehicles to provide extended fields of view for autonomous ground vehicles (Master’s

Parameter Selection and Performance Comparison of Particle Swarm Optimization in Sensor Networks Localization.

PubMed

Cui, Huanqing; Shu, Minglei; Song, Min; Wang, Yinglong

2017-03-01

Localization is a key technology in wireless sensor networks. Faced with the challenges of the sensors' memory, computational constraints, and limited energy, particle swarm optimization has been widely applied in the localization of wireless sensor networks, demonstrating better performance than other optimization methods. In particle swarm optimization-based localization algorithms, the variants and parameters should be chosen elaborately to achieve the best performance. However, there is a lack of guidance on how to choose these variants and parameters. Further, there is no comprehensive performance comparison among particle swarm optimization algorithms. The main contribution of this paper is three-fold. First, it surveys the popular particle swarm optimization variants and particle swarm optimization-based localization algorithms for wireless sensor networks. Secondly, it presents parameter selection of nine particle swarm optimization variants and six types of swarm topologies by extensive simulations. Thirdly, it comprehensively compares the performance of these algorithms. The results show that the particle swarm optimization with constriction coefficient using ring topology outperforms other variants and swarm topologies, and it performs better than the second-order cone programming algorithm.

Parameter Selection and Performance Comparison of Particle Swarm Optimization in Sensor Networks Localization

PubMed Central

Cui, Huanqing; Shu, Minglei; Song, Min; Wang, Yinglong

2017-01-01

Localization is a key technology in wireless sensor networks. Faced with the challenges of the sensors’ memory, computational constraints, and limited energy, particle swarm optimization has been widely applied in the localization of wireless sensor networks, demonstrating better performance than other optimization methods. In particle swarm optimization-based localization algorithms, the variants and parameters should be chosen elaborately to achieve the best performance. However, there is a lack of guidance on how to choose these variants and parameters. Further, there is no comprehensive performance comparison among particle swarm optimization algorithms. The main contribution of this paper is three-fold. First, it surveys the popular particle swarm optimization variants and particle swarm optimization-based localization algorithms for wireless sensor networks. Secondly, it presents parameter selection of nine particle swarm optimization variants and six types of swarm topologies by extensive simulations. Thirdly, it comprehensively compares the performance of these algorithms. The results show that the particle swarm optimization with constriction coefficient using ring topology outperforms other variants and swarm topologies, and it performs better than the second-order cone programming algorithm. PMID:28257060

Computational intelligence-based optimization of maximally stable extremal region segmentation for object detection

NASA Astrophysics Data System (ADS)

Davis, Jeremy E.; Bednar, Amy E.; Goodin, Christopher T.; Durst, Phillip J.; Anderson, Derek T.; Bethel, Cindy L.

2017-05-01

Particle swarm optimization (PSO) and genetic algorithms (GAs) are two optimization techniques from the field of computational intelligence (CI) for search problems where a direct solution can not easily be obtained. One such problem is finding an optimal set of parameters for the maximally stable extremal region (MSER) algorithm to detect areas of interest in imagery. Specifically, this paper describes the design of a GA and PSO for optimizing MSER parameters to detect stop signs in imagery produced via simulation for use in an autonomous vehicle navigation system. Several additions to the GA and PSO are required to successfully detect stop signs in simulated images. These additions are a primary focus of this paper and include: the identification of an appropriate fitness function, the creation of a variable mutation operator for the GA, an anytime algorithm modification to allow the GA to compute a solution quickly, the addition of an exponential velocity decay function to the PSO, the addition of an "execution best" omnipresent particle to the PSO, and the addition of an attractive force component to the PSO velocity update equation. Experimentation was performed with the GA using various combinations of selection, crossover, and mutation operators and experimentation was also performed with the PSO using various combinations of neighborhood topologies, swarm sizes, cognitive influence scalars, and social influence scalars. The results of both the GA and PSO optimized parameter sets are presented. This paper details the benefits and drawbacks of each algorithm in terms of detection accuracy, execution speed, and additions required to generate successful problem specific parameter sets.

In Situ Surveying of Saturn's Rings

NASA Technical Reports Server (NTRS)

Clark, P. E.; Curtis, S. A.; Rilee, M. L.; Cheung, C.

2004-01-01

The Saturn Autonomous Ring Array (SARA) mission concept is a new application for the Autonomous Nano-Technology Swarm (ANTS) architecture, a paradigm being developed for exploration of high surface area and/or multibody targets to minimize costs and maximize effectiveness of survey operations. Systems designed with ANTS architecture are built from potentially very large numbers of highly autonomous, yet socially interactive, specialists, in approximately ten specialist classes. Here, we analyze requirements for such a mission as well as specialized autonomous operations which would support this application.

SMART Power Systems for ANTS Missions

NASA Astrophysics Data System (ADS)

Clark, P. E.; Floyd, S. R.; Curtis, S. A.; Rilee, M. L.

2005-02-01

Autonomous NanoTechnology Swarm (ANTS) Architecture is based on Addressable Reconfigurable Technology (ART) adaptable for the full spectrum of activities in space. ART systems based on currently available electromechanical (EMS) technology could support human crews on the lunar surface within the next 10 to 15 years. Two or more decades from now, NEMS (Super Miniaturized ART or SMART) technology could perform fully autonomous surveys and operations beyond the reach of human crews. Power system requirements would range from 1 kg to generate tens of Watts for near term ART applications, such as a lunar or Mars Lander Amorphous Rover Antenna (LARA), to <0.1 kg to generate hundreds of mWatts for more advanced SMART applications.

Power Provision Based on Self-Sacrificing Craft

NASA Technical Reports Server (NTRS)

Hinchey, Michael G. (Inventor); Sterrit, Roy (Inventor); Vassev, Emil I. (Inventor); Hinchey, Bridget (Inventor)

2015-01-01

A biologically-inspired system and method is provided for self-adapting behavior of swarm-based exploration missions, whereby individual components, for example, spacecraft, in the system can sacrifice themselves for the greater good of the entire system. The self-sacrifice can involve donating resources or assets, such as power provisions, to a different component of an autonomous system. A receiving component of the system can benefit from receiving the donated resource or power provision.

UAV Swarm Mission Planning Development Using Evolutionary Algorithms - Part I

DTIC Science & Technology

2008-05-01

desired behaviors in autonomous vehicles is a difficult problem at best and in general prob- ably impossible to completely resolve in complex dynamic...associated behaviors. Various techniques inspired by biological self-organized systems as found in forging insects and flocking birds, revolve around...swarms of heterogeneous vehicles in a distributed simulation system with animated graphics. Statistical measurements and observations indicate that bio

Swarmathon 2017

NASA Image and Video Library

2017-04-19

In the Swarmathon competition at the Kennedy Space Center Visitor Complex, students were asked to develop computer code for the small robots, programming them to look for "resources" in the form of AprilTag cubes, similar to barcodes. Teams developed search algorithms for the Swarmies to operate autonomously, communicating and interacting as a collective swarm similar to ants foraging for food. In the spaceport's second annual Swarmathon, 20 teams representing 22 minority serving universities and community colleges were invited to develop software code to operate these innovative robots known as "Swarmies" to help find resources when astronauts explore distant locations, such as the moon or Mars.

Swarmathon 2017

NASA Image and Video Library

2017-04-20

In the Swarmathon competition at the Kennedy Space Center Visitor Complex, students were asked to develop computer code for the small robots, programming them to look for "resources" in the form of cubes with AprilTags, similar to barcodes. Teams developed search algorithms for innovative robots known as "Swarmies" to operate autonomously, communicating and interacting as a collective swarm similar to ants foraging for food. In the spaceport's second annual Swarmathon, 20 teams representing 22 minority serving universities and community colleges were invited to participate. Similar robots could help find resources when astronauts explore distant locations, such as the moon or Mars.

Swarmathon 2018

NASA Image and Video Library

2018-04-18

In the Swarmathon competition at the Kennedy Space Center Visitor Complex, students were asked to develop computer code for the small robots, programming them to look for "resources" in the form of AprilTag cubes, similar to barcodes. Teams developed search algorithms for the Swarmies to operate autonomously, communicating and interacting as a collective swarm similar to ants foraging for food. In the spaceport's third annual Swarmathon, 23 teams represented 24 minority serving universities and community colleges were invited to develop software code to operate these innovative robots known as "Swarmies" to help find resources when astronauts explore distant locations, such as the Moon or Mars.

Swarmathon 2018

NASA Image and Video Library

2018-04-17

In the Swarmathon competition at the Kennedy Space Center Visitor Complex, students were asked to develop computer code for the small robots, programming them to look for "resources" in the form of AprilTag cubes, similar to barcodes. Teams developed search algorithms for the Swarmies to operate autonomously, communicating and interacting as a collective swarm similar to ants foraging for food. In the spaceport's third annual Swarmathon, 23 teams represented 24 minority serving universities and community colleges were invited to develop software code to operate these innovative robots known as "Swarmies" to help find resources when astronauts explore distant locations, such as the Moon or Mars.

Autonomous NanoTechnology Swarm (ANTS) Prospecting Asteroid Mission (PAM), Asteroid Proximity Operations

NASA Technical Reports Server (NTRS)

Marr, Greg; Cooley, Steve; Roithmayr, Carlos; Kay-Bunnell, Linda; Williams, Trevor

2004-01-01

The Autonomous NanoTechnology Swarm (ANTS) is a generic mission architecture based on spatially distributed spacecraft, autonomous and redundant components, and hierarchical organization. The ANTS Prospecting Asteroid Mission (PAM) is an ANTS application which will nominally use a swarm of 1000 spacecraft. There would be 10 types of "specialists" with common spacecraft buses. There would be 10 subswarms of approximately 100 spacecraft each or approximately 10 of each specialist in each swarm. The ANTS PAM primary objective is the exploration of the asteroid belt in search of resources and material with astrobiologically relevant origins and signatures. The ANTS PAM spacecraft will nominally be released from a station in an Earth-Moon L1 libration point orbit, and they will use Solar sails for propulsion. The sail structure would be highly flexible, capable of changing morphology to change cross-section for capture of sunlight or to form effective "tip vanes" for attitude control. ANTS PAM sails would be capable of full to partial deployment, to change effective sail area and center of pressure, and thus allow attitude control. Results of analysis of a transfer trajectory from Earth to a sample target asteroid will be presented. ANTS PAM will require continuous coverage of different asteroid locations as close as one to two asteroid "diameters" from the surface of the asteroid for periods of science data collection during asteroid proximity operations. Hovering spacecraft could meet the science data collection objectives. The results of hovering analysis will be presented. There are locations for which hovering is not possible, for example on the illuminated side of the asteroid. For cases where hovering is not possible, the results of utilizing asteroid formations to orbit the asteroid and achieve the desired asteroid viewing will be presented for sample asteroids. The ability of ANTS PAM to reduce the area of the solar sail during asteroid proximity operations is critical to the maintenance of orbiting formations for a period of time. Results of analysis of potential "traffic" problems during asteroid proximity operations will be presented.

Hybrid glowworm swarm optimization for task scheduling in the cloud environment

NASA Astrophysics Data System (ADS)

Zhou, Jing; Dong, Shoubin

2018-06-01

In recent years many heuristic algorithms have been proposed to solve task scheduling problems in the cloud environment owing to their optimization capability. This article proposes a hybrid glowworm swarm optimization (HGSO) based on glowworm swarm optimization (GSO), which uses a technique of evolutionary computation, a strategy of quantum behaviour based on the principle of neighbourhood, offspring production and random walk, to achieve more efficient scheduling with reasonable scheduling costs. The proposed HGSO reduces the redundant computation and the dependence on the initialization of GSO, accelerates the convergence and more easily escapes from local optima. The conducted experiments and statistical analysis showed that in most cases the proposed HGSO algorithm outperformed previous heuristic algorithms to deal with independent tasks.

Seismicity And Accretion Process Along The North Mid-Atlantic Ridge From The SIRENA Autonomous Hydrophone Data

NASA Astrophysics Data System (ADS)

Perrot, J.; Goslin, J.; Dziak, R. P.; Haxel, J. H.; Maia, M. A.; Tisseau, C.; Royer, J.

2009-12-01

The seismicity of the North Atlantic Ocean was recorded by the SIRENA array of 6 autonomous underwater hydrophones (AUH) moored within the SOFAR channel on the flanks of the Mid-Atlantic Ridge (MAR). The instruments were deployed north of the Azores Plateau between 40° and 50°N from June 2002 to September 2003. The low attenuation properties of the SOFAR channel for earthquake T-wave propagation result in a detection threshold reduction to a magnitude completeness level (Mc) of ~2.8, to be compared to a Mc~4.7 for MAR events recorded by land-based seismic networks. A spatio-temporal analysis was performed over the 1696 events localized inside the SIRENA array. For hydrophone-derived catalogs, the acoustic magnitude, or Source Level (SL), is used as a measure of earthquake size. The ''source level completeness'', above which the data set is complete, is SLc=208 dB. The SIRENA catalog was searched for swarms using the cluster software of the SEISAN distribution. A minimum SL of 210 dB was chosen to detect a possible mainshock, and all subsequent events within 40 days following the possible mainshock, located within a radius of 15 km from the mainshock were considered as events of the swarm. 15 km correspond to the maximum fault length in a slow-ridge context. 11 swarms with more than 15 events were detected along the MAR between 40°et 50°N during the SIRENA deployment. The maximum number of earthquakes in a swarm is 40 events. The SL vs. time distribution within each swarm allowed a first discrimination between the swarms occurring in a tectonic context and those which can be attributed to volcanic processes, the latter showing a more constant SL vs. time distribution. Moreover, the swarms occurring in a tectonic context show a "mainshock-afterschock" distribution of the cumulative number of events vs. time, fitting a Modified Omori Law. The location of tectonic and volcanic swarms correlates well with regions where a positive and negative Mantle Bouguer Anomalies (MBAs)(Maia et al., 2007) are observed, indicating the presence of thinner/colder and thicker/warmer crust respectively. Our results thus show that hydrophone data can be fruitfully used to help and characterize active ridge processes at various spatial scales. Maia M., J. Goslin, and P. Gente (2007), Evolution of accretion processes along the Mid-Atlantic Ridge north of the Azores since 5.5 Ma: An insight into the interactions between the ridge and the plume, Geochem. Geophys. Geosyst., 8.

A joint swarm intelligence algorithm for multi-user detection in MIMO-OFDM system

NASA Astrophysics Data System (ADS)

Hu, Fengye; Du, Dakun; Zhang, Peng; Wang, Zhijun

2014-11-01

In the multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) system, traditional multi-user detection (MUD) algorithms that usually used to suppress multiple access interference are difficult to balance system detection performance and the complexity of the algorithm. To solve this problem, this paper proposes a joint swarm intelligence algorithm called Ant Colony and Particle Swarm Optimisation (AC-PSO) by integrating particle swarm optimisation (PSO) and ant colony optimisation (ACO) algorithms. According to simulation results, it has been shown that, with low computational complexity, the MUD for the MIMO-OFDM system based on AC-PSO algorithm gains comparable MUD performance with maximum likelihood algorithm. Thus, the proposed AC-PSO algorithm provides a satisfactory trade-off between computational complexity and detection performance.

Swarm formation control utilizing elliptical surfaces and limiting functions.

PubMed

Barnes, Laura E; Fields, Mary Anne; Valavanis, Kimon P

2009-12-01

In this paper, we present a strategy for organizing swarms of unmanned vehicles into a formation by utilizing artificial potential fields that were generated from normal and sigmoid functions. These functions construct the surface on which swarm members travel, controlling the overall swarm geometry and the individual member spacing. Nonlinear limiting functions are defined to provide tighter swarm control by modifying and adjusting a set of control variables that force the swarm to behave according to set constraints, formation, and member spacing. The artificial potential functions and limiting functions are combined to control swarm formation, orientation, and swarm movement as a whole. Parameters are chosen based on desired formation and user-defined constraints. This approach is computationally efficient and scales well to different swarm sizes, to heterogeneous systems, and to both centralized and decentralized swarm models. Simulation results are presented for a swarm of 10 and 40 robots that follow circle, ellipse, and wedge formations. Experimental results are included to demonstrate the applicability of the approach on a swarm of four custom-built unmanned ground vehicles (UGVs).

A meta-analysis of human-system interfaces in unmanned aerial vehicle (UAV) swarm management.

PubMed

Hocraffer, Amy; Nam, Chang S

2017-01-01

A meta-analysis was conducted to systematically evaluate the current state of research on human-system interfaces for users controlling semi-autonomous swarms composed of groups of drones or unmanned aerial vehicles (UAVs). UAV swarms pose several human factors challenges, such as high cognitive demands, non-intuitive behavior, and serious consequences for errors. This article presents findings from a meta-analysis of 27 UAV swarm management papers focused on the human-system interface and human factors concerns, providing an overview of the advantages, challenges, and limitations of current UAV management interfaces, as well as information on how these interfaces are currently evaluated. In general allowing user and mission-specific customization to user interfaces and raising the swarm's level of autonomy to reduce operator cognitive workload are beneficial and improve situation awareness (SA). It is clear more research is needed in this rapidly evolving field. Copyright © 2016 Elsevier Ltd. All rights reserved.

New numerical methods for open-loop and feedback solutions to dynamic optimization problems

NASA Astrophysics Data System (ADS)

Ghosh, Pradipto

The topic of the first part of this research is trajectory optimization of dynamical systems via computational swarm intelligence. Particle swarm optimization is a nature-inspired heuristic search method that relies on a group of potential solutions to explore the fitness landscape. Conceptually, each particle in the swarm uses its own memory as well as the knowledge accumulated by the entire swarm to iteratively converge on an optimal or near-optimal solution. It is relatively straightforward to implement and unlike gradient-based solvers, does not require an initial guess or continuity in the problem definition. Although particle swarm optimization has been successfully employed in solving static optimization problems, its application in dynamic optimization, as posed in optimal control theory, is still relatively new. In the first half of this thesis particle swarm optimization is used to generate near-optimal solutions to several nontrivial trajectory optimization problems including thrust programming for minimum fuel, multi-burn spacecraft orbit transfer, and computing minimum-time rest-to-rest trajectories for a robotic manipulator. A distinct feature of the particle swarm optimization implementation in this work is the runtime selection of the optimal solution structure. Optimal trajectories are generated by solving instances of constrained nonlinear mixed-integer programming problems with the swarming technique. For each solved optimal programming problem, the particle swarm optimization result is compared with a nearly exact solution found via a direct method using nonlinear programming. Numerical experiments indicate that swarm search can locate solutions to very great accuracy. The second half of this research develops a new extremal-field approach for synthesizing nearly optimal feedback controllers for optimal control and two-player pursuit-evasion games described by general nonlinear differential equations. A notable revelation from this development is that the resulting control law has an algebraic closed-form structure. The proposed method uses an optimal spatial statistical predictor called universal kriging to construct the surrogate model of a feedback controller, which is capable of quickly predicting an optimal control estimate based on current state (and time) information. With universal kriging, an approximation to the optimal feedback map is computed by conceptualizing a set of state-control samples from pre-computed extremals to be a particular realization of a jointly Gaussian spatial process. Feedback policies are computed for a variety of example dynamic optimization problems in order to evaluate the effectiveness of this methodology. This feedback synthesis approach is found to combine good numerical accuracy with low computational overhead, making it a suitable candidate for real-time applications. Particle swarm and universal kriging are combined for a capstone example, a near optimal, near-admissible, full-state feedback control law is computed and tested for the heat-load-limited atmospheric-turn guidance of an aeroassisted transfer vehicle. The performance of this explicit guidance scheme is found to be very promising; initial errors in atmospheric entry due to simulated thruster misfirings are found to be accurately corrected while closely respecting the algebraic state-inequality constraint.

Alarm systems detect volcanic tremor and earthquake swarms during Redoubt eruption, 2009

NASA Astrophysics Data System (ADS)

Thompson, G.; West, M. E.

2009-12-01

We ran two alarm algorithms on real-time data from Redoubt volcano during the 2009 crisis. The first algorithm was designed to detect escalations in continuous seismicity (tremor). This is implemented within an application called IceWeb which computes reduced displacement, and produces plots of reduced displacement and spectrograms linked to the Alaska Volcano Observatory internal webpage every 10 minutes. Reduced displacement is a measure of the amplitude of volcanic tremor, and is computed by applying a geometrical spreading correction to a displacement seismogram. When the reduced displacement at multiple stations exceeds pre-defined thresholds and there has been a factor of 3 increase in reduced displacement over the previous hour, a tremor alarm is declared. The second algorithm was to designed to detect earthquake swarms. The mean and median event rates are computed every 5 minutes based on the last hour of data from a real-time event catalog. By comparing these with thresholds, three swarm alarm conditions can be declared: a new swarm, an escalation in a swarm, and the end of a swarm. The end of swarm alarm is important as it may mark a transition from swarm to continuous tremor. Alarms from both systems were dispatched using a generic alarm management system which implements a call-down list, allowing observatory scientists to be called in sequence until someone acknowledged the alarm via a confirmation web page. The results of this simple approach are encouraging. The tremor alarm algorithm detected 26 of the 27 explosive eruptions that occurred from 23 March - 4 April. The swarm alarm algorithm detected all five of the main volcanic earthquake swarm episodes which occurred during the Redoubt crisis on 26-27 February, 21-23 March, 26 March, 2-4 April and 3-7 May. The end-of-swarm alarms on 23 March and 4 April were particularly helpful as they were caused by transitions from swarm to tremor shortly preceding explosive eruptions; transitions which were detected much earlier by the swarm algorithm than they were by the tremor algorithm.

Autonomous microexplosives subsurface tracing system final report.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Engler, Bruce Phillip; Nogan, John; Melof, Brian Matthew

The objective of the autonomous micro-explosive subsurface tracing system is to image the location and geometry of hydraulically induced fractures in subsurface petroleum reservoirs. This system is based on the insertion of a swarm of autonomous micro-explosive packages during the fracturing process, with subsequent triggering of the energetic material to create an array of micro-seismic sources that can be detected and analyzed using existing seismic receiver arrays and analysis software. The project included investigations of energetic mixtures, triggering systems, package size and shape, and seismic output. Given the current absence of any technology capable of such high resolution mapping ofmore » subsurface structures, this technology has the potential for major impact on petroleum industry, which spends approximately $1 billion dollar per year on hydraulic fracturing operations in the United States alone.« less

Swarmathon 2017

NASA Image and Video Library

2017-04-19

A sign at the Kennedy Space Center Visitor Complex announces the second annual Swarmathon competition. Students were asked to develop computer code for the small robots, programming them to look for "resources" in the form of cubes with AprilTags, similar to barcodes. Teams developed search algorithms for the Swarmies to operate autonomously, communicating and interacting as a collective swarm similar to ants foraging for food. In the spaceport's second annual Swarmathon, 20 teams representing 22 minority serving universities and community colleges were invited to develop software code to operate these innovative robots known as "Swarmies" to help find resources when astronauts explore distant locations, such as the moon or Mars.

Autonomous Agents on Expedition: Humans and Progenitor Ants and Planetary Exploration

NASA Astrophysics Data System (ADS)

Rilee, M. L.; Clark, P. E.; Curtis, S. A.; Truszkowski, W. F.

2002-01-01

The Autonomous Nano-Technology Swarm (ANTS) is an advanced mission architecture based on a social insect analog of many specialized spacecraft working together to achieve mission goals. The principal mission concept driving the ANTS architecture is a Main Belt Asteroid Survey in the 2020s that will involve a thousand or more nano-technology enabled, artificially intelligent, autonomous pico-spacecraft (< 1 kg). The objective of this survey is to construct a compendium of composition, shape, and other physical parameter observations of a significant fraction of asteroid belt objects. Such an atlas will be of primary scientific importance for the understanding of Solar System origins and evolution and will lay the foundation for future exploration and capitalization of space. As the capabilities enabling ANTS are developed over the next two decades, these capabilities will need to be proven. Natural milestones for this process include the deployment of progenitors to ANTS on human expeditions to space and remote missions with interfaces for human interaction and control. These progenitors can show up in a variety of forms ranging from spacecraft subsystems and advanced handheld sensors, through complete prototypical ANTS spacecraft. A critical capability to be demonstrated is reliable, long-term autonomous operations across the ANTS architecture. High level, mission-oriented behaviors are to be managed by a control / communications layer of the swarm, whereas common low level functions required of all spacecraft, e.g. attitude control and guidance and navigation, are handled autonomically on each spacecraft. At the higher levels of mission planning and social interaction deliberative techniques are to be used. For the asteroid survey, ANTS acts as a large community of cooperative agents while for precursor missions there arises the intriguing possibility of Progenitor ANTS and humans acting together as agents. For optimal efficiency and responsiveness for individual spacecraft at the lowest levels of control we have been studying control methods based on nonlinear dynamical systems. We describe the critically important autonomous control architecture of the ANTS mission concept and a sequence of partial implementations that feature increasingly autonomous behaviors. The scientific and engineering roles that these Progenitor ANTS could play in human missions or remote missions with near real time human interactions, particularly to the Moon and Mars, will be discussed.

Developing Software for NASA Missions in the New Millennia

NASA Technical Reports Server (NTRS)

Truszkowski, Walt; Rash, James; Rouff, Christopher; Hinchey, Mike

2004-01-01

NASA is working on new mission concepts for exploration of the solar system. The concepts for these missions include swarms of hundreds of cooperating intelligent spacecraft which will be able to work in teams and gather more data than current single spacecraft missions. These spacecraft will not only have to operate independently for long periods of time on their own and in teams, but will also need to have autonomic properties of self healing, self configuring, self optimizing and self protecting for them to survive in the harsh space environment. Software for these types of missions has never been developed before and represents some of the challenges of software development in the new millennia. The Autonomous Nano Technology Swarm (ANTS) mission is an example of one of the swarm missions NASA is considering. The ANTS mission will use a swarm of one thousand pico-spacecraft that weigh less than five pounds. Using an insect colony analog, ANTS will explore the asteroid belt and catalog the mass, density, morphology, and chemical composition of the asteroids. Due to the size of the spacecraft, each will only carry a single miniaturized science instrument which will require them to cooperate in searching for asteroids that are of scientific interest. This article also discusses the ANTS mission, the properties the spacecraft will need and how that will effect future software development.

Multiscale Model of Swarming Bacteria

NASA Astrophysics Data System (ADS)

Alber, Mark

2011-03-01

Many bacteria can rapidly traverse surfaces from which they are extracting nutrient for growth. They generate flat, spreading colonies, called swarms because they resemble swarms of insects. In the beginning of the talk, swarms of the M. xanthus will be described in detail. Individual M. xanthus cells are elongated; they always move in the direction of their long axis; and they are in constant motion, repeatedly touching each other. As a cell glides, the slime capsule of a cell interacts with the bare agar surface, non-oriented slime which arises from the surface contact with the slime capsule, or oriented slime trails. Remarkably, cells regularly reverse their gliding directions. In this talk a detailed cell- and behavior-based computational model of M. xanthus swarming will be used to demonstrate that reversals of gliding direction and cell bending are essential for swarming and that specific reversal frequencies result in optimal swarming rate of the whole population. This suggests that the circuit regulating reversals evolved to its current sensitivity under selection for growth achieved by swarming.

A prototype of behavior selection mechanism based on emotion

NASA Astrophysics Data System (ADS)

Zhang, Guofeng; Li, Zushu

2007-12-01

In bionic methodology rather than in design methodology more familiar with, summarizing the psychological researches of emotion, we propose the biologic mechanism of emotion, emotion selection role in creature evolution and a anima framework including emotion similar to the classical control structure; and consulting Prospect Theory, build an Emotion Characteristic Functions(ECF) that computer emotion; two more emotion theories are added to them that higher emotion is preferred and middle emotion makes brain run more efficiently, emotional behavior mechanism comes into being. A simulation of proposed mechanism are designed and carried out on Alife Swarm software platform. In this simulation, a virtual grassland ecosystem is achieved where there are two kinds of artificial animals: herbivore and preyer. These artificial animals execute four types of behavior: wandering, escaping, finding food, finding sex partner in their lives. According the theories of animal ethnology, escaping from preyer is prior to other behaviors for its existence, finding food is secondly important behavior, rating is third one and wandering is last behavior. In keeping this behavior order, based on our behavior characteristic function theory, the specific functions of emotion computing are built of artificial autonomous animals. The result of simulation confirms the behavior selection mechanism.

Evolving and Controlling Perimeter, Rendezvous, and Foraging Behaviors in a Computation-Free Robot Swarm

DTIC Science & Technology

2016-04-01

cheap, disposable swarms of robots that can accomplish these tasks quickly and with- out much human supervision. While there has been a lot of work...have shown that swarms of robots so dumb that they have no computational power–they can’t even add or subtract, and have no memory can still collec...behaviors can be achieved using swarms of computation-free robots . Our work starts with the simple robot model proposed in [6] and adds a form of

Machining fixture layout optimization using particle swarm optimization algorithm

NASA Astrophysics Data System (ADS)

Dou, Jianping; Wang, Xingsong; Wang, Lei

2011-05-01

Optimization of fixture layout (locator and clamp locations) is critical to reduce geometric error of the workpiece during machining process. In this paper, the application of particle swarm optimization (PSO) algorithm is presented to minimize the workpiece deformation in the machining region. A PSO based approach is developed to optimize fixture layout through integrating ANSYS parametric design language (APDL) of finite element analysis to compute the objective function for a given fixture layout. Particle library approach is used to decrease the total computation time. The computational experiment of 2D case shows that the numbers of function evaluations are decreased about 96%. Case study illustrates the effectiveness and efficiency of the PSO based optimization approach.

Support Vector Machine Based on Adaptive Acceleration Particle Swarm Optimization

PubMed Central

Abdulameer, Mohammed Hasan; Othman, Zulaiha Ali

2014-01-01

Existing face recognition methods utilize particle swarm optimizer (PSO) and opposition based particle swarm optimizer (OPSO) to optimize the parameters of SVM. However, the utilization of random values in the velocity calculation decreases the performance of these techniques; that is, during the velocity computation, we normally use random values for the acceleration coefficients and this creates randomness in the solution. To address this problem, an adaptive acceleration particle swarm optimization (AAPSO) technique is proposed. To evaluate our proposed method, we employ both face and iris recognition based on AAPSO with SVM (AAPSO-SVM). In the face and iris recognition systems, performance is evaluated using two human face databases, YALE and CASIA, and the UBiris dataset. In this method, we initially perform feature extraction and then recognition on the extracted features. In the recognition process, the extracted features are used for SVM training and testing. During the training and testing, the SVM parameters are optimized with the AAPSO technique, and in AAPSO, the acceleration coefficients are computed using the particle fitness values. The parameters in SVM, which are optimized by AAPSO, perform efficiently for both face and iris recognition. A comparative analysis between our proposed AAPSO-SVM and the PSO-SVM technique is presented. PMID:24790584

A Parallel Particle Swarm Optimization Algorithm Accelerated by Asynchronous Evaluations

NASA Technical Reports Server (NTRS)

Venter, Gerhard; Sobieszczanski-Sobieski, Jaroslaw

2005-01-01

A parallel Particle Swarm Optimization (PSO) algorithm is presented. Particle swarm optimization is a fairly recent addition to the family of non-gradient based, probabilistic search algorithms that is based on a simplified social model and is closely tied to swarming theory. Although PSO algorithms present several attractive properties to the designer, they are plagued by high computational cost as measured by elapsed time. One approach to reduce the elapsed time is to make use of coarse-grained parallelization to evaluate the design points. Previous parallel PSO algorithms were mostly implemented in a synchronous manner, where all design points within a design iteration are evaluated before the next iteration is started. This approach leads to poor parallel speedup in cases where a heterogeneous parallel environment is used and/or where the analysis time depends on the design point being analyzed. This paper introduces an asynchronous parallel PSO algorithm that greatly improves the parallel e ciency. The asynchronous algorithm is benchmarked on a cluster assembled of Apple Macintosh G5 desktop computers, using the multi-disciplinary optimization of a typical transport aircraft wing as an example.

A parallel competitive Particle Swarm Optimization for non-linear first arrival traveltime tomography and uncertainty quantification

NASA Astrophysics Data System (ADS)

Luu, Keurfon; Noble, Mark; Gesret, Alexandrine; Belayouni, Nidhal; Roux, Pierre-François

2018-04-01

Seismic traveltime tomography is an optimization problem that requires large computational efforts. Therefore, linearized techniques are commonly used for their low computational cost. These local optimization methods are likely to get trapped in a local minimum as they critically depend on the initial model. On the other hand, global optimization methods based on MCMC are insensitive to the initial model but turn out to be computationally expensive. Particle Swarm Optimization (PSO) is a rather new global optimization approach with few tuning parameters that has shown excellent convergence rates and is straightforwardly parallelizable, allowing a good distribution of the workload. However, while it can traverse several local minima of the evaluated misfit function, classical implementation of PSO can get trapped in local minima at later iterations as particles inertia dim. We propose a Competitive PSO (CPSO) to help particles to escape from local minima with a simple implementation that improves swarm's diversity. The model space can be sampled by running the optimizer multiple times and by keeping all the models explored by the swarms in the different runs. A traveltime tomography algorithm based on CPSO is successfully applied on a real 3D data set in the context of induced seismicity.

Why Computer-Based Systems Should be Autonomic

NASA Technical Reports Server (NTRS)

Sterritt, Roy; Hinchey, Mike

2005-01-01

The objective of this paper is to discuss why computer-based systems should be autonomic, where autonomicity implies self-managing, often conceptualized in terms of being self-configuring, self-healing, self-optimizing, self-protecting and self-aware. We look at motivations for autonomicity, examine how more and more systems are exhibiting autonomic behavior, and finally look at future directions.

A New Stochastic Technique for Painlevé Equation-I Using Neural Network Optimized with Swarm Intelligence

PubMed Central

Raja, Muhammad Asif Zahoor; Khan, Junaid Ali; Ahmad, Siraj-ul-Islam; Qureshi, Ijaz Mansoor

2012-01-01

A methodology for solution of Painlevé equation-I is presented using computational intelligence technique based on neural networks and particle swarm optimization hybridized with active set algorithm. The mathematical model of the equation is developed with the help of linear combination of feed-forward artificial neural networks that define the unsupervised error of the model. This error is minimized subject to the availability of appropriate weights of the networks. The learning of the weights is carried out using particle swarm optimization algorithm used as a tool for viable global search method, hybridized with active set algorithm for rapid local convergence. The accuracy, convergence rate, and computational complexity of the scheme are analyzed based on large number of independents runs and their comprehensive statistical analysis. The comparative studies of the results obtained are made with MATHEMATICA solutions, as well as, with variational iteration method and homotopy perturbation method. PMID:22919371

Robot Wars: US Empire and geopolitics in the robotic age

PubMed Central

Shaw, Ian GR

2017-01-01

How will the robot age transform warfare? What geopolitical futures are being imagined by the US military? This article constructs a robotic futurology to examine these crucial questions. Its central concern is how robots – driven by leaps in artificial intelligence and swarming – are rewiring the spaces and logics of US empire, warfare, and geopolitics. The article begins by building a more-than-human geopolitics to de-center the role of humans in conflict and foreground a worldly understanding of robots. The article then analyzes the idea of US empire, before speculating upon how and why robots are materializing new forms of proxy war. A three-part examination of the shifting spaces of US empire then follows: (1) Swarm Wars explores the implications of miniaturized drone swarming; (2) Roboworld investigates how robots are changing US military basing strategy and producing new topological spaces of violence; and (3) The Autogenic Battle-Site reveals how autonomous robots will produce emergent, technologically event-ful sites of security and violence – revolutionizing the battlespace. The conclusion reflects on the rise of a robotic US empire and its consequences for democracy. PMID:29081605

Robot Wars: US Empire and geopolitics in the robotic age.

PubMed

Shaw, Ian Gr

2017-10-01

How will the robot age transform warfare? What geopolitical futures are being imagined by the US military? This article constructs a robotic futurology to examine these crucial questions. Its central concern is how robots - driven by leaps in artificial intelligence and swarming - are rewiring the spaces and logics of US empire, warfare, and geopolitics. The article begins by building a more-than-human geopolitics to de-center the role of humans in conflict and foreground a worldly understanding of robots. The article then analyzes the idea of US empire, before speculating upon how and why robots are materializing new forms of proxy war. A three-part examination of the shifting spaces of US empire then follows: (1) Swarm Wars explores the implications of miniaturized drone swarming; (2) Roboworld investigates how robots are changing US military basing strategy and producing new topological spaces of violence; and (3) The Autogenic Battle-Site reveals how autonomous robots will produce emergent, technologically event-ful sites of security and violence - revolutionizing the battlespace. The conclusion reflects on the rise of a robotic US empire and its consequences for democracy.

NASA Tech Briefs, September 2003

NASA Technical Reports Server (NTRS)

2003-01-01

Topics include: Oxygen-Partial-Pressure Sensor for Aircraft Oxygen Mask; Three-Dimensional Venturi Sensor for Measuring Extreme Winds; Swarms of Micron-Sized Sensors; Monitoring Volcanoes by Use of Air-Dropped Sensor Packages; Capacitive Sensors for Measuring Masses of Cryogenic Fluids; UHF Microstrip Antenna Array for Synthetic- Aperture Radar; Multimode Broad-Band Patch Antennas; 164-GHz MMIC HEMT Frequency Doubler; GPS Position and Heading Circuitry for Ships; Software for Managing Parametric Studies; Software Aids Visualization of Computed Unsteady Flow; Software for Testing Electroactive Structural Components; Advanced Software for Analysis of High-Speed Rolling-Element Bearings; Web Program for Development of GUIs for Cluster Computers; XML-Based Generator of C++ Code for Integration With GUIs; Oxide Protective Coats for Ir/Re Rocket Combustion Chambers; Simplified Waterproofing of Aerogels; Improved Thermal-Insulation Systems for Low Temperatures; Device for Automated Cutting and Transfer of Plant Shoots; Extension of Liouville Formalism to Postinstability Dynamics; Advances in Thrust-Based Emergency Control of an Airplane; Ultrasonic/Sonic Mechanisms for Drilling and Coring; Exercise Device Would Exert Selectable Constant Resistance; Improved Apparatus for Measuring Distance Between Axles; Six Classes of Diffraction-Based Optoelectronic Instruments; Modernizing Fortran 77 Legacy Codes; Active State Model for Autonomous Systems; Shields for Enhanced Protection Against High-Speed Debris; Scaling of Two-Phase Flows to Partial-Earth Gravity; Neutral-Axis Springs for Thin-Wall Integral Boom Hinges.

Swarmathon 2018

NASA Image and Video Library

2018-04-17

Students from Montgomery College in Rockville in Maryland, follow the progress of their Swarmie robots during the Swarmathon competition at the Kennedy Space Center Visitor Complex. Students were asked to develop computer code for the small robots, programming them to look for "resources" in the form of AprilTag cubes, similar to barcodes. Teams developed search algorithms for the Swarmies to operate autonomously, communicating and interacting as a collective swarm similar to ants foraging for food. In the spaceport's third annual Swarmathon, 23 teams represented 24 minority serving universities and community colleges were invited to develop software code to operate these innovative robots known as "Swarmies" to help find resources when astronauts explore distant locations, such as the Moon or Mars.

Swarmathon 2018

NASA Image and Video Library

2018-04-18

In the Swarmathon competition at the Kennedy Space Center Visitor Complex, students were asked to develop computer code for the small robots, programming them to look for "resources" in the form of AprilTag cubes, similar to barcodes. To add to the challenge, obstacles in the form of simulated rocks were placed in the completion arena. Teams developed search algorithms for the Swarmies to operate autonomously, communicating and interacting as a collective swarm similar to ants foraging for food. In the spaceport's third annual Swarmathon, 23 teams represented 24 minority serving universities and community colleges were invited to develop software code to operate these innovative robots known as "Swarmies" to help find resources when astronauts explore distant locations, such as the Moon or Mars.

Simulating Flying Insects Using Dynamics and Data-Driven Noise Modeling to Generate Diverse Collective Behaviors

PubMed Central

Ren, Jiaping; Wang, Xinjie; Manocha, Dinesh

2016-01-01

We present a biologically plausible dynamics model to simulate swarms of flying insects. Our formulation, which is based on biological conclusions and experimental observations, is designed to simulate large insect swarms of varying densities. We use a force-based model that captures different interactions between the insects and the environment and computes collision-free trajectories for each individual insect. Furthermore, we model the noise as a constructive force at the collective level and present a technique to generate noise-induced insect movements in a large swarm that are similar to those observed in real-world trajectories. We use a data-driven formulation that is based on pre-recorded insect trajectories. We also present a novel evaluation metric and a statistical validation approach that takes into account various characteristics of insect motions. In practice, the combination of Curl noise function with our dynamics model is used to generate realistic swarm simulations and emergent behaviors. We highlight its performance for simulating large flying swarms of midges, fruit fly, locusts and moths and demonstrate many collective behaviors, including aggregation, migration, phase transition, and escape responses. PMID:27187068

Multiagent robotic systems' ambient light sensor

NASA Astrophysics Data System (ADS)

Iureva, Radda A.; Maslennikov, Oleg S.; Komarov, Igor I.

2017-05-01

Swarm robotics is one of the fastest growing areas of modern technology. Being subclass of multi-agent systems it inherits the main part of scientific-methodological apparatus of construction and functioning of practically useful complexes, which consist of rather autonomous independent agents. Ambient light sensors (ALS) are widely used in robotics. But speaking about swarm robotics, the technology which has great number of specific features and is developing, we can't help mentioning that its important to use sensors on each robot not only in order to help it to get directionally oriented, but also to follow light emitted by robot-chief or to help to find the goal easier. Key words: ambient light sensor, swarm system, multiagent system, robotic system, robotic complexes, simulation modelling

ANTS: Applying A New Paradigm for Lunar and Planetary Exploration

NASA Technical Reports Server (NTRS)

Clark, P. E.; Curtis, S. A.; Rilee, M. L.

2002-01-01

ANTS (Autonomous Nano- Technology Swarm), a mission architecture consisting of a large (1000 member) swarm of picoclass (1 kg) totally autonomous spacecraft with both adaptable and evolvable heuristic systems, is being developed as a NASA advanced mission concept, and is here examined as a paradigm for lunar surface exploration. As the capacity and complexity of hardware and software, demands for bandwidth, and the sophistication of goals for lunar and planetary exploration have increased, greater cost constraints have led to fewer resources and thus, the need to operate spacecraft with less frequent human contact. At present, autonomous operation of spacecraft systems allows great capability of spacecraft to 'safe' themselves and survive when conditions threaten spacecraft safety. To further develop spacecraft capability, NASA is at the forefront of development of new mission architectures which involve the use of Intelligent Software Agents (ISAs), performing experiments in space and on the ground to advance deliberative and collaborative autonomous control techniques. Selected missions in current planning stages require small groups of spacecraft weighing tens, instead of hundreds, of kilograms to cooperate at a tactical level to select and schedule measurements to be made by appropriate instruments onboard. Such missions will be characterizing rapidly unfolding real-time events on a routine basis. The next level of development, which we are considering here, is in the use of autonomous systems at the strategic level, to explore the remote terranes, potentially involving large surveys or detailed reconnaissance.

AN OFFSET FOR AFSOF: COMBINING ADDITIVE MANUFACTURING AND AUTONOMOUS SYSTEMS WITH SWARM EMPLOYMENT

DTIC Science & Technology

2016-10-01

teams composed of autonomous robot players compete in games of soccer .58 Strongly coordinated centralized systems are similar to the distributed...goal in a dynamically changing environment. This is a very active area of research and exemplified by the robot soccer league, a competition where...University, 2013, 23. 63 Massie, Andrew. “Autonomy and the Future Force” Strategic Studies Quarterly, Summer 2016, 146. 64 Zacharias, Greg. "Autonomus

3D model generation using an airborne swarm

DOE Office of Scientific and Technical Information (OSTI.GOV)

Clark, R. A.; Punzo, G.; Macdonald, M.

2015-03-31

Using an artificial kinematic field to provide co-ordination between multiple inspection UAVs, the authors herein demonstrate full 3D modelling capability based on a photogrammetric system. The operation of the system is demonstrated by generating a full 3D surface model of an intermediate level nuclear waste storage drum. Such drums require periodic inspection to ensure that drum distortion or corrosion is carefully monitored. Performing this inspection with multiple airborne platforms enables rapid inspection of structures that are inaccessible to on-surface remote vehicles and are in human-hazardous environments. A three-dimensional surface-meshed model of the target can then be constructed in post-processing throughmore » photogrammetry analysis of the visual inspection data. The inspection environment uses a tracking system to precisely monitor the position of each aerial vehicle within the enclosure. The vehicles used are commercially available Parrot AR. Drone quadcopters, controlled through a computer interface connected over an IEEE 802.11n (WiFi) network, implementing a distributed controller for each vehicle. This enables the autonomous and distributed elements of the control scheme to be retained, while alleviating the vehicles of the control algorithm’s computational load. The control scheme relies on a kinematic field defined with the target at its centre. This field defines the trajectory for all the drones in the volume relative to the central target, enabling the drones to circle the target at a set radius while avoiding drone collisions. This function enables complete coverage along the height of the object, which is assured by transitioning to another inspection band only after completing circumferential coverage. Using a swarm of vehicles, the time until complete coverage can be significantly reduced.« less

3D model generation using an airborne swarm

NASA Astrophysics Data System (ADS)

Clark, R. A.; Punzo, G.; Dobie, G.; MacLeod, C. N.; Summan, R.; Pierce, G.; Macdonald, M.; Bolton, G.

2015-03-01

Using an artificial kinematic field to provide co-ordination between multiple inspection UAVs, the authors herein demonstrate full 3D modelling capability based on a photogrammetric system. The operation of the system is demonstrated by generating a full 3D surface model of an intermediate level nuclear waste storage drum. Such drums require periodic inspection to ensure that drum distortion or corrosion is carefully monitored. Performing this inspection with multiple airborne platforms enables rapid inspection of structures that are inaccessible to on-surface remote vehicles and are in human-hazardous environments. A three-dimensional surface-meshed model of the target can then be constructed in post-processing through photogrammetry analysis of the visual inspection data. The inspection environment uses a tracking system to precisely monitor the position of each aerial vehicle within the enclosure. The vehicles used are commercially available Parrot AR. Drone quadcopters, controlled through a computer interface connected over an IEEE 802.11n (WiFi) network, implementing a distributed controller for each vehicle. This enables the autonomous and distributed elements of the control scheme to be retained, while alleviating the vehicles of the control algorithm's computational load. The control scheme relies on a kinematic field defined with the target at its centre. This field defines the trajectory for all the drones in the volume relative to the central target, enabling the drones to circle the target at a set radius while avoiding drone collisions. This function enables complete coverage along the height of the object, which is assured by transitioning to another inspection band only after completing circumferential coverage. Using a swarm of vehicles, the time until complete coverage can be significantly reduced.

Energy-Aware Multipath Routing Scheme Based on Particle Swarm Optimization in Mobile Ad Hoc Networks

PubMed Central

Robinson, Y. Harold; Rajaram, M.

2015-01-01

Mobile ad hoc network (MANET) is a collection of autonomous mobile nodes forming an ad hoc network without fixed infrastructure. Dynamic topology property of MANET may degrade the performance of the network. However, multipath selection is a great challenging task to improve the network lifetime. We proposed an energy-aware multipath routing scheme based on particle swarm optimization (EMPSO) that uses continuous time recurrent neural network (CTRNN) to solve optimization problems. CTRNN finds the optimal loop-free paths to solve link disjoint paths in a MANET. The CTRNN is used as an optimum path selection technique that produces a set of optimal paths between source and destination. In CTRNN, particle swarm optimization (PSO) method is primly used for training the RNN. The proposed scheme uses the reliability measures such as transmission cost, energy factor, and the optimal traffic ratio between source and destination to increase routing performance. In this scheme, optimal loop-free paths can be found using PSO to seek better link quality nodes in route discovery phase. PSO optimizes a problem by iteratively trying to get a better solution with regard to a measure of quality. The proposed scheme discovers multiple loop-free paths by using PSO technique. PMID:26819966

Asteroid Exploration with Autonomic Systems

NASA Technical Reports Server (NTRS)

Truszkowski, Walt; Rash, James; Rouff, Christopher; Hinchey, Mike

2004-01-01

NASA is studying advanced technologies for a future robotic exploration mission to the asteroid belt. The prospective ANTS (Autonomous Nano Technology Swarm) mission comprises autonomous agents including worker agents (small spacecra3) designed to cooperate in asteroid exploration under the overall authoriq of at least one ruler agent (a larger spacecraft) whose goal is to cause science data to be returned to Earth. The ANTS team (ruler plus workers and messenger agents), but not necessarily any individual on the team, will exhibit behaviors that qualify it as an autonomic system, where an autonomic system is defined as a system that self-reconfigures, self-optimizes, self-heals, and self-protects. Autonomic system concepts lead naturally to realistic, scalable architectures rich in capabilities and behaviors. In-depth consideration of a major mission like ANTS in terms of autonomic systems brings new insights into alternative definitions of autonomic behavior. This paper gives an overview of the ANTS mission and discusses the autonomic properties of the mission.

Genetic particle swarm parallel algorithm analysis of optimization arrangement on mistuned blades

NASA Astrophysics Data System (ADS)

Zhao, Tianyu; Yuan, Huiqun; Yang, Wenjun; Sun, Huagang

2017-12-01

This article introduces a method of mistuned parameter identification which consists of static frequency testing of blades, dichotomy and finite element analysis. A lumped parameter model of an engine bladed-disc system is then set up. A bladed arrangement optimization method, namely the genetic particle swarm optimization algorithm, is presented. It consists of a discrete particle swarm optimization and a genetic algorithm. From this, the local and global search ability is introduced. CUDA-based co-evolution particle swarm optimization, using a graphics processing unit, is presented and its performance is analysed. The results show that using optimization results can reduce the amplitude and localization of the forced vibration response of a bladed-disc system, while optimization based on the CUDA framework can improve the computing speed. This method could provide support for engineering applications in terms of effectiveness and efficiency.

Artificial bee colony in neuro - Symbolic integration

NASA Astrophysics Data System (ADS)

Kasihmuddin, Mohd Shareduwan Mohd; Sathasivam, Saratha; Mansor, Mohd. Asyraf

2017-08-01

Swarm intelligence is a research area that models the population of the swarm based on natural computation. Artificial bee colony (ABC) algorithm is a swarm based metaheuristic algorithm introduced by Karaboga to optimize numerical problem. Pattern-SAT is a pattern reconstruction paradigm that utilized 2SAT logical rule in representing the behavior of the desired pattern. The information of the desired pattern in terms of 2SAT logic is embedded to Hopfield neural network (HNN-P2SAT) and the desired pattern is reconstructed during the retrieval phase. Since the performance of HNN-P2SAT in Pattern-SAT deteriorates when the number of 2SAT clause increased, newly improved ABC is used to reduce the computation burden during the learning phase of HNN-P2SAT (HNN-P2SATABC). The aim of this study is to investigate the performance of Pattern-SAT produced by ABC incorporated with HNN-P2SAT and compare it with conventional standalone HNN. The comparison is examined by using Microsoft Visual Basic C++ 2013 software. The detailed comparison in doing Pattern-SAT is discussed based on global Pattern-SAT, ratio of activated clauses and computation time. The result obtained from computer simulation indicates the beneficial features of HNN-P2SATABC in doing Pattern-SAT. This finding is expected to result in a significant implication on the choice of searching method used to do Pattern-SAT.

Collective motion patterns of swarms with delay coupling: Theory and experiment.

PubMed

Szwaykowska, Klementyna; Schwartz, Ira B; Mier-Y-Teran Romero, Luis; Heckman, Christoffer R; Mox, Dan; Hsieh, M Ani

2016-03-01

The formation of coherent patterns in swarms of interacting self-propelled autonomous agents is a subject of great interest in a wide range of application areas, ranging from engineering and physics to biology. In this paper, we model and experimentally realize a mixed-reality large-scale swarm of delay-coupled agents. The coupling term is modeled as a delayed communication relay of position. Our analyses, assuming agents communicating over an Erdös-Renyi network, demonstrate the existence of stable coherent patterns that can be achieved only with delay coupling and that are robust to decreasing network connectivity and heterogeneity in agent dynamics. We also show how the bifurcation structure for emergence of different patterns changes with heterogeneity in agent acceleration capabilities and limited connectivity in the network as a function of coupling strength and delay. Our results are verified through simulation as well as preliminary experimental results of delay-induced pattern formation in a mixed-reality swarm.

Collective motion patterns of swarms with delay coupling: Theory and experiment

NASA Astrophysics Data System (ADS)

Szwaykowska, Klementyna; Schwartz, Ira B.; Mier-y-Teran Romero, Luis; Heckman, Christoffer R.; Mox, Dan; Hsieh, M. Ani

2016-03-01

The formation of coherent patterns in swarms of interacting self-propelled autonomous agents is a subject of great interest in a wide range of application areas, ranging from engineering and physics to biology. In this paper, we model and experimentally realize a mixed-reality large-scale swarm of delay-coupled agents. The coupling term is modeled as a delayed communication relay of position. Our analyses, assuming agents communicating over an Erdös-Renyi network, demonstrate the existence of stable coherent patterns that can be achieved only with delay coupling and that are robust to decreasing network connectivity and heterogeneity in agent dynamics. We also show how the bifurcation structure for emergence of different patterns changes with heterogeneity in agent acceleration capabilities and limited connectivity in the network as a function of coupling strength and delay. Our results are verified through simulation as well as preliminary experimental results of delay-induced pattern formation in a mixed-reality swarm.

Complete characterization of the stability of cluster synchronization in complex dynamical networks.

PubMed

Sorrentino, Francesco; Pecora, Louis M; Hagerstrom, Aaron M; Murphy, Thomas E; Roy, Rajarshi

2016-04-01

Synchronization is an important and prevalent phenomenon in natural and engineered systems. In many dynamical networks, the coupling is balanced or adjusted to admit global synchronization, a condition called Laplacian coupling. Many networks exhibit incomplete synchronization, where two or more clusters of synchronization persist, and computational group theory has recently proved to be valuable in discovering these cluster states based on the topology of the network. In the important case of Laplacian coupling, additional synchronization patterns can exist that would not be predicted from the group theory analysis alone. Understanding how and when clusters form, merge, and persist is essential for understanding collective dynamics, synchronization, and failure mechanisms of complex networks such as electric power grids, distributed control networks, and autonomous swarming vehicles. We describe a method to find and analyze all of the possible cluster synchronization patterns in a Laplacian-coupled network, by applying methods of computational group theory to dynamically equivalent networks. We present a general technique to evaluate the stability of each of the dynamically valid cluster synchronization patterns. Our results are validated in an optoelectronic experiment on a five-node network that confirms the synchronization patterns predicted by the theory.

Fog computing job scheduling optimization based on bees swarm

NASA Astrophysics Data System (ADS)

Bitam, Salim; Zeadally, Sherali; Mellouk, Abdelhamid

2018-04-01

Fog computing is a new computing architecture, composed of a set of near-user edge devices called fog nodes, which collaborate together in order to perform computational services such as running applications, storing an important amount of data, and transmitting messages. Fog computing extends cloud computing by deploying digital resources at the premise of mobile users. In this new paradigm, management and operating functions, such as job scheduling aim at providing high-performance, cost-effective services requested by mobile users and executed by fog nodes. We propose a new bio-inspired optimization approach called Bees Life Algorithm (BLA) aimed at addressing the job scheduling problem in the fog computing environment. Our proposed approach is based on the optimized distribution of a set of tasks among all the fog computing nodes. The objective is to find an optimal tradeoff between CPU execution time and allocated memory required by fog computing services established by mobile users. Our empirical performance evaluation results demonstrate that the proposal outperforms the traditional particle swarm optimization and genetic algorithm in terms of CPU execution time and allocated memory.

From Present Surveying to Future Prospecting of the Asteroid Belt

NASA Astrophysics Data System (ADS)

Clark, P. E.; Curtis, S. A.; Rilee, M.; Cheung, C.

2004-03-01

Requirements are analyzed for application of future mission architecture, the Autonomous Nano-Technology Swarm (ANTS), to proposed in situ prospecting, of the asteroid belt, the Prospecting Asteroid Mission (PAM) as part of a NASA 2003 Revolutionary Aerospace Concept (RASC) study.

Optimized Landing of Autonomous Unmanned Aerial Vehicle Swarms

DTIC Science & Technology

2012-06-01

understanding about the world. Examples of these emergent behaviors include construction of complex structures (e.g., hives, termite mounds), trends in economic...Sep. 2007. [16] M. Resnick, Turtles, Termites , and Traffic Jams: Explorations in Massively Parallel Microworlds. MIT Press, 1997. [Online]. Available

The "Biologically-Inspired Computing" Column

NASA Technical Reports Server (NTRS)

Hinchey, Mike

2007-01-01

Self-managing systems, whether viewed from the perspective of Autonomic Computing, or from that of another initiative, offers a holistic vision for the development and evolution of biologically-inspired computer-based systems. It aims to bring new levels of automation and dependability to systems, while simultaneously hiding their complexity and reducing costs. A case can certainly be made that all computer-based systems should exhibit autonomic properties [6], and we envisage greater interest in, and uptake of, autonomic principles in future system development.

Honey Bees Inspired Optimization Method: The Bees Algorithm.

PubMed

Yuce, Baris; Packianather, Michael S; Mastrocinque, Ernesto; Pham, Duc Truong; Lambiase, Alfredo

2013-11-06

Optimization algorithms are search methods where the goal is to find an optimal solution to a problem, in order to satisfy one or more objective functions, possibly subject to a set of constraints. Studies of social animals and social insects have resulted in a number of computational models of swarm intelligence. Within these swarms their collective behavior is usually very complex. The collective behavior of a swarm of social organisms emerges from the behaviors of the individuals of that swarm. Researchers have developed computational optimization methods based on biology such as Genetic Algorithms, Particle Swarm Optimization, and Ant Colony. The aim of this paper is to describe an optimization algorithm called the Bees Algorithm, inspired from the natural foraging behavior of honey bees, to find the optimal solution. The algorithm performs both an exploitative neighborhood search combined with random explorative search. In this paper, after an explanation of the natural foraging behavior of honey bees, the basic Bees Algorithm and its improved versions are described and are implemented in order to optimize several benchmark functions, and the results are compared with those obtained with different optimization algorithms. The results show that the Bees Algorithm offering some advantage over other optimization methods according to the nature of the problem.

Using modified fruit fly optimisation algorithm to perform the function test and case studies

NASA Astrophysics Data System (ADS)

Pan, Wen-Tsao

2013-06-01

Evolutionary computation is a computing mode established by practically simulating natural evolutionary processes based on the concept of Darwinian Theory, and it is a common research method. The main contribution of this paper was to reinforce the function of searching for the optimised solution using the fruit fly optimization algorithm (FOA), in order to avoid the acquisition of local extremum solutions. The evolutionary computation has grown to include the concepts of animal foraging behaviour and group behaviour. This study discussed three common evolutionary computation methods and compared them with the modified fruit fly optimization algorithm (MFOA). It further investigated the ability of the three mathematical functions in computing extreme values, as well as the algorithm execution speed and the forecast ability of the forecasting model built using the optimised general regression neural network (GRNN) parameters. The findings indicated that there was no obvious difference between particle swarm optimization and the MFOA in regards to the ability to compute extreme values; however, they were both better than the artificial fish swarm algorithm and FOA. In addition, the MFOA performed better than the particle swarm optimization in regards to the algorithm execution speed, and the forecast ability of the forecasting model built using the MFOA's GRNN parameters was better than that of the other three forecasting models.

Swarm intelligence metaheuristics for enhanced data analysis and optimization.

PubMed

Hanrahan, Grady

2011-09-21

The swarm intelligence (SI) computing paradigm has proven itself as a comprehensive means of solving complicated analytical chemistry problems by emulating biologically-inspired processes. As global optimum search metaheuristics, associated algorithms have been widely used in training neural networks, function optimization, prediction and classification, and in a variety of process-based analytical applications. The goal of this review is to provide readers with critical insight into the utility of swarm intelligence tools as methods for solving complex chemical problems. Consideration will be given to algorithm development, ease of implementation and model performance, detailing subsequent influences on a number of application areas in the analytical, bioanalytical and detection sciences.

Solving Fractional Programming Problems based on Swarm Intelligence

NASA Astrophysics Data System (ADS)

Raouf, Osama Abdel; Hezam, Ibrahim M.

2014-04-01

This paper presents a new approach to solve Fractional Programming Problems (FPPs) based on two different Swarm Intelligence (SI) algorithms. The two algorithms are: Particle Swarm Optimization, and Firefly Algorithm. The two algorithms are tested using several FPP benchmark examples and two selected industrial applications. The test aims to prove the capability of the SI algorithms to solve any type of FPPs. The solution results employing the SI algorithms are compared with a number of exact and metaheuristic solution methods used for handling FPPs. Swarm Intelligence can be denoted as an effective technique for solving linear or nonlinear, non-differentiable fractional objective functions. Problems with an optimal solution at a finite point and an unbounded constraint set, can be solved using the proposed approach. Numerical examples are given to show the feasibility, effectiveness, and robustness of the proposed algorithm. The results obtained using the two SI algorithms revealed the superiority of the proposed technique among others in computational time. A better accuracy was remarkably observed in the solution results of the industrial application problems.

An improved design method based on polyphase components for digital FIR filters

NASA Astrophysics Data System (ADS)

Kumar, A.; Kuldeep, B.; Singh, G. K.; Lee, Heung No

2017-11-01

This paper presents an efficient design of digital finite impulse response (FIR) filter, based on polyphase components and swarm optimisation techniques (SOTs). For this purpose, the design problem is formulated as mean square error between the actual response and ideal response in frequency domain using polyphase components of a prototype filter. To achieve more precise frequency response at some specified frequency, fractional derivative constraints (FDCs) have been applied, and optimal FDCs are computed using SOTs such as cuckoo search and modified cuckoo search algorithms. A comparative study of well-proved swarm optimisation, called particle swarm optimisation and artificial bee colony algorithm is made. The excellence of proposed method is evaluated using several important attributes of a filter. Comparative study evidences the excellence of proposed method for effective design of FIR filter.

Automatic Clustering Using Multi-objective Particle Swarm and Simulated Annealing

PubMed Central

Abubaker, Ahmad; Baharum, Adam; Alrefaei, Mahmoud

2015-01-01

This paper puts forward a new automatic clustering algorithm based on Multi-Objective Particle Swarm Optimization and Simulated Annealing, “MOPSOSA”. The proposed algorithm is capable of automatic clustering which is appropriate for partitioning datasets to a suitable number of clusters. MOPSOSA combines the features of the multi-objective based particle swarm optimization (PSO) and the Multi-Objective Simulated Annealing (MOSA). Three cluster validity indices were optimized simultaneously to establish the suitable number of clusters and the appropriate clustering for a dataset. The first cluster validity index is centred on Euclidean distance, the second on the point symmetry distance, and the last cluster validity index is based on short distance. A number of algorithms have been compared with the MOPSOSA algorithm in resolving clustering problems by determining the actual number of clusters and optimal clustering. Computational experiments were carried out to study fourteen artificial and five real life datasets. PMID:26132309

Material quality assessment of silk nanofibers based on swarm intelligence

NASA Astrophysics Data System (ADS)

Brandoli Machado, Bruno; Nunes Gonçalves, Wesley; Martinez Bruno, Odemir

2013-02-01

In this paper, we propose a novel approach for texture analysis based on artificial crawler model. Our method assumes that each agent can interact with the environment and each other. The evolution process converges to an equilibrium state according to the set of rules. For each textured image, the feature vector is composed by signatures of the live agents curve at each time. Experimental results revealed that combining the minimum and maximum signatures into one increase the classification rate. In addition, we pioneer the use of autonomous agents for characterizing silk fibroin scaffolds. The results strongly suggest that our approach can be successfully employed for texture analysis.

A tale of two forces: simultaneous chemical and acoustic propulsion of bimetallic micromotors.

PubMed

Wang, Wei; Duan, Wentao; Zhang, Zexin; Sun, Mei; Sen, Ayusman; Mallouk, Thomas E

2015-01-21

Bimetallic gold-ruthenium microrods are propelled in opposite directions in water by ultrasound and by catalytic decomposition of hydrogen peroxide. This property was used to effect reversible swarming, to stall and reverse autonomous axial propulsion, and to study the chemically powered movement of acoustically levitated microrods.

EEG/ERP adaptive noise canceller design with controlled search space (CSS) approach in cuckoo and other optimization algorithms.

PubMed

Ahirwal, M K; Kumar, Anil; Singh, G K

2013-01-01

This paper explores the migration of adaptive filtering with swarm intelligence/evolutionary techniques employed in the field of electroencephalogram/event-related potential noise cancellation or extraction. A new approach is proposed in the form of controlled search space to stabilize the randomness of swarm intelligence techniques especially for the EEG signal. Swarm-based algorithms such as Particles Swarm Optimization, Artificial Bee Colony, and Cuckoo Optimization Algorithm with their variants are implemented to design optimized adaptive noise canceler. The proposed controlled search space technique is tested on each of the swarm intelligence techniques and is found to be more accurate and powerful. Adaptive noise canceler with traditional algorithms such as least-mean-square, normalized least-mean-square, and recursive least-mean-square algorithms are also implemented to compare the results. ERP signals such as simulated visual evoked potential, real visual evoked potential, and real sensorimotor evoked potential are used, due to their physiological importance in various EEG studies. Average computational time and shape measures of evolutionary techniques are observed 8.21E-01 sec and 1.73E-01, respectively. Though, traditional algorithms take negligible time consumption, but are unable to offer good shape preservation of ERP, noticed as average computational time and shape measure difference, 1.41E-02 sec and 2.60E+00, respectively.

Research on UAV Intelligent Obstacle Avoidance Technology During Inspection of Transmission Line

NASA Astrophysics Data System (ADS)

Wei, Chuanhu; Zhang, Fei; Yin, Chaoyuan; Liu, Yue; Liu, Liang; Li, Zongyu; Wang, Wanguo

Autonomous obstacle avoidance of unmanned aerial vehicle (hereinafter referred to as UAV) in electric power line inspection process has important significance for operation safety and economy for UAV intelligent inspection system of transmission line as main content of UAV intelligent inspection system on transmission line. In the paper, principles of UAV inspection obstacle avoidance technology of transmission line are introduced. UAV inspection obstacle avoidance technology based on particle swarm global optimization algorithm is proposed after common obstacle avoidance technologies are studied. Stimulation comparison is implemented with traditional UAV inspection obstacle avoidance technology which adopts artificial potential field method. Results show that UAV inspection strategy of particle swarm optimization algorithm, adopted in the paper, is prominently better than UAV inspection strategy of artificial potential field method in the aspects of obstacle avoidance effect and the ability of returning to preset inspection track after passing through the obstacle. An effective method is provided for UAV inspection obstacle avoidance of transmission line.

Propulsion Trade Studies for Spacecraft Swarm Mission Design

NASA Technical Reports Server (NTRS)

Dono, Andres; Plice, Laura; Mueting, Joel; Conn, Tracie; Ho, Michael

2018-01-01

Spacecraft swarms constitute a challenge from an orbital mechanics standpoint. Traditional mission design involves the application of methodical processes where predefined maneuvers for an individual spacecraft are planned in advance. This approach does not scale to spacecraft swarms consisting of many satellites orbiting in close proximity; non-deterministic maneuvers cannot be preplanned due to the large number of units and the uncertainties associated with their differential deployment and orbital motion. For autonomous small sat swarms in LEO, we investigate two approaches for controlling the relative motion of a swarm. The first method involves modified miniature phasing maneuvers, where maneuvers are prescribed that cancel the differential delta V of each CubeSat's deployment vector. The second method relies on artificial potential functions (APFs) to contain the spacecraft within a volumetric boundary and avoid collisions. Performance results and required delta V budgets are summarized, indicating that each method has advantages and drawbacks for particular applications. The mini phasing maneuvers are more predictable and sustainable. The APF approach provides a more responsive and distributed performance, but at considerable propellant cost. After considering current state of the art CubeSat propulsion systems, we conclude that the first approach is feasible, but the modified APF method of requires too much control authority to be enabled by current propulsion systems.

Towards distributed ATR using subjective logic combination rules with a swarm of UAVs

NASA Astrophysics Data System (ADS)

O'Hara, Stephen; Simon, Michael; Zhu, Qiuming

2007-04-01

In this paper, we present our initial findings demonstrating a cost-effective approach to Aided Target Recognition (ATR) employing a swarm of inexpensive Unmanned Aerial Vehicles (UAVs). We call our approach Distributed ATR (DATR). Our paper describes the utility of DATR for autonomous UAV operations, provides an overview of our methods, and the results of our initial simulation-based implementation and feasibility study. Our technology is aimed towards small and micro UAVs where platform restrictions allow only a modest quality camera and limited on-board computational capabilities. It is understood that an inexpensive sensor coupled with limited processing capability would be challenged in deriving a high probability of detection (P d) while maintaining a low probability of false alarms (P fa). Our hypothesis is that an evidential reasoning approach to fusing the observations of multiple UAVs observing approximately the same scene can raise the P d and lower the P fa sufficiently in order to provide a cost-effective ATR capability. This capability can lead to practical implementations of autonomous, coordinated, multi-UAV operations. In our system, the live video feed from a UAV is processed by a lightweight real-time ATR algorithm. This algorithm provides a set of possible classifications for each detected object over a possibility space defined by a set of exemplars. The classifications for each frame within a short observation interval (a few seconds) are used to generate a belief statement. Our system considers how many frames in the observation interval support each potential classification. A definable function transforms the observational data into a belief value. The belief value, or opinion, represents the UAV's belief that an object of the particular class exists in the area covered during the observation interval. The opinion is submitted as evidence in an evidential reasoning system. Opinions from observations over the same spatial area will have similar index values in the evidence cache. The evidential reasoning system combines observations of similar spatial indexes, discounting older observations based upon a parameterized information aging function. We employ Subjective Logic operations in the discounting and combination of opinions. The result is the consensus opinion from all observations that an object of a given class exists in a given region.

Vehicle routing problem with time windows using natural inspired algorithms

NASA Astrophysics Data System (ADS)

Pratiwi, A. B.; Pratama, A.; Sa’diyah, I.; Suprajitno, H.

2018-03-01

Process of distribution of goods needs a strategy to make the total cost spent for operational activities minimized. But there are several constrains have to be satisfied which are the capacity of the vehicles and the service time of the customers. This Vehicle Routing Problem with Time Windows (VRPTW) gives complex constrains problem. This paper proposes natural inspired algorithms for dealing with constrains of VRPTW which involves Bat Algorithm and Cat Swarm Optimization. Bat Algorithm is being hybrid with Simulated Annealing, the worst solution of Bat Algorithm is replaced by the solution from Simulated Annealing. Algorithm which is based on behavior of cats, Cat Swarm Optimization, is improved using Crow Search Algorithm to make simplier and faster convergence. From the computational result, these algorithms give good performances in finding the minimized total distance. Higher number of population causes better computational performance. The improved Cat Swarm Optimization with Crow Search gives better performance than the hybridization of Bat Algorithm and Simulated Annealing in dealing with big data.

A Data Link Layer in Support of Swarming of Autonomous Underwater Vehicles

ERIC Educational Resources Information Center

Jabba Molinares, Daladier

2009-01-01

Communication underwater is challenging because of the inherent characteristics of the media. First, common radio frequency (RF) signals utilized in wireless communications cannot be used under water. RF signals are attenuated in such as way that RF communication underwater is restricted to very few meters. As a result, acoustic-based…

Swarmathon 2017

NASA Image and Video Library

2017-04-19

A cube identified with an AprilTag, similar to a barcode, is delivered to a "home" square in the middle of a competition arena during the Swarmathon competition. At the Kennedy Space Center Visitor Complex, student teams developed search algorithms for the Swarmies to operate autonomously, communicating and interacting as a collective swarm similar to ants foraging for food.

An adaptive neural swarm approach for intrusion defense in ad hoc networks

NASA Astrophysics Data System (ADS)

Cannady, James

2011-06-01

Wireless sensor networks (WSN) and mobile ad hoc networks (MANET) are being increasingly deployed in critical applications due to the flexibility and extensibility of the technology. While these networks possess numerous advantages over traditional wireless systems in dynamic environments they are still vulnerable to many of the same types of host-based and distributed attacks common to those systems. Unfortunately, the limited power and bandwidth available in WSNs and MANETs, combined with the dynamic connectivity that is a defining characteristic of the technology, makes it extremely difficult to utilize traditional intrusion detection techniques. This paper describes an approach to accurately and efficiently detect potentially damaging activity in WSNs and MANETs. It enables the network as a whole to recognize attacks, anomalies, and potential vulnerabilities in a distributive manner that reflects the autonomic processes of biological systems. Each component of the network recognizes activity in its local environment and then contributes to the overall situational awareness of the entire system. The approach utilizes agent-based swarm intelligence to adaptively identify potential data sources on each node and on adjacent nodes throughout the network. The swarm agents then self-organize into modular neural networks that utilize a reinforcement learning algorithm to identify relevant behavior patterns in the data without supervision. Once the modular neural networks have established interconnectivity both locally and with neighboring nodes the analysis of events within the network can be conducted collectively in real-time. The approach has been shown to be extremely effective in identifying distributed network attacks.

Feasibility Study of a Vision-Based Landing System for Unmanned Fixed-Wing Aircraft

DTIC Science & Technology

2017-06-01

International Journal of Computer Science and Network Security 7 no. 3: 112–117. Accessed April 7, 2017. http://www.sciencedirect.com/science/ article /pii...the feasibility of applying computer vision techniques and visual feedback in the control loop for an autonomous system. This thesis examines the...integration into an autonomous aircraft control system. 14. SUBJECT TERMS autonomous systems, auto-land, computer vision, image processing

Subpixel displacement measurement method based on the combination of particle swarm optimization and gradient algorithm

NASA Astrophysics Data System (ADS)

Guang, Chen; Qibo, Feng; Keqin, Ding; Zhan, Gao

2017-10-01

A subpixel displacement measurement method based on the combination of particle swarm optimization (PSO) and gradient algorithm (GA) was proposed for accuracy and speed optimization in GA, which is a subpixel displacement measurement method better applied in engineering practice. An initial integer-pixel value was obtained according to the global searching ability of PSO, and then gradient operators were adopted for a subpixel displacement search. A comparison was made between this method and GA by simulated speckle images and rigid-body displacement in metal specimens. The results showed that the computational accuracy of the combination of PSO and GA method reached 0.1 pixel in the simulated speckle images, or even 0.01 pixels in the metal specimen. Also, computational efficiency and the antinoise performance of the improved method were markedly enhanced.

Autonomous sensor manager agents (ASMA)

NASA Astrophysics Data System (ADS)

Osadciw, Lisa A.

2004-04-01

Autonomous sensor manager agents are presented as an algorithm to perform sensor management within a multisensor fusion network. The design of the hybrid ant system/particle swarm agents is described in detail with some insight into their performance. Although the algorithm is designed for the general sensor management problem, a simulation example involving 2 radar systems is presented. Algorithmic parameters are determined by the size of the region covered by the sensor network, the number of sensors, and the number of parameters to be selected. With straight forward modifications, this algorithm can be adapted for most sensor management problems.

Improving high-altitude emp modeling capabilities by using a non-equilibrium electron swarm model to monitor conduction electron evolution

NASA Astrophysics Data System (ADS)

Pusateri, Elise Noel

An Electromagnetic Pulse (EMP) can severely disrupt the use of electronic devices in its path causing a significant amount of infrastructural damage. EMP can also cause breakdown of the surrounding atmosphere during lightning discharges. This makes modeling EMP phenomenon an important research effort in many military and atmospheric physics applications. EMP events include high-energy Compton electrons or photoelectrons that ionize air and produce low energy conduction electrons. A sufficient number of conduction electrons will damp or alter the EMP through conduction current. Therefore, it is important to understand how conduction electrons interact with air in order to accurately predict the EMP evolution and propagation in the air. It is common for EMP simulation codes to use an equilibrium ohmic model for computing the conduction current. Equilibrium ohmic models assume the conduction electrons are always in equilibrium with the local instantaneous electric field, i.e. for a specific EMP electric field, the conduction electrons instantaneously reach steady state without a transient process. An equilibrium model will work well if the electrons have time to reach their equilibrium distribution with respect to the rise time or duration of the EMP. If the time to reach equilibrium is comparable or longer than the rise time or duration of the EMP then the equilibrium model would not accurately predict the conduction current necessary for the EMP simulation. This is because transport coefficients used in the conduction current calculation will be found based on equilibrium reactions rates which may differ significantly from their non-equilibrium values. We see this deficiency in Los Alamos National Laboratory's EMP code, CHAP-LA (Compton High Altitude Pulse-Los Alamos), when modeling certain EMP scenarios at high altitudes, such as upward EMP, where the ionization rate by secondary electrons is over predicted by the equilibrium model, causing the EMP to short abruptly. The objective of the PhD research is to mitigate this effect by integrating a conduction electron model into CHAP-LA which can calculate the conduction current based on a non-equilibrium electron distribution. We propose to use an electron swarm model to monitor the time evolution of conduction electrons in the EMP environment which is characterized by electric field and pressure. Swarm theory uses various collision frequencies and reaction rates to study how the electron distribution and the resultant transport coefficients change with time, ultimately reaching an equilibrium distribution. Validation of the swarm model we develop is a necessary step for completion of the thesis work. After validation, the swarm model is integrated in the air chemistry model CHAP-LA employs for conduction electron simulations. We test high altitude EMP simulations with the swarm model option in the air chemistry model to show improvements in the computational capability of CHAP-LA. A swarm model has been developed that is based on a previous swarm model developed by Higgins, Longmire and O'Dell 1973, hereinafter HLO. The code used for the swarm model calculation solves a system of coupled differential equations for electric field, electron temperature, electron number density, and drift velocity. Important swarm parameters, including the momentum transfer collision frequency, energy transfer collision frequency, and ionization rate, are recalculated and compared to the previously reported empirical results given by HLO. These swarm parameters are found using BOLSIG+, a two term Boltzmann solver developed by Hagelaar and Pitchford 2005. BOLSIG+ utilizes updated electron scattering cross sections that are defined over an expanded energy range found in the atomic and molecular cross section database published by Phelps in the Phelps Database 2014 on the LXcat website created by Pancheshnyi et al. 2012. The swarm model is also updated from the original HLO model by including additional physical parameters such as the O2 electron attachment rate, recombination rate, and mutual neutralization rate. This necessitates tracking the positive and negative ion densities in the swarm model. Adding these parameters, especially electron attachment, is important at lower EMP altitudes where atmospheric density is high. We compare swarm model equilibrium temperatures and times using the HLO and BOLSIG+ coefficients for a uniform electric field of 1 StatV/cm for a range of atmospheric heights. This is done in order to test sensitivity to the swarm parameters used in the swarm model. It is shown that the equilibrium temperature and time are sensitive to the modifications in the collision frequency and ionization rate based on the updated electron interaction cross sections. We validate the swarm model by comparing ionization coefficients and equilibrium drift velocities to experimental results over a wide range of reduced electric field values. The final part of the PhD thesis work includes integrating the swarm model into CHAP-LA. We discuss the physics included in the CHAP-LA EMP model and demonstrate EMP damping behavior caused by the ohmic model at high altitudes. We report on numerical techniques for incorporation of the swarm model into CHAP-LA's Maxwell solver. This includes a discussion of integration techniques for Maxwell's equations in CHAP-LA using the swarm model calculated conduction current. We show improvements on EMP parameter calculations when modeling a high altitude, upward EMP scenario. This provides a novel computational capability that will have an important impact on the atmospheric and EMP research community.

An Investigation of the Relationship between College Chinese EFL Students' Autonomous Learning Capacity and Motivation in Using Computer-Assisted Language Learning

ERIC Educational Resources Information Center

Pu, Minran

2009-01-01

The purpose of the study was to investigate the relationship between college EFL students' autonomous learning capacity and motivation in using web-based Computer-Assisted Language Learning (CALL) in China. This study included three questionnaires: the student background questionnaire, the questionnaire on student autonomous learning capacity, and…

Improved Quantum Artificial Fish Algorithm Application to Distributed Network Considering Distributed Generation.

PubMed

Du, Tingsong; Hu, Yang; Ke, Xianting

2015-01-01

An improved quantum artificial fish swarm algorithm (IQAFSA) for solving distributed network programming considering distributed generation is proposed in this work. The IQAFSA based on quantum computing which has exponential acceleration for heuristic algorithm uses quantum bits to code artificial fish and quantum revolving gate, preying behavior, and following behavior and variation of quantum artificial fish to update the artificial fish for searching for optimal value. Then, we apply the proposed new algorithm, the quantum artificial fish swarm algorithm (QAFSA), the basic artificial fish swarm algorithm (BAFSA), and the global edition artificial fish swarm algorithm (GAFSA) to the simulation experiments for some typical test functions, respectively. The simulation results demonstrate that the proposed algorithm can escape from the local extremum effectively and has higher convergence speed and better accuracy. Finally, applying IQAFSA to distributed network problems and the simulation results for 33-bus radial distribution network system show that IQAFSA can get the minimum power loss after comparing with BAFSA, GAFSA, and QAFSA.

Improved Quantum Artificial Fish Algorithm Application to Distributed Network Considering Distributed Generation

PubMed Central

Hu, Yang; Ke, Xianting

2015-01-01

An improved quantum artificial fish swarm algorithm (IQAFSA) for solving distributed network programming considering distributed generation is proposed in this work. The IQAFSA based on quantum computing which has exponential acceleration for heuristic algorithm uses quantum bits to code artificial fish and quantum revolving gate, preying behavior, and following behavior and variation of quantum artificial fish to update the artificial fish for searching for optimal value. Then, we apply the proposed new algorithm, the quantum artificial fish swarm algorithm (QAFSA), the basic artificial fish swarm algorithm (BAFSA), and the global edition artificial fish swarm algorithm (GAFSA) to the simulation experiments for some typical test functions, respectively. The simulation results demonstrate that the proposed algorithm can escape from the local extremum effectively and has higher convergence speed and better accuracy. Finally, applying IQAFSA to distributed network problems and the simulation results for 33-bus radial distribution network system show that IQAFSA can get the minimum power loss after comparing with BAFSA, GAFSA, and QAFSA. PMID:26447713

Residential Consumer-Centric Demand-Side Management Based on Energy Disaggregation-Piloting Constrained Swarm Intelligence: Towards Edge Computing

PubMed Central

Hu, Yu-Chen

2018-01-01

The emergence of smart Internet of Things (IoT) devices has highly favored the realization of smart homes in a down-stream sector of a smart grid. The underlying objective of Demand Response (DR) schemes is to actively engage customers to modify their energy consumption on domestic appliances in response to pricing signals. Domestic appliance scheduling is widely accepted as an effective mechanism to manage domestic energy consumption intelligently. Besides, to residential customers for DR implementation, maintaining a balance between energy consumption cost and users’ comfort satisfaction is a challenge. Hence, in this paper, a constrained Particle Swarm Optimization (PSO)-based residential consumer-centric load-scheduling method is proposed. The method can be further featured with edge computing. In contrast with cloud computing, edge computing—a method of optimizing cloud computing technologies by driving computing capabilities at the IoT edge of the Internet as one of the emerging trends in engineering technology—addresses bandwidth-intensive contents and latency-sensitive applications required among sensors and central data centers through data analytics at or near the source of data. A non-intrusive load-monitoring technique proposed previously is utilized to automatic determination of physical characteristics of power-intensive home appliances from users’ life patterns. The swarm intelligence, constrained PSO, is used to minimize the energy consumption cost while considering users’ comfort satisfaction for DR implementation. The residential consumer-centric load-scheduling method proposed in this paper is evaluated under real-time pricing with inclining block rates and is demonstrated in a case study. The experimentation reported in this paper shows the proposed residential consumer-centric load-scheduling method can re-shape loads by home appliances in response to DR signals. Moreover, a phenomenal reduction in peak power consumption is achieved by 13.97%. PMID:29702607

Modeling the complex shape evolution of sedimenting particle swarms in fractures

NASA Astrophysics Data System (ADS)

Mitchell, C. A.; Nitsche, L.; Pyrak-Nolte, L. J.

2016-12-01

The flow of micro- and nano-particles through subsurface systems can occur in several environments, such as hydraulic fracturing or enhanced oil recovery. Computer simulations were performed to advance our understanding of the complexity of subsurface particle swarm transport in fractures. Previous experiments observed that particle swarms in fractures with uniform apertures exhibit enhanced transport speeds and suppressed bifurcations for an optimal range of apertures. Numerical simulations were performed for low Reynolds number, no interfacial tension and uniform viscosity conditions with particulate swarms represented by point-particles that mutually interact through their (regularized) Stokeslet fields. A P3 M technique accelerates the summations for swarms exceeding 105 particles. Fracture wall effects were incorporated using a least-squares variant of the method of fundamental solutions, with grid mapping of the surface force and source elements within the fast-summation scheme. The numerical study was executed on the basis of dimensionless variables and parameters, in the interest of examining the fundamental behavior and relationships of particle swarms in the presence of uniform apertures. Model parameters were representative of particle swarms experiments to enable direct comparison of the results with the experimental observations. The simulations confirmed that the principal phenomena observed in the experiments can be explained within the realm of Stokes flow. The numerical investigation effectively replicated swarm evolution in a uniform fracture and captured the coalescence, torus and tail formation, and ultimate breakup of the particle swarm as it fell under gravity in a quiescent fluid. The rate of swarm evolution depended on the number of particles in a swarm. When an ideal number of particles was used, swarm transport was characterized by an enhanced velocity regime as observed in the laboratory data. Understanding the physics particle swarms in fractured media will improve the ability to perform controlled micro-particulate transport through rock. Acknowledgment: This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Geosciences Research Program under Award Number (DE-FG02-09ER16022).

A solution quality assessment method for swarm intelligence optimization algorithms.

PubMed

Zhang, Zhaojun; Wang, Gai-Ge; Zou, Kuansheng; Zhang, Jianhua

2014-01-01

Nowadays, swarm intelligence optimization has become an important optimization tool and wildly used in many fields of application. In contrast to many successful applications, the theoretical foundation is rather weak. Therefore, there are still many problems to be solved. One problem is how to quantify the performance of algorithm in finite time, that is, how to evaluate the solution quality got by algorithm for practical problems. It greatly limits the application in practical problems. A solution quality assessment method for intelligent optimization is proposed in this paper. It is an experimental analysis method based on the analysis of search space and characteristic of algorithm itself. Instead of "value performance," the "ordinal performance" is used as evaluation criteria in this method. The feasible solutions were clustered according to distance to divide solution samples into several parts. Then, solution space and "good enough" set can be decomposed based on the clustering results. Last, using relative knowledge of statistics, the evaluation result can be got. To validate the proposed method, some intelligent algorithms such as ant colony optimization (ACO), particle swarm optimization (PSO), and artificial fish swarm algorithm (AFS) were taken to solve traveling salesman problem. Computational results indicate the feasibility of proposed method.

Individual Pause-and-Go Motion Is Instrumental to the Formation and Maintenance of Swarms of Marching Locust Nymphs

PubMed Central

Ariel, Gil; Ophir, Yotam; Levi, Sagi; Ben-Jacob, Eshel; Ayali, Amir

2014-01-01

The principal interactions leading to the emergence of order in swarms of marching locust nymphs was studied both experimentally, using small groups of marching locusts in the lab, and using computer simulations. We utilized a custom tracking algorithm to reveal fundamental animal-animal interactions leading to collective motion. Uncovering this behavior introduced a new agent-based modeling approach in which pause-and-go motion is pivotal. The behavioral and modeling findings are largely based on motion-related visual sensory inputs obtained by the individual locust. Results suggest a generic principle, in which intermittent animal motion can be considered as a sequence of individual decisions as animals repeatedly reassess their situation and decide whether or not to swarm. This interpretation implies, among other things, some generic characteristics regarding the build-up and emergence of collective order in swarms: in particular, that order and disorder are generic meta-stable states of the system, suggesting that the emergence of order is kinetic and does not necessarily require external environmental changes. This work calls for further experimental as well as theoretical investigation of the neural mechanisms underlying locust coordinative behavior. PMID:24988464

Multi-terminal pipe routing by Steiner minimal tree and particle swarm optimisation

NASA Astrophysics Data System (ADS)

Liu, Qiang; Wang, Chengen

2012-08-01

Computer-aided design of pipe routing is of fundamental importance for complex equipments' developments. In this article, non-rectilinear branch pipe routing with multiple terminals that can be formulated as a Euclidean Steiner Minimal Tree with Obstacles (ESMTO) problem is studied in the context of an aeroengine-integrated design engineering. Unlike the traditional methods that connect pipe terminals sequentially, this article presents a new branch pipe routing algorithm based on the Steiner tree theory. The article begins with a new algorithm for solving the ESMTO problem by using particle swarm optimisation (PSO), and then extends the method to the surface cases by using geodesics to meet the requirements of routing non-rectilinear pipes on the surfaces of aeroengines. Subsequently, the adaptive region strategy and the basic visibility graph method are adopted to increase the computation efficiency. Numeral computations show that the proposed routing algorithm can find satisfactory routing layouts while running in polynomial time.

Type IV pili interactions promote intercellular association and moderate swarming of Pseudomonas aeruginosa

PubMed Central

Anyan, Morgen E.; Amiri, Aboutaleb; Harvey, Cameron W.; Tierra, Giordano; Morales-Soto, Nydia; Driscoll, Callan M.; Alber, Mark S.; Shrout, Joshua D.

2014-01-01

Pseudomonas aeruginosa is a ubiquitous bacterium that survives in many environments, including as an acute and chronic pathogen in humans. Substantial evidence shows that P. aeruginosa behavior is affected by its motility, and appendages known as flagella and type IV pili (TFP) are known to confer such motility. The role these appendages play when not facilitating motility or attachment, however, is unclear. Here we discern a passive intercellular role of TFP during flagellar-mediated swarming of P. aeruginosa that does not require TFP extension or retraction. We studied swarming at the cellular level using a combination of laboratory experiments and computational simulations to explain the resultant patterns of cells imaged from in vitro swarms. Namely, we used a computational model to simulate swarming and to probe for individual cell behavior that cannot currently be otherwise measured. Our simulations showed that TFP of swarming P. aeruginosa should be distributed all over the cell and that TFP−TFP interactions between cells should be a dominant mechanism that promotes cell−cell interaction, limits lone cell movement, and slows swarm expansion. This predicted physical mechanism involving TFP was confirmed in vitro using pairwise mixtures of strains with and without TFP where cells without TFP separate from cells with TFP. While TFP slow swarm expansion, we show in vitro that TFP help alter collective motion to avoid toxic compounds such as the antibiotic carbenicillin. Thus, TFP physically affect P. aeruginosa swarming by actively promoting cell−cell association and directional collective motion within motile groups to aid their survival. PMID:25468980

A frozen Gaussian approximation-based multi-level particle swarm optimization for seismic inversion

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Jinglai, E-mail: jinglaili@sjtu.edu.cn; Lin, Guang, E-mail: lin491@purdue.edu; Computational Sciences and Mathematics Division, Pacific Northwest National Laboratory, Richland, WA 99352

2015-09-01

In this paper, we propose a frozen Gaussian approximation (FGA)-based multi-level particle swarm optimization (MLPSO) method for seismic inversion of high-frequency wave data. The method addresses two challenges in it: First, the optimization problem is highly non-convex, which makes hard for gradient-based methods to reach global minima. This is tackled by MLPSO which can escape from undesired local minima. Second, the character of high-frequency of seismic waves requires a large number of grid points in direct computational methods, and thus renders an extremely high computational demand on the simulation of each sample in MLPSO. We overcome this difficulty by threemore » steps: First, we use FGA to compute high-frequency wave propagation based on asymptotic analysis on phase plane; Then we design a constrained full waveform inversion problem to prevent the optimization search getting into regions of velocity where FGA is not accurate; Last, we solve the constrained optimization problem by MLPSO that employs FGA solvers with different fidelity. The performance of the proposed method is demonstrated by a two-dimensional full-waveform inversion example of the smoothed Marmousi model.« less

ANTS/SARA: Future Observation of Saturn's Rings

NASA Astrophysics Data System (ADS)

Clark, P. E.; Rilee, M. L.; Curtis, S. A.; Cheung, C. Y.; Mumma, M. J.

2004-05-01

The Saturn Autonomous Ring Array (SARA) mission concept applies the Autonomous Nano-Technology Swarm (ANTS) architecture, a paradigm developed for exploration of high surface area and/or multi-body targets. ANTS architecture involves large numbers of tiny, highly autonomous, yet socially interactive, craft, in a small number of specialist classes. SARA will acquire in situ observations in the high gravity environment of Saturn's rings. The high potential for collision represents an insurmountable challenge for previous mission designs. Each ANTS nanocraft weighs approximately a kilogram, and thus requires gossamer structures for all subsystems. Individual specialists include Workers, the vast majority, that acquire scientific measurements, as well as Messenger/Rulers that provide communication and coordination. The high density distribution of particles combines with the high intensity gravity and magnetic field environment to produce dynamic plasmas. Plasma, particle, wave, and field detectors will take measurements from the edge of the ring plane to observe the result of particle interactions. Imagers and spectrome-ters would measure variations composition and dust/gas ratio among particles using a strategy for serial rendezvous with individual particles. The numbers and distances of these particles, as well as anticipated high attrition rate, re-quire hundreds of spacecraft to characterize thousands of particles and ring features over the course of the mission. The bimodal propulsion system would include a large solar sail carrier for transporting the swarm the long distance in low gravity between deployment site and the target, and a nuclear system for each craft for maneuvering in the high gravity regime of Saturn's rings.

A hybrid protection approaches for denial of service (DoS) attacks in wireless sensor networks

NASA Astrophysics Data System (ADS)

Gunasekaran, Mahalakshmi; Periakaruppan, Subathra

2017-06-01

Wireless sensor network (WSN) contains the distributed autonomous devices with the sensing capability of physical and environmental conditions. During the clustering operation, the consumption of more energy causes the draining in battery power that leads to minimum network lifetime. Hence, the WSN devices are initially operated on low-power sleep mode to maximise the lifetime. But, the attacks arrival cause the disruption in low-power operating called denial of service (DoS) attacks. The conventional intrusion detection (ID) approaches such as rule-based and anomaly-based methods effectively detect the DoS attacks. But, the energy consumption and false detection rate are more. The absence of attack information and broadcast of its impact to the other cluster head (CH) leads to easy DoS attacks arrival. This article combines the isolation and routing tables to detect the attack in the specific cluster and broadcasts the information to other CH. The intercommunication between the CHs prevents the DoS attacks effectively. In addition, the swarm-based defence approach is proposed to migrate the fault channel to normal operating channel through frequency hop approaches. The comparative analysis between the proposed table-based intrusion detection systems (IDSs) and swarm-based defence approaches with the traditional IDS regarding the parameters of transmission overhead/efficiency, energy consumption, and false positive/negative rates proves the capability of DoS prediction/prevention in WSN.

Analysis of mutual assured destruction-like scenario with swarms of non-recallable autonomous robots

NASA Astrophysics Data System (ADS)

Straub, Jeremy

2015-05-01

This paper considers the implications of the creation of an autonomous robotic fighting force without recall-ability which could serve as a deterrent to a `total war' magnitude attack. It discusses the technical considerations for this type of robotic system and the limited enhancements required to current technologies (particularly UAVs) needed to create such a system. Particular consideration is paid to how the introduction of this type of technology by one actor could create a need for reciprocal development. Also considered is the prospective utilization of this type of technology by non-state actors and the impact of this on state actors.

Rayleigh wave dispersion curve inversion by using particle swarm optimization and genetic algorithm

NASA Astrophysics Data System (ADS)

Buyuk, Ersin; Zor, Ekrem; Karaman, Abdullah

2017-04-01

Inversion of surface wave dispersion curves with its highly nonlinear nature has some difficulties using traditional linear inverse methods due to the need and strong dependence to the initial model, possibility of trapping in local minima and evaluation of partial derivatives. There are some modern global optimization methods to overcome of these difficulties in surface wave analysis such as Genetic algorithm (GA) and Particle Swarm Optimization (PSO). GA is based on biologic evolution consisting reproduction, crossover and mutation operations, while PSO algorithm developed after GA is inspired from the social behaviour of birds or fish of swarms. Utility of these methods require plausible convergence rate, acceptable relative error and optimum computation cost that are important for modelling studies. Even though PSO and GA processes are similar in appearence, the cross-over operation in GA is not used in PSO and the mutation operation is a stochastic process for changing the genes within chromosomes in GA. Unlike GA, the particles in PSO algorithm changes their position with logical velocities according to particle's own experience and swarm's experience. In this study, we applied PSO algorithm to estimate S wave velocities and thicknesses of the layered earth model by using Rayleigh wave dispersion curve and also compared these results with GA and we emphasize on the advantage of using PSO algorithm for geophysical modelling studies considering its rapid convergence, low misfit error and computation cost.

An Approach to Economic Dispatch with Multiple Fuels Based on Particle Swarm Optimization

NASA Astrophysics Data System (ADS)

Sriyanyong, Pichet

2011-06-01

Particle Swarm Optimization (PSO), a stochastic optimization technique, shows superiority to other evolutionary computation techniques in terms of less computation time, easy implementation with high quality solution, stable convergence characteristic and independent from initialization. For this reason, this paper proposes the application of PSO to the Economic Dispatch (ED) problem, which occurs in the operational planning of power systems. In this study, ED problem can be categorized according to the different characteristics of its cost function that are ED problem with smooth cost function and ED problem with multiple fuels. Taking the multiple fuels into account will make the problem more realistic. The experimental results show that the proposed PSO algorithm is more efficient than previous approaches under consideration as well as highly promising in real world applications.

Application of GA, PSO, and ACO algorithms to path planning of autonomous underwater vehicles

NASA Astrophysics Data System (ADS)

Aghababa, Mohammad Pourmahmood; Amrollahi, Mohammad Hossein; Borjkhani, Mehdi

2012-09-01

In this paper, an underwater vehicle was modeled with six dimensional nonlinear equations of motion, controlled by DC motors in all degrees of freedom. Near-optimal trajectories in an energetic environment for underwater vehicles were computed using a numerical solution of a nonlinear optimal control problem (NOCP). An energy performance index as a cost function, which should be minimized, was defined. The resulting problem was a two-point boundary value problem (TPBVP). A genetic algorithm (GA), particle swarm optimization (PSO), and ant colony optimization (ACO) algorithms were applied to solve the resulting TPBVP. Applying an Euler-Lagrange equation to the NOCP, a conjugate gradient penalty method was also adopted to solve the TPBVP. The problem of energetic environments, involving some energy sources, was discussed. Some near-optimal paths were found using a GA, PSO, and ACO algorithms. Finally, the problem of collision avoidance in an energetic environment was also taken into account.

Mixed reality framework for collective motion patterns of swarms with delay coupling

NASA Astrophysics Data System (ADS)

Szwaykowska, Klementyna; Schwartz, Ira

The formation of coherent patterns in swarms of interacting self-propelled autonomous agents is an important subject for many applications within the field of distributed robotic systems. However, there are significant logistical challenges associated with testing fully distributed systems in real-world settings. In this paper, we provide a rigorous theoretical justification for the use of mixed-reality experiments as a stepping stone to fully physical testing of distributed robotic systems. We also model and experimentally realize a mixed-reality large-scale swarm of delay-coupled agents. Our analyses, assuming agents communicating over an Erdos-Renyi network, demonstrate the existence of stable coherent patterns that can be achieved only with delay coupling and that are robust to decreasing network connectivity and heterogeneity in agent dynamics. We show how the bifurcation structure for emergence of different patterns changes with heterogeneity in agent acceleration capabilities and limited connectivity in the network as a function of coupling strength and delay. Our results are verified through simulation as well as preliminary experimental results of delay-induced pattern formation in a mixed-reality swarm. K. S. was a National Research Council postdoctoral fellow. I.B.S was supported by the U.S. Naval Research Laboratory funding (N0001414WX00023) and office of Naval Research (N0001414WX20610).

Integrating Symbolic and Statistical Methods for Testing Intelligent Systems Applications to Machine Learning and Computer Vision

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jha, Sumit Kumar; Pullum, Laura L; Ramanathan, Arvind

Embedded intelligent systems ranging from tiny im- plantable biomedical devices to large swarms of autonomous un- manned aerial systems are becoming pervasive in our daily lives. While we depend on the flawless functioning of such intelligent systems, and often take their behavioral correctness and safety for granted, it is notoriously difficult to generate test cases that expose subtle errors in the implementations of machine learning algorithms. Hence, the validation of intelligent systems is usually achieved by studying their behavior on representative data sets, using methods such as cross-validation and bootstrapping.In this paper, we present a new testing methodology for studyingmore » the correctness of intelligent systems. Our approach uses symbolic decision procedures coupled with statistical hypothesis testing to. We also use our algorithm to analyze the robustness of a human detection algorithm built using the OpenCV open-source computer vision library. We show that the human detection implementation can fail to detect humans in perturbed video frames even when the perturbations are so small that the corresponding frames look identical to the naked eye.« less

A small, cheap, and portable reconnaissance robot

NASA Astrophysics Data System (ADS)

Kenyon, Samuel H.; Creary, D.; Thi, Dan; Maynard, Jeffrey

2005-05-01

While there is much interest in human-carriable mobile robots for defense/security applications, existing examples are still too large/heavy, and there are not many successful small human-deployable mobile ground robots, especially ones that can survive being thrown/dropped. We have developed a prototype small short-range teleoperated indoor reconnaissance/surveillance robot that is semi-autonomous. It is self-powered, self-propelled, spherical, and meant to be carried and thrown by humans into indoor, yet relatively unstructured, dynamic environments. The robot uses multiple channels for wireless control and feedback, with the potential for inter-robot communication, swarm behavior, or distributed sensor network capabilities. The primary reconnaissance sensor for this prototype is visible-spectrum video. This paper focuses more on the software issues, both the onboard intelligent real time control system and the remote user interface. The communications, sensor fusion, intelligent real time controller, etc. are implemented with onboard microcontrollers. We based the autonomous and teleoperation controls on a simple finite state machine scripting layer. Minimal localization and autonomous routines were designed to best assist the operator, execute whatever mission the robot may have, and promote its own survival. We also discuss the advantages and pitfalls of an inexpensive, rapidly-developed semi-autonomous robotic system, especially one that is spherical, and the importance of human-robot interaction as considered for the human-deployment and remote user interface.

Methods of determining complete sensor requirements for autonomous mobility

NASA Technical Reports Server (NTRS)

Curtis, Steven A. (Inventor)

2012-01-01

A method of determining complete sensor requirements for autonomous mobility of an autonomous system includes computing a time variation of each behavior of a set of behaviors of the autonomous system, determining mobility sensitivity to each behavior of the autonomous system, and computing a change in mobility based upon the mobility sensitivity to each behavior and the time variation of each behavior. The method further includes determining the complete sensor requirements of the autonomous system through analysis of the relative magnitude of the change in mobility, the mobility sensitivity to each behavior, and the time variation of each behavior, wherein the relative magnitude of the change in mobility, the mobility sensitivity to each behavior, and the time variation of each behavior are characteristic of the stability of the autonomous system.

Sustainable and Autonomic Space Exploration Missions

NASA Technical Reports Server (NTRS)

Hinchey, Michael G.; Sterritt, Roy; Rouff, Christopher; Rash, James L.; Truszkowski, Walter

2006-01-01

Visions for future space exploration have long term science missions in sight, resulting in the need for sustainable missions. Survivability is a critical property of sustainable systems and may be addressed through autonomicity, an emerging paradigm for self-management of future computer-based systems based on inspiration from the human autonomic nervous system. This paper examines some of the ongoing research efforts to realize these survivable systems visions, with specific emphasis on developments in Autonomic Policies.

Residential Consumer-Centric Demand-Side Management Based on Energy Disaggregation-Piloting Constrained Swarm Intelligence: Towards Edge Computing.

PubMed

Lin, Yu-Hsiu; Hu, Yu-Chen

2018-04-27

The emergence of smart Internet of Things (IoT) devices has highly favored the realization of smart homes in a down-stream sector of a smart grid. The underlying objective of Demand Response (DR) schemes is to actively engage customers to modify their energy consumption on domestic appliances in response to pricing signals. Domestic appliance scheduling is widely accepted as an effective mechanism to manage domestic energy consumption intelligently. Besides, to residential customers for DR implementation, maintaining a balance between energy consumption cost and users’ comfort satisfaction is a challenge. Hence, in this paper, a constrained Particle Swarm Optimization (PSO)-based residential consumer-centric load-scheduling method is proposed. The method can be further featured with edge computing. In contrast with cloud computing, edge computing—a method of optimizing cloud computing technologies by driving computing capabilities at the IoT edge of the Internet as one of the emerging trends in engineering technology—addresses bandwidth-intensive contents and latency-sensitive applications required among sensors and central data centers through data analytics at or near the source of data. A non-intrusive load-monitoring technique proposed previously is utilized to automatic determination of physical characteristics of power-intensive home appliances from users’ life patterns. The swarm intelligence, constrained PSO, is used to minimize the energy consumption cost while considering users’ comfort satisfaction for DR implementation. The residential consumer-centric load-scheduling method proposed in this paper is evaluated under real-time pricing with inclining block rates and is demonstrated in a case study. The experimentation reported in this paper shows the proposed residential consumer-centric load-scheduling method can re-shape loads by home appliances in response to DR signals. Moreover, a phenomenal reduction in peak power consumption is achieved by 13.97%.

An Improved Co-evolutionary Particle Swarm Optimization for Wireless Sensor Networks with Dynamic Deployment

PubMed Central

Wang, Xue; Wang, Sheng; Ma, Jun-Jie

2007-01-01

The effectiveness of wireless sensor networks (WSNs) depends on the coverage and target detection probability provided by dynamic deployment, which is usually supported by the virtual force (VF) algorithm. However, in the VF algorithm, the virtual force exerted by stationary sensor nodes will hinder the movement of mobile sensor nodes. Particle swarm optimization (PSO) is introduced as another dynamic deployment algorithm, but in this case the computation time required is the big bottleneck. This paper proposes a dynamic deployment algorithm which is named “virtual force directed co-evolutionary particle swarm optimization” (VFCPSO), since this algorithm combines the co-evolutionary particle swarm optimization (CPSO) with the VF algorithm, whereby the CPSO uses multiple swarms to optimize different components of the solution vectors for dynamic deployment cooperatively and the velocity of each particle is updated according to not only the historical local and global optimal solutions, but also the virtual forces of sensor nodes. Simulation results demonstrate that the proposed VFCPSO is competent for dynamic deployment in WSNs and has better performance with respect to computation time and effectiveness than the VF, PSO and VFPSO algorithms.

Smart Swarms of Bacteria-Inspired Agents with Performance Adaptable Interactions

PubMed Central

Shklarsh, Adi; Ariel, Gil; Schneidman, Elad; Ben-Jacob, Eshel

2011-01-01

Collective navigation and swarming have been studied in animal groups, such as fish schools, bird flocks, bacteria, and slime molds. Computer modeling has shown that collective behavior of simple agents can result from simple interactions between the agents, which include short range repulsion, intermediate range alignment, and long range attraction. Here we study collective navigation of bacteria-inspired smart agents in complex terrains, with adaptive interactions that depend on performance. More specifically, each agent adjusts its interactions with the other agents according to its local environment – by decreasing the peers' influence while navigating in a beneficial direction, and increasing it otherwise. We show that inclusion of such performance dependent adaptable interactions significantly improves the collective swarming performance, leading to highly efficient navigation, especially in complex terrains. Notably, to afford such adaptable interactions, each modeled agent requires only simple computational capabilities with short-term memory, which can easily be implemented in simple swarming robots. PMID:21980274

Smart swarms of bacteria-inspired agents with performance adaptable interactions.

PubMed

Shklarsh, Adi; Ariel, Gil; Schneidman, Elad; Ben-Jacob, Eshel

2011-09-01

Collective navigation and swarming have been studied in animal groups, such as fish schools, bird flocks, bacteria, and slime molds. Computer modeling has shown that collective behavior of simple agents can result from simple interactions between the agents, which include short range repulsion, intermediate range alignment, and long range attraction. Here we study collective navigation of bacteria-inspired smart agents in complex terrains, with adaptive interactions that depend on performance. More specifically, each agent adjusts its interactions with the other agents according to its local environment--by decreasing the peers' influence while navigating in a beneficial direction, and increasing it otherwise. We show that inclusion of such performance dependent adaptable interactions significantly improves the collective swarming performance, leading to highly efficient navigation, especially in complex terrains. Notably, to afford such adaptable interactions, each modeled agent requires only simple computational capabilities with short-term memory, which can easily be implemented in simple swarming robots.

Security Engineering Pilot

DTIC Science & Technology

2013-02-28

needed to detect and isolate the compromised component • Prevent a cyber attack exploit from reading enough information to form a coherent data set...Analysis Signal Copy Selected Sub-Bands • Gimbaled, Stabilized EO/IR Camera Ball • High Precision GPS & INS (eventual swarm capable inter-UAV coherent ... LIDAR , HSI, Chem-Bio • Multi-Platform Distributed Sensor Experiments (eg, MIMO) • Autonomous & Collaborative Multi-Platform Control • Space for

Command and Control of Teams of Autonomous Units

DTIC Science & Technology

2012-06-01

done by a hybrid genetic algorithm (GA) particle swarm optimization ( PSO ) algorithm called PIDGION-alternate. This training algorithm is an ANN ...human controller will recognize the behaviors as being safe and correct. As the HyperNEAT approach produces Artificial Neural Nets ( ANN ), we can...optimization technique that generates efficient ANN controls from simple environmental feedback. FALCONET has been tested showing that it can produce

Distributed pheromone-based swarming control of unmanned air and ground vehicles for RSTA

NASA Astrophysics Data System (ADS)

Sauter, John A.; Mathews, Robert S.; Yinger, Andrew; Robinson, Joshua S.; Moody, John; Riddle, Stephanie

2008-04-01

The use of unmanned vehicles in Reconnaissance, Surveillance, and Target Acquisition (RSTA) applications has received considerable attention recently. Cooperating land and air vehicles can support multiple sensor modalities providing pervasive and ubiquitous broad area sensor coverage. However coordination of multiple air and land vehicles serving different mission objectives in a dynamic and complex environment is a challenging problem. Swarm intelligence algorithms, inspired by the mechanisms used in natural systems to coordinate the activities of many entities provide a promising alternative to traditional command and control approaches. This paper describes recent advances in a fully distributed digital pheromone algorithm that has demonstrated its effectiveness in managing the complexity of swarming unmanned systems. The results of a recent demonstration at NASA's Wallops Island of multiple Aerosonde Unmanned Air Vehicles (UAVs) and Pioneer Unmanned Ground Vehicles (UGVs) cooperating in a coordinated RSTA application are discussed. The vehicles were autonomously controlled by the onboard digital pheromone responding to the needs of the automatic target recognition algorithms. UAVs and UGVs controlled by the same pheromone algorithm self-organized to perform total area surveillance, automatic target detection, sensor cueing, and automatic target recognition with no central processing or control and minimal operator input. Complete autonomy adds several safety and fault tolerance requirements which were integrated into the basic pheromone framework. The adaptive algorithms demonstrated the ability to handle some unplanned hardware failures during the demonstration without any human intervention. The paper describes lessons learned and the next steps for this promising technology.

Precise science orbits for the Swarm satellite constellation

NASA Astrophysics Data System (ADS)

van den IJssel, Jose; Encarnação, João; Doornbos, Eelco; Visser, Pieter

2015-09-01

The European Space Agency (ESA) Swarm mission was launched on 22 November 2013 to study the dynamics of the Earth's magnetic field and its interaction with the Earth system. The mission consists of three identical satellites, flying in carefully selected near polar orbits. Two satellites fly almost side-by-side at an initial altitude of about 480 km, and will descend due to drag to around 300 km during the mission lifetime. The third satellite was placed in a higher orbit of about 530 km altitude, and therefore descends much more slowly. To geolocate the Swarm observations, each satellite is equipped with an 8-channel, dual-frequency GPS receiver for Precise Orbit Determination (POD). Onboard laser retroreflectors provide the opportunity to validate the orbits computed from the GPS observations using Satellite Laser Ranging (SLR) data. Precise Science Orbits (PSOs) for the Swarm satellites are computed by the Faculty of Aerospace Engineering at Delft University of Technology in the framework of the Swarm Satellite Constellation Application and Research Facility (SCARF). The PSO product consists of both a reduced-dynamic and a kinematic orbit solution. After a short description of the Swarm GPS data characteristics, the adopted POD strategy for both orbit types is explained and first PSO results from more than one year of Swarm GPS data are presented. Independent SLR validation shows that the reduced-dynamic Swarm PSOs have an accuracy of better than 2 cm, while the kinematic orbits have a slightly reduced accuracy of about 4-5 cm. Orbit comparisons indicate that the consistency between the reduced-dynamic and kinematic Swarm PSO for most parts of the Earth is at the 4-5 cm level. Close to the geomagnetic poles and along the geomagnetic equator, however, the kinematic orbits show larger errors, which are probably due to ionospheric scintillations that affect the Swarm GPS receivers over these areas.

Supervised self-organization of homogeneous swarms using ergodic projections of Markov chains.

PubMed

Chattopadhyay, Ishanu; Ray, Asok

2009-12-01

This paper formulates a self-organization algorithm to address the problem of global behavior supervision in engineered swarms of arbitrarily large population sizes. The swarms considered in this paper are assumed to be homogeneous collections of independent identical finite-state agents, each of which is modeled by an irreducible finite Markov chain. The proposed algorithm computes the necessary perturbations in the local agents' behavior, which guarantees convergence to the desired observed state of the swarm. The ergodicity property of the swarm, which is induced as a result of the irreducibility of the agent models, implies that while the local behavior of the agents converges to the desired behavior only in the time average, the overall swarm behavior converges to the specification and stays there at all times. A simulation example illustrates the underlying concept.

Swarm robotics and complex behaviour of continuum material

NASA Astrophysics Data System (ADS)

dell'Erba, Ramiro

2018-05-01

In swarm robotics, just as for an animal swarm in nature, one of the aims is to reach and maintain a desired configuration. One of the possibilities for the team, to reach this aim, is to see what its neighbours are doing. This approach generates a rules system governing the movement of the single robot just by reference to neighbour's motion. The same approach is used in position-based dynamics to simulate behaviour of complex continuum materials under deformation. Therefore, in some previous works, we have considered a two-dimensional lattice of particles and calculated its time evolution by using a rules system derived from our experience in swarm robotics. The new position of a particle, like the element of a swarm, is determined by the spatial position of the other particles. No dynamic is considered, but it can be thought as being hidden in the behaviour rules. This method has given good results in some simple situations reproducing the behaviour of deformable bodies under imposed strain. In this paper we try to stress our model to highlight its limits and how they can be improved. Some other, more complex, examples are computed and discussed. Shear test, different lattices, different fracture mechanisms and ASTM shape sample behaviour have been investigated by the software tool we have developed.

Multiple-Swarm Ensembles: Improving the Predictive Power and Robustness of Predictive Models and Its Use in Computational Biology.

PubMed

Alves, Pedro; Liu, Shuang; Wang, Daifeng; Gerstein, Mark

2018-01-01

Machine learning is an integral part of computational biology, and has already shown its use in various applications, such as prognostic tests. In the last few years in the non-biological machine learning community, ensembling techniques have shown their power in data mining competitions such as the Netflix challenge; however, such methods have not found wide use in computational biology. In this work, we endeavor to show how ensembling techniques can be applied to practical problems, including problems in the field of bioinformatics, and how they often outperform other machine learning techniques in both predictive power and robustness. Furthermore, we develop a methodology of ensembling, Multi-Swarm Ensemble (MSWE) by using multiple particle swarm optimizations and demonstrate its ability to further enhance the performance of ensembles.

Swarm intelligence in bioinformatics: methods and implementations for discovering patterns of multiple sequences.

PubMed

Cui, Zhihua; Zhang, Yi

2014-02-01

As a promising and innovative research field, bioinformatics has attracted increasing attention recently. Beneath the enormous number of open problems in this field, one fundamental issue is about the accurate and efficient computational methodology that can deal with tremendous amounts of data. In this paper, we survey some applications of swarm intelligence to discover patterns of multiple sequences. To provide a deep insight, ant colony optimization, particle swarm optimization, artificial bee colony and artificial fish swarm algorithm are selected, and their applications to multiple sequence alignment and motif detecting problem are discussed.

Swarm Intelligence Optimization and Its Applications

NASA Astrophysics Data System (ADS)

Ding, Caichang; Lu, Lu; Liu, Yuanchao; Peng, Wenxiu

Swarm Intelligence is a computational and behavioral metaphor for solving distributed problems inspired from biological examples provided by social insects such as ants, termites, bees, and wasps and by swarm, herd, flock, and shoal phenomena in vertebrates such as fish shoals and bird flocks. An example of successful research direction in Swarm Intelligence is ant colony optimization (ACO), which focuses on combinatorial optimization problems. Ant algorithms can be viewed as multi-agent systems (ant colony), where agents (individual ants) solve required tasks through cooperation in the same way that ants create complex social behavior from the combined efforts of individuals.

A Novel Consensus-Based Particle Swarm Optimization-Assisted Trust-Tech Methodology for Large-Scale Global Optimization.

PubMed

Zhang, Yong-Feng; Chiang, Hsiao-Dong

2017-09-01

A novel three-stage methodology, termed the "consensus-based particle swarm optimization (PSO)-assisted Trust-Tech methodology," to find global optimal solutions for nonlinear optimization problems is presented. It is composed of Trust-Tech methods, consensus-based PSO, and local optimization methods that are integrated to compute a set of high-quality local optimal solutions that can contain the global optimal solution. The proposed methodology compares very favorably with several recently developed PSO algorithms based on a set of small-dimension benchmark optimization problems and 20 large-dimension test functions from the CEC 2010 competition. The analytical basis for the proposed methodology is also provided. Experimental results demonstrate that the proposed methodology can rapidly obtain high-quality optimal solutions that can contain the global optimal solution. The scalability of the proposed methodology is promising.

Decision-making in honeybee swarms based on quality and distance information of candidate nest sites.

PubMed

Laomettachit, Teeraphan; Termsaithong, Teerasit; Sae-Tang, Anuwat; Duangphakdee, Orawan

2015-01-07

In the nest-site selection process of honeybee swarms, an individual bee performs a waggle dance to communicate information about direction, quality, and distance of a discovered site to other bees at the swarm. Initially, different groups of bees dance to represent different potential sites, but eventually the swarm usually reaches an agreement for only one site. Here, we model the nest-site selection process in honeybee swarms of Apis mellifera and show how the swarms make adaptive decisions based on a trade-off between the quality and distance to candidate nest sites. We use bifurcation analysis and stochastic simulations to reveal that the swarm's site distance preference is moderate>near>far when the swarms choose between low quality sites. However, the distance preference becomes near>moderate>far when the swarms choose between high quality sites. Our simulations also indicate that swarms with large population size prefer nearer sites and, in addition, are more adaptive at making decisions based on available information compared to swarms with smaller population size. Copyright © 2014 Elsevier Ltd. All rights reserved.

An efficient repeating signal detector to investigate earthquake swarms

NASA Astrophysics Data System (ADS)

Skoumal, Robert J.; Brudzinski, Michael R.; Currie, Brian S.

2016-08-01

Repetitive earthquake swarms have been recognized as key signatures in fluid injection induced seismicity, precursors to volcanic eruptions, and slow slip events preceding megathrust earthquakes. We investigate earthquake swarms by developing a Repeating Signal Detector (RSD), a computationally efficient algorithm utilizing agglomerative clustering to identify similar waveforms buried in years of seismic recordings using a single seismometer. Instead of relying on existing earthquake catalogs of larger earthquakes, RSD identifies characteristic repetitive waveforms by rapidly identifying signals of interest above a low signal-to-noise ratio and then grouping based on spectral and time domain characteristics, resulting in dramatically shorter processing time than more exhaustive autocorrelation approaches. We investigate seismicity in four regions using RSD: (1) volcanic seismicity at Mammoth Mountain, California, (2) subduction-related seismicity in Oaxaca, Mexico, (3) induced seismicity in Central Alberta, Canada, and (4) induced seismicity in Harrison County, Ohio. In each case, RSD detects a similar or larger number of earthquakes than existing catalogs created using more time intensive methods. In Harrison County, RSD identifies 18 seismic sequences that correlate temporally and spatially to separate hydraulic fracturing operations, 15 of which were previously unreported. RSD utilizes a single seismometer for earthquake detection which enables seismicity to be quickly identified in poorly instrumented regions at the expense of relying on another method to locate the new detections. Due to the smaller computation overhead and success at distances up to ~50 km, RSD is well suited for real-time detection of low-magnitude earthquake swarms with permanent regional networks.

Guidance and control of swarms of spacecraft

NASA Astrophysics Data System (ADS)

Morgan, Daniel James

There has been considerable interest in formation flying spacecraft due to their potential to perform certain tasks at a cheaper cost than monolithic spacecraft. Formation flying enables the use of smaller, cheaper spacecraft that distribute the risk of the mission. Recently, the ideas of formation flying have been extended to spacecraft swarms made up of hundreds to thousands of 100-gram-class spacecraft known as femtosatellites. The large number of spacecraft and limited capabilities of each individual spacecraft present a significant challenge in guidance, navigation, and control. This dissertation deals with the guidance and control algorithms required to enable the flight of spacecraft swarms. The algorithms developed in this dissertation are focused on achieving two main goals: swarm keeping and swarm reconfiguration. The objectives of swarm keeping are to maintain bounded relative distances between spacecraft, prevent collisions between spacecraft, and minimize the propellant used by each spacecraft. Swarm reconfiguration requires the transfer of the swarm to a specific shape. Like with swarm keeping, minimizing the propellant used and preventing collisions are the main objectives. Additionally, the algorithms required for swarm keeping and swarm reconfiguration should be decentralized with respect to communication and computation so that they can be implemented on femtosats, which have limited hardware capabilities. The algorithms developed in this dissertation are concerned with swarms located in low Earth orbit. In these orbits, Earth oblateness and atmospheric drag have a significant effect on the relative motion of the swarm. The complicated dynamic environment of low Earth orbits further complicates the swarm-keeping and swarm-reconfiguration problems. To better develop and test these algorithms, a nonlinear, relative dynamic model with J2 and drag perturbations is developed. This model is used throughout this dissertation to validate the algorithms using computer simulations. The swarm-keeping problem can be solved by placing the spacecraft on J2-invariant relative orbits, which prevent collisions and minimize the drift of the swarm over hundreds of orbits using a single burn. These orbits are achieved by energy matching the spacecraft to the reference orbit. Additionally, these conditions can be repeatedly applied to minimize the drift of the swarm when atmospheric drag has a large effect (orbits with an altitude under 500 km). The swarm reconfiguration is achieved using two steps: trajectory optimization and assignment. The trajectory optimization problem can be written as a nonlinear, optimal control problem. This optimal control problem is discretized, decoupled, and convexified so that the individual femtosats can efficiently solve the optimization. Sequential convex programming is used to generate the control sequences and trajectories required to safely and efficiently transfer a spacecraft from one position to another. The sequence of trajectories is shown to converge to a Karush-Kuhn-Tucker point of the nonconvex problem. In the case where many of the spacecraft are interchangeable, a variable-swarm, distributed auction algorithm is used to determine the assignment of spacecraft to target positions. This auction algorithm requires only local communication and all of the bidding parameters are stored locally. The assignment generated using this auction algorithm is shown to be near optimal and to converge in a finite number of bids. Additionally, the bidding process is used to modify the number of targets used in the assignment so that the reconfiguration can be achieved even when there is a disconnected communication network or a significant loss of agents. Once the assignment is achieved, the trajectory optimization can be run using the terminal positions determined by the auction algorithm. To implement these algorithms in real time a model predictive control formulation is used. Model predictive control uses a finite horizon to apply the most up-to-date control sequence while simultaneously calculating a new assignment and trajectory based on updated state information. Using a finite horizon allows collisions to only be considered between spacecraft that are near each other at the current time. This relaxes the all-to-all communication assumption so that only neighboring agents need to communicate. Experimental validation is done using the formation flying testbed. The swarm-reconfiguration algorithms are tested using multiple quadrotors. Experiments have been performed using sequential convex programming for offline trajectory planning, model predictive control and sequential convex programming for real-time trajectory generation, and the variable-swarm, distributed auction algorithm for optimal assignment. These experiments show that the swarm-reconfiguration algorithms can be implemented in real time using actual hardware. In general, this dissertation presents guidance and control algorithms that maintain and reconfigure swarms of spacecraft while maintaining the shape of the swarm, preventing collisions between the spacecraft, and minimizing the amount of propellant used.

A Modified Particle Swarm Optimization Technique for Finding Optimal Designs for Mixture Models

PubMed Central

Wong, Weng Kee; Chen, Ray-Bing; Huang, Chien-Chih; Wang, Weichung

2015-01-01

Particle Swarm Optimization (PSO) is a meta-heuristic algorithm that has been shown to be successful in solving a wide variety of real and complicated optimization problems in engineering and computer science. This paper introduces a projection based PSO technique, named ProjPSO, to efficiently find different types of optimal designs, or nearly optimal designs, for mixture models with and without constraints on the components, and also for related models, like the log contrast models. We also compare the modified PSO performance with Fedorov's algorithm, a popular algorithm used to generate optimal designs, Cocktail algorithm, and the recent algorithm proposed by [1]. PMID:26091237

Revolutionizing Remote Exploration with ANTS

NASA Astrophysics Data System (ADS)

Clark, P. E.; Rilee, M. L.; Curtis, S.; Truszkowski, W.

2002-05-01

We are developing the Autonomous Nano-Technology Swarm (ANTS) architecture based on an insect colony analogue for the cost-effective, efficient, systematic survey of remote or inaccessible areas with multiple object targets, including planetary surface, marine, airborne, and space environments. The mission context is the exploration in the 2020s of the most compelling remaining targets in the solar system: main belt asteroids. Main belt asteroids harbor important clues to Solar System origins and evolution which are central to NASA's goals in Space Science. Asteroids are smaller than planets, but their number is far greater, and their combined surface area likely dwarfs the Earth's. An asteroid survey will dramatically increase our understanding of the local resources available for the Human Exploration and Development of Space. During the mission composition, shape, gravity, and orbit parameters could be returned to Earth for perhaps several thousand asteroids. A survey of this area will rival the great explorations that encircled this globe, opened up the New World, and laid the groundwork for the progress and challenges of the last centuries. The ANTS architecture for a main belt survey consists of a swarm of as many as a thousand or more highly specialized pico-spacecraft that form teams to survey as many as one hundred asteroids a month. Multi-level autonomy is critical for ANTS and the objective of the proposed study is to work through the implications and constraints this entails. ANTS couples biologically inspired autonomic control for basic functions to higher level artificial intelligence that together enable individual spacecraft to operate as specialized, cooperative, social agents. This revolutionary approach postulates highly advanced, but familiar, components integrated and operated in a way that uniquely transcends any evolutionary extrapolation of existing trends and enables thousand-spacecraft missions.

Adaptive infinite impulse response system identification using modified-interior search algorithm with Lèvy flight.

PubMed

Kumar, Manjeet; Rawat, Tarun Kumar; Aggarwal, Apoorva

2017-03-01

In this paper, a new meta-heuristic optimization technique, called interior search algorithm (ISA) with Lèvy flight is proposed and applied to determine the optimal parameters of an unknown infinite impulse response (IIR) system for the system identification problem. ISA is based on aesthetics, which is commonly used in interior design and decoration processes. In ISA, composition phase and mirror phase are applied for addressing the nonlinear and multimodal system identification problems. System identification using modified-ISA (M-ISA) based method involves faster convergence, single parameter tuning and does not require derivative information because it uses a stochastic random search using the concepts of Lèvy flight. A proper tuning of control parameter has been performed in order to achieve a balance between intensification and diversification phases. In order to evaluate the performance of the proposed method, mean square error (MSE), computation time and percentage improvement are considered as the performance measure. To validate the performance of M-ISA based method, simulations has been carried out for three benchmarked IIR systems using same order and reduced order system. Genetic algorithm (GA), particle swarm optimization (PSO), cat swarm optimization (CSO), cuckoo search algorithm (CSA), differential evolution using wavelet mutation (DEWM), firefly algorithm (FFA), craziness based particle swarm optimization (CRPSO), harmony search (HS) algorithm, opposition based harmony search (OHS) algorithm, hybrid particle swarm optimization-gravitational search algorithm (HPSO-GSA) and ISA are also used to model the same examples and simulation results are compared. Obtained results confirm the efficiency of the proposed method. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Biomimicry of symbiotic multi-species coevolution for discrete and continuous optimization in RFID networks.

PubMed

Lin, Na; Chen, Hanning; Jing, Shikai; Liu, Fang; Liang, Xiaodan

2017-03-01

In recent years, symbiosis as a rich source of potential engineering applications and computational model has attracted more and more attentions in the adaptive complex systems and evolution computing domains. Inspired by different symbiotic coevolution forms in nature, this paper proposed a series of multi-swarm particle swarm optimizers called PS 2 Os, which extend the single population particle swarm optimization (PSO) algorithm to interacting multi-swarms model by constructing hierarchical interaction topologies and enhanced dynamical update equations. According to different symbiotic interrelationships, four versions of PS 2 O are initiated to mimic mutualism, commensalism, predation, and competition mechanism, respectively. In the experiments, with five benchmark problems, the proposed algorithms are proved to have considerable potential for solving complex optimization problems. The coevolutionary dynamics of symbiotic species in each PS 2 O version are also studied respectively to demonstrate the heterogeneity of different symbiotic interrelationships that effect on the algorithm's performance. Then PS 2 O is used for solving the radio frequency identification (RFID) network planning (RNP) problem with a mixture of discrete and continuous variables. Simulation results show that the proposed algorithm outperforms the reference algorithms for planning RFID networks, in terms of optimization accuracy and computation robustness.

Autonomic Computing: Panacea or Poppycock?

NASA Technical Reports Server (NTRS)

Sterritt, Roy; Hinchey, Mike

2005-01-01

Autonomic Computing arose out of a need for a means to cope with rapidly growing complexity of integrating, managing, and operating computer-based systems as well as a need to reduce the total cost of ownership of today's systems. Autonomic Computing (AC) as a discipline was proposed by IBM in 2001, with the vision to develop self-managing systems. As the name implies, the influence for the new paradigm is the human body's autonomic system, which regulates vital bodily functions such as the control of heart rate, the body's temperature and blood flow-all without conscious effort. The vision is to create selfivare through self-* properties. The initial set of properties, in terms of objectives, were self-configuring, self-healing, self-optimizing and self-protecting, along with attributes of self-awareness, self-monitoring and self-adjusting. This self-* list has grown: self-anticipating, self-critical, self-defining, self-destructing, self-diagnosis, self-governing, self-organized, self-reflecting, and self-simulation, for instance.

Confidential and Authenticated Communications in a Large Fixed-Wing UAV Swarm

DTIC Science & Technology

2016-12-01

either a UAV or a ground station. Asymmetric cryptography is not an option for swarm communications. It is a potential option for initially keying or...each UAV grows ten bytes for each UAV in the swarm, and a 30% overhead is added on for worst case cryptography . The resulting throughput is...analysis in Section IV, we can predict the burden that cryptography places on the ODroid computer. Given that the average unencrypted message size was

An Integrated Method Based on PSO and EDA for the Max-Cut Problem.

PubMed

Lin, Geng; Guan, Jian

2016-01-01

The max-cut problem is NP-hard combinatorial optimization problem with many real world applications. In this paper, we propose an integrated method based on particle swarm optimization and estimation of distribution algorithm (PSO-EDA) for solving the max-cut problem. The integrated algorithm overcomes the shortcomings of particle swarm optimization and estimation of distribution algorithm. To enhance the performance of the PSO-EDA, a fast local search procedure is applied. In addition, a path relinking procedure is developed to intensify the search. To evaluate the performance of PSO-EDA, extensive experiments were carried out on two sets of benchmark instances with 800 to 20,000 vertices from the literature. Computational results and comparisons show that PSO-EDA significantly outperforms the existing PSO-based and EDA-based algorithms for the max-cut problem. Compared with other best performing algorithms, PSO-EDA is able to find very competitive results in terms of solution quality.

Swarm-based medicine.

PubMed

Putora, Paul Martin; Oldenburg, Jan

2013-09-19

Occasionally, medical decisions have to be taken in the absence of evidence-based guidelines. Other sources can be drawn upon to fill in the gaps, including experience and intuition. Authorities or experts, with their knowledge and experience, may provide further input--known as "eminence-based medicine". Due to the Internet and digital media, interactions among physicians now take place at a higher rate than ever before. With the rising number of interconnected individuals and their communication capabilities, the medical community is obtaining the properties of a swarm. The way individual physicians act depends on other physicians; medical societies act based on their members. Swarm behavior might facilitate the generation and distribution of knowledge as an unconscious process. As such, "swarm-based medicine" may add a further source of information to the classical approaches of evidence- and eminence-based medicine. How to integrate swarm-based medicine into practice is left to the individual physician, but even this decision will be influenced by the swarm.

A design approach for small vision-based autonomous vehicles

NASA Astrophysics Data System (ADS)

Edwards, Barrett B.; Fife, Wade S.; Archibald, James K.; Lee, Dah-Jye; Wilde, Doran K.

2006-10-01

This paper describes the design of a small autonomous vehicle based on the Helios computing platform, a custom FPGA-based board capable of supporting on-board vision. Target applications for the Helios computing platform are those that require lightweight equipment and low power consumption. To demonstrate the capabilities of FPGAs in real-time control of autonomous vehicles, a 16 inch long R/C monster truck was outfitted with a Helios board. The platform provided by such a small vehicle is ideal for testing and development. The proof of concept application for this autonomous vehicle was a timed race through an environment with obstacles. Given the size restrictions of the vehicle and its operating environment, the only feasible on-board sensor is a small CMOS camera. The single video feed is therefore the only source of information from the surrounding environment. The image is then segmented and processed by custom logic in the FPGA that also controls direction and speed of the vehicle based on visual input.

A Model-Driven Architecture Approach for Modeling, Specifying and Deploying Policies in Autonomous and Autonomic Systems

NASA Technical Reports Server (NTRS)

Pena, Joaquin; Hinchey, Michael G.; Sterritt, Roy; Ruiz-Cortes, Antonio; Resinas, Manuel

2006-01-01

Autonomic Computing (AC), self-management based on high level guidance from humans, is increasingly gaining momentum as the way forward in designing reliable systems that hide complexity and conquer IT management costs. Effectively, AC may be viewed as Policy-Based Self-Management. The Model Driven Architecture (MDA) approach focuses on building models that can be transformed into code in an automatic manner. In this paper, we look at ways to implement Policy-Based Self-Management by means of models that can be converted to code using transformations that follow the MDA philosophy. We propose a set of UML-based models to specify autonomic and autonomous features along with the necessary procedures, based on modification and composition of models, to deploy a policy as an executing system.

A novel artificial fish swarm algorithm for recalibration of fiber optic gyroscope error parameters.

PubMed

Gao, Yanbin; Guan, Lianwu; Wang, Tingjun; Sun, Yunlong

2015-05-05

The artificial fish swarm algorithm (AFSA) is one of the state-of-the-art swarm intelligent techniques, which is widely utilized for optimization purposes. Fiber optic gyroscope (FOG) error parameters such as scale factors, biases and misalignment errors are relatively unstable, especially with the environmental disturbances and the aging of fiber coils. These uncalibrated error parameters are the main reasons that the precision of FOG-based strapdown inertial navigation system (SINS) degraded. This research is mainly on the application of a novel artificial fish swarm algorithm (NAFSA) on FOG error coefficients recalibration/identification. First, the NAFSA avoided the demerits (e.g., lack of using artificial fishes' pervious experiences, lack of existing balance between exploration and exploitation, and high computational cost) of the standard AFSA during the optimization process. To solve these weak points, functional behaviors and the overall procedures of AFSA have been improved with some parameters eliminated and several supplementary parameters added. Second, a hybrid FOG error coefficients recalibration algorithm has been proposed based on NAFSA and Monte Carlo simulation (MCS) approaches. This combination leads to maximum utilization of the involved approaches for FOG error coefficients recalibration. After that, the NAFSA is verified with simulation and experiments and its priorities are compared with that of the conventional calibration method and optimal AFSA. Results demonstrate high efficiency of the NAFSA on FOG error coefficients recalibration.

Stigmergy based behavioural coordination for satellite clusters

NASA Astrophysics Data System (ADS)

Tripp, Howard; Palmer, Phil

2010-04-01

Multi-platform swarm/cluster missions are an attractive prospect for improved science return as they provide a natural capability for temporal, spatial and signal separation with further engineering and economic advantages. As spacecraft numbers increase and/or the round-trip communications delay from Earth lengthens, the traditional "remote-control" approach begins to break down. It is therefore essential to push control into space; to make spacecraft more autonomous. An autonomous group of spacecraft requires coordination, but standard terrestrial paradigms such as negotiation, require high levels of inter-spacecraft communication, which is nontrivial in space. This article therefore introduces the principals of stigmergy as a novel method for coordinating a cluster. Stigmergy is an agent-based, behavioural approach that allows for infrequent communication with decisions based on local information. Behaviours are selected dynamically using a genetic algorithm onboard. supervisors/ground stations occasionally adjust parameters and disseminate a "common environment" that is used for local decisions. After outlining the system, an analysis of some crucial parameters such as communications overhead and number of spacecraft is presented to demonstrate scalability. Further scenarios are considered to demonstrate the natural ability to deal with dynamic situations such as the failure of spacecraft, changing mission objectives and responding to sudden bursts of high priority tasks.

Preliminary Design of an Autonomous Underwater Vehicle Using Multi-Objective Optimization

DTIC Science & Technology

2014-03-01

fuel cell PC propulsive coefficient PEMFC proton exchange membrane fuel cell PHP propulsive horsepower PO Pareto optimal PSO particle swarm...membrane fuel cell ( PEMFC ), molten carbonate fuel cell (MCFC), solid oxide fuel cell (SOFC) and direct and indirect methanol fuel cell (DMFC). Figure...of fuel cells in depth, I will note that PEMFCs are smaller and have a lower operating temperature compared to the other types. Those are the main

Bio-inspired UAV routing, source localization, and acoustic signature classification for persistent surveillance

NASA Astrophysics Data System (ADS)

Burman, Jerry; Hespanha, Joao; Madhow, Upamanyu; Pham, Tien

2011-06-01

A team consisting of Teledyne Scientific Company, the University of California at Santa Barbara and the Army Research Laboratory* is developing technologies in support of automated data exfiltration from heterogeneous battlefield sensor networks to enhance situational awareness for dismounts and command echelons. Unmanned aerial vehicles (UAV) provide an effective means to autonomously collect data from a sparse network of unattended ground sensors (UGSs) that cannot communicate with each other. UAVs are used to reduce the system reaction time by generating autonomous collection routes that are data-driven. Bio-inspired techniques for search provide a novel strategy to detect, capture and fuse data. A fast and accurate method has been developed to localize an event by fusing data from a sparse number of UGSs. This technique uses a bio-inspired algorithm based on chemotaxis or the motion of bacteria seeking nutrients in their environment. A unique acoustic event classification algorithm was also developed based on using swarm optimization. Additional studies addressed the problem of routing multiple UAVs, optimally placing sensors in the field and locating the source of gunfire at helicopters. A field test was conducted in November of 2009 at Camp Roberts, CA. The field test results showed that a system controlled by bio-inspired software algorithms can autonomously detect and locate the source of an acoustic event with very high accuracy and visually verify the event. In nine independent test runs of a UAV, the system autonomously located the position of an explosion nine times with an average accuracy of 3 meters. The time required to perform source localization using the UAV was on the order of a few minutes based on UAV flight times. In June 2011, additional field tests of the system will be performed and will include multiple acoustic events, optimal sensor placement based on acoustic phenomenology and the use of the International Technology Alliance (ITA) Sensor Network Fabric (IBM).

A novel artificial fish swarm algorithm for solving large-scale reliability-redundancy application problem.

PubMed

He, Qiang; Hu, Xiangtao; Ren, Hong; Zhang, Hongqi

2015-11-01

A novel artificial fish swarm algorithm (NAFSA) is proposed for solving large-scale reliability-redundancy allocation problem (RAP). In NAFSA, the social behaviors of fish swarm are classified in three ways: foraging behavior, reproductive behavior, and random behavior. The foraging behavior designs two position-updating strategies. And, the selection and crossover operators are applied to define the reproductive ability of an artificial fish. For the random behavior, which is essentially a mutation strategy, the basic cloud generator is used as the mutation operator. Finally, numerical results of four benchmark problems and a large-scale RAP are reported and compared. NAFSA shows good performance in terms of computational accuracy and computational efficiency for large scale RAP. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

An Approach to Self-Assembling Swarm Robots Using Multitree Genetic Programming

PubMed Central

An, Jinung

2013-01-01

In recent days, self-assembling swarm robots have been studied by a number of researchers due to their advantages such as high efficiency, stability, and scalability. However, there are still critical issues in applying them to practical problems in the real world. The main objective of this study is to develop a novel self-assembling swarm robot algorithm that overcomes the limitations of existing approaches. To this end, multitree genetic programming is newly designed to efficiently discover a set of patterns necessary to carry out the mission of the self-assembling swarm robots. The obtained patterns are then incorporated into their corresponding robot modules. The computational experiments prove the effectiveness of the proposed approach. PMID:23861655

An approach to self-assembling swarm robots using multitree genetic programming.

PubMed

Lee, Jong-Hyun; Ahn, Chang Wook; An, Jinung

2013-01-01

In recent days, self-assembling swarm robots have been studied by a number of researchers due to their advantages such as high efficiency, stability, and scalability. However, there are still critical issues in applying them to practical problems in the real world. The main objective of this study is to develop a novel self-assembling swarm robot algorithm that overcomes the limitations of existing approaches. To this end, multitree genetic programming is newly designed to efficiently discover a set of patterns necessary to carry out the mission of the self-assembling swarm robots. The obtained patterns are then incorporated into their corresponding robot modules. The computational experiments prove the effectiveness of the proposed approach.

Next Generation System and Software Architectures: Challenges from Future NASA Exploration Missions

NASA Technical Reports Server (NTRS)

Sterritt, Roy; Rouff, Christopher A.; Hinchey, Michael G.; Rash, James L.; Truszkowski, Walt

2006-01-01

The four key objective properties of a system that are required of it in order for it to qualify as "autonomic" are now well-accepted-self-configuring, self-healing, self-protecting, and self-optimizing- together with the attribute properties-viz. self-aware, environment-aware, self-monitoring and self- adjusting. This paper describes the need for next generation system software architectures, where components are agents, rather than objects masquerading as agents, and where support is provided for self-* properties (both existing self-chop and emerging self-* properties). These are discussed as exhibited in NASA missions, and in particular with reference to a NASA concept mission, ANTS, which is illustrative of future NASA exploration missions based on the technology of intelligent swarms.

Design of Linear Accelerator (LINAC) tanks for proton therapy via Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) approaches

DOE Office of Scientific and Technical Information (OSTI.GOV)

Castellano, T.; De Palma, L.; Laneve, D.

2015-07-01

A homemade computer code for designing a Side- Coupled Linear Accelerator (SCL) is written. It integrates a simplified model of SCL tanks with the Particle Swarm Optimization (PSO) algorithm. The computer code main aim is to obtain useful guidelines for the design of Linear Accelerator (LINAC) resonant cavities. The design procedure, assisted via the aforesaid approach seems very promising, allowing future improvements towards the optimization of actual accelerating geometries. (authors)

Cloud-Based Perception and Control of Sensor Nets and Robot Swarms

DTIC Science & Technology

2016-04-01

distributed stream processing framework provides the necessary API and infrastructure to develop and execute such applications in a cluster of computation...streaming DDDAS applications based on challenges they present to the backend Cloud control system. Figure 2 Parallel SLAM Application 3 1) Set of...the art deep learning- based object detectors can recognize among hundreds of object classes and this capability would be very useful for mobile

Biomanufacturing and self-propulsion dynamics of nanoscale bacteria-enabled autonomous delivery systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Traore, Mahama A.; Behkam, Bahareh, E-mail: behkam@vt.edu; School of Biomedical Engineering and Sciences, Virginia Tech, Blacksburg, Virginia 24061

Flagellated bacteria have superb self-propulsion capabilities and are able to effectively move through highly viscous fluid and semi-solid (porous) environments. This innate aptitude has been harvested for whole-cell actuation of bio-hybrid microrobotic systems with applications in directed transport and microassembly. In this work, we present the biomanufacturing of Nanoscale Bacteria-Enabled Autonomous Delivery Systems (NanoBEADS) by controlled self-assembly and investigate the role of nanoparticle load on the dynamics of their self-propulsion in aqueous environments. Each NanoBEADS agent is comprised of spherical polystyrene nanoparticles assembled onto the body of a flagellated Escherichia coli bacterium. We demonstrate that the NanoBEADS assembly configuration ismore » strongly dependent upon the nanoparticles to bacteria ratio. Furthermore, we characterized the stochastic motion of the NanoBEADS as a function of the quantity and size of the nanoparticle load and computationally analyzed the effect of the nanoparticle load on the experienced drag force. We report that the average NanoBEADS swimming speed is reduced to 65% of the free-swimming bacteria speed (31 μm/s) at the highest possible load. NanoBEADS can be utilized as single agents or in a collaborative swarm in order to carry out specific tasks in a wide range of applications ranging from drug delivery to whole cell biosensing.« less

Optimization of C4.5 algorithm-based particle swarm optimization for breast cancer diagnosis

NASA Astrophysics Data System (ADS)

Muslim, M. A.; Rukmana, S. H.; Sugiharti, E.; Prasetiyo, B.; Alimah, S.

2018-03-01

Data mining has become a basic methodology for computational applications in the field of medical domains. Data mining can be applied in the health field such as for diagnosis of breast cancer, heart disease, diabetes and others. Breast cancer is most common in women, with more than one million cases and nearly 600,000 deaths occurring worldwide each year. The most effective way to reduce breast cancer deaths was by early diagnosis. This study aims to determine the level of breast cancer diagnosis. This research data uses Wisconsin Breast Cancer dataset (WBC) from UCI machine learning. The method used in this research is the algorithm C4.5 and Particle Swarm Optimization (PSO) as a feature option and to optimize the algorithm. C4.5. Ten-fold cross-validation is used as a validation method and a confusion matrix. The result of this research is C4.5 algorithm. The particle swarm optimization C4.5 algorithm has increased by 0.88%.

A hybrid binary particle swarm optimization for large capacitated multi item multi level lot sizing (CMIMLLS) problem

NASA Astrophysics Data System (ADS)

Mishra, S. K.; Sahithi, V. V. D.; Rao, C. S. P.

2016-09-01

The lot sizing problem deals with finding optimal order quantities which minimizes the ordering and holding cost of product mix. when multiple items at multiple levels with all capacity restrictions are considered, the lot sizing problem become NP hard. Many heuristics were developed in the past have inevitably failed due to size, computational complexity and time. However the authors were successful in the development of PSO based technique namely iterative improvement binary particles swarm technique to address very large capacitated multi-item multi level lot sizing (CMIMLLS) problem. First binary particle Swarm Optimization algorithm is used to find a solution in a reasonable time and iterative improvement local search mechanism is employed to improvise the solution obtained by BPSO algorithm. This hybrid mechanism of using local search on the global solution is found to improve the quality of solutions with respect to time thus IIBPSO method is found best and show excellent results.

Exploring protein kinase conformation using swarm-enhanced sampling molecular dynamics.

PubMed

Atzori, Alessio; Bruce, Neil J; Burusco, Kepa K; Wroblowski, Berthold; Bonnet, Pascal; Bryce, Richard A

2014-10-27

Protein plasticity, while often linked to biological function, also provides opportunities for rational design of selective and potent inhibitors of their function. The application of computational methods to the prediction of concealed protein concavities is challenging, as the motions involved can be significant and occur over long time scales. Here we introduce the swarm-enhanced sampling molecular dynamics (sesMD) method as a tool to improve sampling of conformational landscapes. In this approach, a swarm of replica simulations interact cooperatively via a set of pairwise potentials incorporating attractive and repulsive components. We apply the sesMD approach to explore the conformations of the DFG motif in the protein p38α mitogen-activated protein kinase. In contrast to multiple MD simulations, sesMD trajectories sample a range of DFG conformations, some of which map onto existing crystal structures. Simulated structures intermediate between the DFG-in and DFG-out conformations are predicted to have druggable pockets of interest for structure-based ligand design.

Parameters Identification for Photovoltaic Module Based on an Improved Artificial Fish Swarm Algorithm

PubMed Central

Wang, Hong-Hua

2014-01-01

A precise mathematical model plays a pivotal role in the simulation, evaluation, and optimization of photovoltaic (PV) power systems. Different from the traditional linear model, the model of PV module has the features of nonlinearity and multiparameters. Since conventional methods are incapable of identifying the parameters of PV module, an excellent optimization algorithm is required. Artificial fish swarm algorithm (AFSA), originally inspired by the simulation of collective behavior of real fish swarms, is proposed to fast and accurately extract the parameters of PV module. In addition to the regular operation, a mutation operator (MO) is designed to enhance the searching performance of the algorithm. The feasibility of the proposed method is demonstrated by various parameters of PV module under different environmental conditions, and the testing results are compared with other studied methods in terms of final solutions and computational time. The simulation results show that the proposed method is capable of obtaining higher parameters identification precision. PMID:25243233

Realization of the FPGA-based reconfigurable computing environment by the example of morphological processing of a grayscale image

NASA Astrophysics Data System (ADS)

Shatravin, V.; Shashev, D. V.

2018-05-01

Currently, robots are increasingly being used in every industry. One of the most high-tech areas is creation of completely autonomous robotic devices including vehicles. The results of various global research prove the efficiency of vision systems in autonomous robotic devices. However, the use of these systems is limited because of the computational and energy resources available in the robot device. The paper describes the results of applying the original approach for image processing on reconfigurable computing environments by the example of morphological operations over grayscale images. This approach is prospective for realizing complex image processing algorithms and real-time image analysis in autonomous robotic devices.

Evolution of Collective Behaviour in an Artificial World Using Linguistic Fuzzy Rule-Based Systems

PubMed Central

Lebar Bajec, Iztok

2017-01-01

Collective behaviour is a fascinating and easily observable phenomenon, attractive to a wide range of researchers. In biology, computational models have been extensively used to investigate various properties of collective behaviour, such as: transfer of information across the group, benefits of grouping (defence against predation, foraging), group decision-making process, and group behaviour types. The question ‘why,’ however remains largely unanswered. Here the interest goes into which pressures led to the evolution of such behaviour, and evolutionary computational models have already been used to test various biological hypotheses. Most of these models use genetic algorithms to tune the parameters of previously presented non-evolutionary models, but very few attempt to evolve collective behaviour from scratch. Of these last, the successful attempts display clumping or swarming behaviour. Empirical evidence suggests that in fish schools there exist three classes of behaviour; swarming, milling and polarized. In this paper we present a novel, artificial life-like evolutionary model, where individual agents are governed by linguistic fuzzy rule-based systems, which is capable of evolving all three classes of behaviour. PMID:28045964

Evolution of Collective Behaviour in an Artificial World Using Linguistic Fuzzy Rule-Based Systems.

PubMed

Demšar, Jure; Lebar Bajec, Iztok

2017-01-01

Collective behaviour is a fascinating and easily observable phenomenon, attractive to a wide range of researchers. In biology, computational models have been extensively used to investigate various properties of collective behaviour, such as: transfer of information across the group, benefits of grouping (defence against predation, foraging), group decision-making process, and group behaviour types. The question 'why,' however remains largely unanswered. Here the interest goes into which pressures led to the evolution of such behaviour, and evolutionary computational models have already been used to test various biological hypotheses. Most of these models use genetic algorithms to tune the parameters of previously presented non-evolutionary models, but very few attempt to evolve collective behaviour from scratch. Of these last, the successful attempts display clumping or swarming behaviour. Empirical evidence suggests that in fish schools there exist three classes of behaviour; swarming, milling and polarized. In this paper we present a novel, artificial life-like evolutionary model, where individual agents are governed by linguistic fuzzy rule-based systems, which is capable of evolving all three classes of behaviour.

Free Energy Calculations using a Swarm-Enhanced Sampling Molecular Dynamics Approach.

PubMed

Burusco, Kepa K; Bruce, Neil J; Alibay, Irfan; Bryce, Richard A

2015-10-26

Free energy simulations are an established computational tool in modelling chemical change in the condensed phase. However, sampling of kinetically distinct substates remains a challenge to these approaches. As a route to addressing this, we link the methods of thermodynamic integration (TI) and swarm-enhanced sampling molecular dynamics (sesMD), where simulation replicas interact cooperatively to aid transitions over energy barriers. We illustrate the approach by using alchemical alkane transformations in solution, comparing them with the multiple independent trajectory TI (IT-TI) method. Free energy changes for transitions computed by using IT-TI grew increasingly inaccurate as the intramolecular barrier was heightened. By contrast, swarm-enhanced sampling TI (sesTI) calculations showed clear improvements in sampling efficiency, leading to more accurate computed free energy differences, even in the case of the highest barrier height. The sesTI approach, therefore, has potential in addressing chemical change in systems where conformations exist in slow exchange. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Distributed UAV-Swarm Real-Time Geomatic Data Collection Under Dynamically Changing Resolution Requirements

NASA Astrophysics Data System (ADS)

Almeida, Miguel; Hildmann, Hanno; Solmaz, Gürkan

2017-08-01

Unmanned Aerial Vehicles (UAVs) have been used for reconnaissance and surveillance missions as far back as the Vietnam War, but with the recent rapid increase in autonomy, precision and performance capabilities - and due to the massive reduction in cost and size - UAVs have become pervasive products, available and affordable for the general public. The use cases for UAVs are in the areas of disaster recovery, environmental mapping & protection and increasingly also as extended eyes and ears of civil security forces such as fire-fighters and emergency response units. In this paper we present a swarm algorithm that enables a fleet of autonomous UAVs to collectively perform sensing tasks related to environmental and rescue operations and to dynamically adapt to e.g. changing resolution requirements. We discuss the hardware used to build our own drones and the settings under which we validate the proposed approach.

Application of Particle Swarm Optimization in Computer Aided Setup Planning

NASA Astrophysics Data System (ADS)

Kafashi, Sajad; Shakeri, Mohsen; Abedini, Vahid

2011-01-01

New researches are trying to integrate computer aided design (CAD) and computer aided manufacturing (CAM) environments. The role of process planning is to convert the design specification into manufacturing instructions. Setup planning has a basic role in computer aided process planning (CAPP) and significantly affects the overall cost and quality of machined part. This research focuses on the development for automatic generation of setups and finding the best setup plan in feasible condition. In order to computerize the setup planning process, three major steps are performed in the proposed system: a) Extraction of machining data of the part. b) Analyzing and generation of all possible setups c) Optimization to reach the best setup plan based on cost functions. Considering workshop resources such as machine tool, cutter and fixture, all feasible setups could be generated. Then the problem is adopted with technological constraints such as TAD (tool approach direction), tolerance relationship and feature precedence relationship to have a completely real and practical approach. The optimal setup plan is the result of applying the PSO (particle swarm optimization) algorithm into the system using cost functions. A real sample part is illustrated to demonstrate the performance and productivity of the system.

A Secure Group Communication Architecture for a Swarm of Autonomous Unmanned Aerial Vehicles

DTIC Science & Technology

2008-03-01

members to use the same decryption key. This shared decryption key is called the Session Encryption Key ( SEK ) or Traffic Encryption Key (TEK...Since everyone shares the SEK , members need to hold additional Key Encryption Keys (KEK) that are used to securely distribute the SEK to each valid...managing this process. To preserve the secrecy of the multicast data, the SEK needs to be updated upon certain events such as a member joining and

Seismicity And Accretion Processes Along The Mid-Atlantic Ridge south of the Azores using data from the MARCHE Autonomous Hydrophone Array

NASA Astrophysics Data System (ADS)

Perrot, Julie; Cevatoglu, Melis; Cannat, Mathilde; Escartin, Javier; Maia, Marcia; Tisseau, Chantal; Dziak, Robert; Goslin, Jean

2013-04-01

The seismicity of the South Atlantic Ocean has been recorded by the MARCHE network of 4 autonomous underwater hydrophones (AUH) moored within the SOFAR channel on the flanks of the Mid-Atlantic Ridge (MAR). The instruments were deployed south of the Azores Plateau between 32° and 39°N from July 2005 to August 2008. The low attenuation properties of the SOFAR channel for earthquake T-wave propagation result in a detection threshold reduction from a magnitude completeness level (Mc) of ~4.3 for MAR events recorded by the land-based seismic networks to Mc=2.1 using this hydrophone array. A spatio-temporal analysis has been performed among the 5600 events recorded inside the MARCHE array. Most events are distributed along the ridge between lat. 39°N on the Azores Platform and the Rainbow (36°N) segment. In the hydrophone catalogue, acoustic magnitude (Source Level, SL) is used as a measure of earthquake size. The source level above which the data set is complete is SLc=205 dB. We look for seismic swarms using the cluster software of the SEISAN package. The criterion used are a minimum SL of 210 to detect a possible mainshock, and a radius of 30 km and a time window of 40 days after this mainshock (Cevatoglu, 2010, Goslin et al., 2012). 7 swarms with more than 15 events are identified using this approach between 32°et 39°N of latitude. The maximum number of earthquake in a swarm is 57 events. This result differs from the study of Simao et al. (2010) as we processed a further year of data and selected sequences with fewer events. Looking at the distribution of the SL as a function of time after the mainshock, we discuss the possible mechanism of these earthquakes : tectonic events with a "mainshock-aftershock" distribution fitting a modified Omori law or volcanic events showing more constant SL values. We also present the geophysical setting of these 7 swarms, using gravity, bathymetry, and available local geological data. This study illustrates the potential of hydrophone data to monitor segment-scale ridges processes in the vicinity of the Lucky Strike seafloor observatory (lat. 37°20'N), the Azores node of the EMSO (European Multidiciplinary Subsea Observatory) system. - M. Cevatoglu, Analyse de la sismicité médio-atlantique et son apport dans la compréhension des processus d'accrétion : Expérience MARCHE3, master's thesis , IUEM, University of Brest, 52 p., june 2010. - J. Goslin, J. Perrot, J.-Y. Royer, C. Martin, N. Lourenco, J. Luis, R. P. Dziak, H. Matsumoto, J. Haxel, M. J. Fowler, C. G. Fox, A. T.-K. Lau and S.Bazin. Spatiotemporal distribution of the seismicity along the Mid-Atlantic Ridge north of the Azores from hydroacoustic data: Insights into seismogenic processes in a ridge-hot spot context, 2012, Geochem. Geophys. Geosyst., 13, Q02010, doi:10.1029/2011GC003828. - N. Simao and J. Escartin and J. Goslin and J. Haxel and M. Cannat and R. Dziak, Regional Seismicity of the Mid-Atlantic Ridge: Observations from autonomous hydrophone arrays, 2010, Geophys. J. Int., 183, 1559-1578.

Swarm v2: highly-scalable and high-resolution amplicon clustering.

PubMed

Mahé, Frédéric; Rognes, Torbjørn; Quince, Christopher; de Vargas, Colomban; Dunthorn, Micah

2015-01-01

Previously we presented Swarm v1, a novel and open source amplicon clustering program that produced fine-scale molecular operational taxonomic units (OTUs), free of arbitrary global clustering thresholds and input-order dependency. Swarm v1 worked with an initial phase that used iterative single-linkage with a local clustering threshold (d), followed by a phase that used the internal abundance structures of clusters to break chained OTUs. Here we present Swarm v2, which has two important novel features: (1) a new algorithm for d = 1 that allows the computation time of the program to scale linearly with increasing amounts of data; and (2) the new fastidious option that reduces under-grouping by grafting low abundant OTUs (e.g., singletons and doubletons) onto larger ones. Swarm v2 also directly integrates the clustering and breaking phases, dereplicates sequencing reads with d = 0, outputs OTU representatives in fasta format, and plots individual OTUs as two-dimensional networks.

Biologically-inspired approaches for self-organization, adaptation, and collaboration of heterogeneous autonomous systems

NASA Astrophysics Data System (ADS)

Steinberg, Marc

2011-06-01

This paper presents a selective survey of theoretical and experimental progress in the development of biologicallyinspired approaches for complex surveillance and reconnaissance problems with multiple, heterogeneous autonomous systems. The focus is on approaches that may address ISR problems that can quickly become mathematically intractable or otherwise impractical to implement using traditional optimization techniques as the size and complexity of the problem is increased. These problems require dealing with complex spatiotemporal objectives and constraints at a variety of levels from motion planning to task allocation. There is also a need to ensure solutions are reliable and robust to uncertainty and communications limitations. First, the paper will provide a short introduction to the current state of relevant biological research as relates to collective animal behavior. Second, the paper will describe research on largely decentralized, reactive, or swarm approaches that have been inspired by biological phenomena such as schools of fish, flocks of birds, ant colonies, and insect swarms. Next, the paper will discuss approaches towards more complex organizational and cooperative mechanisms in team and coalition behaviors in order to provide mission coverage of large, complex areas. Relevant team behavior may be derived from recent advances in understanding of the social and cooperative behaviors used for collaboration by tens of animals with higher-level cognitive abilities such as mammals and birds. Finally, the paper will briefly discuss challenges involved in user interaction with these types of systems.

Apoptosis and Self-Destruct: A Contribution to Autonomic Agents?

NASA Technical Reports Server (NTRS)

Sterritt, Roy; Hinchey, Mike

2004-01-01

Autonomic Computing (AC), a self-managing systems initiative based on the biological metaphor of the autonomic nervous system, is increasingly gaining momentum as the way forward in designing reliable systems. Agent technologies have been identified as a key enabler for engineering autonomicity in systems, both in terms of retrofitting autonomicity into legacy systems and designing new systems. The AC initiative provides an opportunity to consider other biological systems and principles in seeking new design strategies. This paper reports on one such investigation; utilizing the apoptosis metaphor of biological systems to provide a dynamic health indicator signal between autonomic agents.

A Chaotic Particle Swarm Optimization-Based Heuristic for Market-Oriented Task-Level Scheduling in Cloud Workflow Systems.

PubMed

Li, Xuejun; Xu, Jia; Yang, Yun

2015-01-01

Cloud workflow system is a kind of platform service based on cloud computing. It facilitates the automation of workflow applications. Between cloud workflow system and its counterparts, market-oriented business model is one of the most prominent factors. The optimization of task-level scheduling in cloud workflow system is a hot topic. As the scheduling is a NP problem, Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) have been proposed to optimize the cost. However, they have the characteristic of premature convergence in optimization process and therefore cannot effectively reduce the cost. To solve these problems, Chaotic Particle Swarm Optimization (CPSO) algorithm with chaotic sequence and adaptive inertia weight factor is applied to present the task-level scheduling. Chaotic sequence with high randomness improves the diversity of solutions, and its regularity assures a good global convergence. Adaptive inertia weight factor depends on the estimate value of cost. It makes the scheduling avoid premature convergence by properly balancing between global and local exploration. The experimental simulation shows that the cost obtained by our scheduling is always lower than the other two representative counterparts.

Reduction of the Nonlinear Phase Shift Induced by Stimulated Brillouin Scattering for Bi-Directional Pumping Configuration System Using Particle Swarm Optimization Algorithm

NASA Astrophysics Data System (ADS)

Al-Asadi, H. A.

2013-02-01

We present a theoretical analysis of an additional nonlinear phase shift of backward Stokes wave based on stimulated Brillouin scattering in the system with a bi-directional pumping scheme. We optimize three parameters of the system: the numerical aperture, the optical loss and the pumping wavelength to minimize an additional nonlinear phase shift of backward Stokes waves due to stimulated Brillouin scattering. The optimization is performed with various Brillouin pump powers and the optical reflectivity values are based on the modern, global evolutionary computation algorithm, particle swarm optimization. It is shown that the additional nonlinear phase shift of backward Stokes wave varies with different optical fiber lengths, and can be minimized to less than 0.07 rad according to the particle swarm optimization algorithm for 5 km. The bi-directional pumping configuration system is shown to be efficient when it is possible to transmit the power output to advanced when frequency detuning is negative and delayed when it is positive, with the optimum values of the three parameters to achieve the reduction of an additional nonlinear phase shift.

Interest rate next-day variation prediction based on hybrid feedforward neural network, particle swarm optimization, and multiresolution techniques

NASA Astrophysics Data System (ADS)

Lahmiri, Salim

2016-02-01

Multiresolution analysis techniques including continuous wavelet transform, empirical mode decomposition, and variational mode decomposition are tested in the context of interest rate next-day variation prediction. In particular, multiresolution analysis techniques are used to decompose interest rate actual variation and feedforward neural network for training and prediction. Particle swarm optimization technique is adopted to optimize its initial weights. For comparison purpose, autoregressive moving average model, random walk process and the naive model are used as main reference models. In order to show the feasibility of the presented hybrid models that combine multiresolution analysis techniques and feedforward neural network optimized by particle swarm optimization, we used a set of six illustrative interest rates; including Moody's seasoned Aaa corporate bond yield, Moody's seasoned Baa corporate bond yield, 3-Month, 6-Month and 1-Year treasury bills, and effective federal fund rate. The forecasting results show that all multiresolution-based prediction systems outperform the conventional reference models on the criteria of mean absolute error, mean absolute deviation, and root mean-squared error. Therefore, it is advantageous to adopt hybrid multiresolution techniques and soft computing models to forecast interest rate daily variations as they provide good forecasting performance.

A Chaotic Particle Swarm Optimization-Based Heuristic for Market-Oriented Task-Level Scheduling in Cloud Workflow Systems

PubMed Central

Li, Xuejun; Xu, Jia; Yang, Yun

2015-01-01

Cloud workflow system is a kind of platform service based on cloud computing. It facilitates the automation of workflow applications. Between cloud workflow system and its counterparts, market-oriented business model is one of the most prominent factors. The optimization of task-level scheduling in cloud workflow system is a hot topic. As the scheduling is a NP problem, Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) have been proposed to optimize the cost. However, they have the characteristic of premature convergence in optimization process and therefore cannot effectively reduce the cost. To solve these problems, Chaotic Particle Swarm Optimization (CPSO) algorithm with chaotic sequence and adaptive inertia weight factor is applied to present the task-level scheduling. Chaotic sequence with high randomness improves the diversity of solutions, and its regularity assures a good global convergence. Adaptive inertia weight factor depends on the estimate value of cost. It makes the scheduling avoid premature convergence by properly balancing between global and local exploration. The experimental simulation shows that the cost obtained by our scheduling is always lower than the other two representative counterparts. PMID:26357510

From Here to Autonomicity: Self-Managing Agents and the Biological Metaphors that Inspire Them

NASA Technical Reports Server (NTRS)

Sterritt, Roy; Hinchey, Mike

2005-01-01

We seek inspiration for self-managing systems from (obviously, pre-existing) biological mechanisms. Autonomic Computing (AC), a self-managing systems initiative based on the biological metaphor of the autonomic nervous system, is increasingly gaining momentum as the way forward for integrating and designing reliable systems, while agent technologies have been identified as a key enabler for engineering autonomicity in systems. This paper looks at other biological metaphors such as reflex and healing, heart- beat monitors, pulse monitors and apoptosis for assisting in the realization of autonomicity.

An Algorithm for Autonomous Formation Obstacle Avoidance

NASA Astrophysics Data System (ADS)

Cruz, Yunior I.

The level of human interaction with Unmanned Aerial Systems varies greatly from remotely piloted aircraft to fully autonomous systems. In the latter end of the spectrum, the challenge lies in designing effective algorithms to dictate the behavior of the autonomous agents. A swarm of autonomous Unmanned Aerial Vehicles requires collision avoidance and formation flight algorithms to negotiate environmental challenges it may encounter during the execution of its mission, which may include obstacles and chokepoints. In this work, a simple algorithm is developed to allow a formation of autonomous vehicles to perform point to point navigation while avoiding obstacles and navigating through chokepoints. Emphasis is placed on maintaining formation structures. Rather than breaking formation and individually navigating around the obstacle or through the chokepoint, vehicles are required to assemble into appropriately sized/shaped sub-formations, bifurcate around the obstacle or negotiate the chokepoint, and reassemble into the original formation at the far side of the obstruction. The algorithm receives vehicle and environmental properties as inputs and outputs trajectories for each vehicle from start to the desired ending location. Simulation results show that the algorithm safely routes all vehicles past the obstruction while adhering to the aforementioned requirements. The formation adapts and successfully negotiates the obstacles and chokepoints in its path while maintaining proper vehicle separation.

Merged Vision and GPS Control of a Semi-Autonomous, Small Helicopter

NASA Technical Reports Server (NTRS)

Rock, Stephen M.

1999-01-01

This final report documents the activities performed during the research period from April 1, 1996 to September 30, 1997. It contains three papers: Carrier Phase GPS and Computer Vision for Control of an Autonomous Helicopter; A Contestant in the 1997 International Aerospace Robotics Laboratory Stanford University; and Combined CDGPS and Vision-Based Control of a Small Autonomous Helicopter.

Self Organized Sorting in Swarms

NASA Astrophysics Data System (ADS)

Copenhagen, Katherine; Quint, David; Gopinathan, Ajay

2014-03-01

Swarming behavior extends across multiple length scales in biology ranging from bacteria to whales. Natural swarms are affected by erratic, or dissenting behavior by individuals within the swarm who may display different types of behaviors than the rest of the swarm. This research investigates the introduction of heterogenous behavior amongst individuals within a swarm and their impact on swarm formation and robustness. We model swarms with a finite number of agents utilizing a velocity alignment interaction and a Lennard-Jones potential, which provides both cohesive and repulsive interactions between neighboring agents. Depending on the parameters governing the swarming interactions and the level of heterogeneity in behavior introduced, we found a variety of collective behavior including sharp transitions from swarming to non-swarming regimes and self organized sorting of individuals based on their types of behavior. Our research sheds light on the varied responses of swarms to internal dissent and suggests optimal strategies to tolerate errant individuals.

PSO Algorithm Particle Filters for Improving the Performance of Lane Detection and Tracking Systems in Difficult Roads

PubMed Central

Cheng, Wen-Chang

2012-01-01

In this paper we propose a robust lane detection and tracking method by combining particle filters with the particle swarm optimization method. This method mainly uses the particle filters to detect and track the local optimum of the lane model in the input image and then seeks the global optimal solution of the lane model by a particle swarm optimization method. The particle filter can effectively complete lane detection and tracking in complicated or variable lane environments. However, the result obtained is usually a local optimal system status rather than the global optimal system status. Thus, the particle swarm optimization method is used to further refine the global optimal system status in all system statuses. Since the particle swarm optimization method is a global optimization algorithm based on iterative computing, it can find the global optimal lane model by simulating the food finding way of fish school or insects under the mutual cooperation of all particles. In verification testing, the test environments included highways and ordinary roads as well as straight and curved lanes, uphill and downhill lanes, lane changes, etc. Our proposed method can complete the lane detection and tracking more accurately and effectively then existing options. PMID:23235453

A trunk ranging system based on binocular stereo vision

NASA Astrophysics Data System (ADS)

Zhao, Xixuan; Kan, Jiangming

2017-07-01

Trunk ranging is an essential function for autonomous forestry robots. Traditional trunk ranging systems based on personal computers are not convenient in practical application. This paper examines the implementation of a trunk ranging system based on the binocular vision theory via TI's DaVinc DM37x system. The system is smaller and more reliable than that implemented using a personal computer. It calculates the three-dimensional information from the images acquired by binocular cameras, producing the targeting and ranging results. The experimental results show that the measurement error is small and the system design is feasible for autonomous forestry robots.

Physics-based approach to chemical source localization using mobile robotic swarms

NASA Astrophysics Data System (ADS)

Zarzhitsky, Dimitri

2008-07-01

Recently, distributed computation has assumed a dominant role in the fields of artificial intelligence and robotics. To improve system performance, engineers are combining multiple cooperating robots into cohesive collectives called swarms. This thesis illustrates the application of basic principles of physicomimetics, or physics-based design, to swarm robotic systems. Such principles include decentralized control, short-range sensing and low power consumption. We show how the application of these principles to robotic swarms results in highly scalable, robust, and adaptive multi-robot systems. The emergence of these valuable properties can be predicted with the help of well-developed theoretical methods. In this research effort, we have designed and constructed a distributed physicomimetics system for locating sources of airborne chemical plumes. This task, called chemical plume tracing (CPT), is receiving a great deal of attention due to persistent homeland security threats. For this thesis, we have created a novel CPT algorithm called fluxotaxis that is based on theoretical principles of fluid dynamics. Analytically, we show that fluxotaxis combines the essence, as well as the strengths, of the two most popular biologically-inspired CPT methods-- chemotaxis and anemotaxis. The chemotaxis strategy consists of navigating in the direction of the chemical density gradient within the plume, while the anemotaxis approach is based on an upwind traversal of the chemical cloud. Rigorous and extensive experimental evaluations have been performed in simulated chemical plume environments. Using a suite of performance metrics that capture the salient aspects of swarm-specific behavior, we have been able to evaluate and compare the three CPT algorithms. We demonstrate the improved performance of our fluxotaxis approach over both chemotaxis and anemotaxis in these realistic simulation environments, which include obstacles. To test our understanding of CPT on actual hardware, we have implemented chemotaxis on three laboratory-scale robots. Chemotaxis requires only chemical sensors; eventually, when small-scale anemometers capable of reliably detecting low air velocities become available, we plan to implement anemotaxis and fluxotaxis on the robots as well. Our chemotaxis robots use the physicomimetics control algorithm to arrange the team of vehicles into a triangular formation, which then traces an ethanol vapor plume to its source emitter. In agreement with our theoretical predictions, the swarm implementation shows a consistent gain in CPT performance as compared to a single-robot solution.

GeoTrack: bio-inspired global video tracking by networks of unmanned aircraft systems

NASA Astrophysics Data System (ADS)

Barooah, Prabir; Collins, Gaemus E.; Hespanha, João P.

2009-05-01

Research from the Institute for Collaborative Biotechnologies (ICB) at the University of California at Santa Barbara (UCSB) has identified swarming algorithms used by flocks of birds and schools of fish that enable these animals to move in tight formation and cooperatively track prey with minimal estimation errors, while relying solely on local communication between the animals. This paper describes ongoing work by UCSB, the University of Florida (UF), and the Toyon Research Corporation on the utilization of these algorithms to dramatically improve the capabilities of small unmanned aircraft systems (UAS) to cooperatively locate and track ground targets. Our goal is to construct an electronic system, called GeoTrack, through which a network of hand-launched UAS use dedicated on-board processors to perform multi-sensor data fusion. The nominal sensors employed by the system will EO/IR video cameras on the UAS. When GMTI or other wide-area sensors are available, as in a layered sensing architecture, data from the standoff sensors will also be fused into the GeoTrack system. The output of the system will be position and orientation information on stationary or mobile targets in a global geo-stationary coordinate system. The design of the GeoTrack system requires significant advances beyond the current state-of-the-art in distributed control for a swarm of UAS to accomplish autonomous coordinated tracking; target geo-location using distributed sensor fusion by a network of UAS, communicating over an unreliable channel; and unsupervised real-time image-plane video tracking in low-powered computing platforms.

Analysis of image thresholding segmentation algorithms based on swarm intelligence

NASA Astrophysics Data System (ADS)

Zhang, Yi; Lu, Kai; Gao, Yinghui; Yang, Bo

2013-03-01

Swarm intelligence-based image thresholding segmentation algorithms are playing an important role in the research field of image segmentation. In this paper, we briefly introduce the theories of four existing image segmentation algorithms based on swarm intelligence including fish swarm algorithm, artificial bee colony, bacteria foraging algorithm and particle swarm optimization. Then some image benchmarks are tested in order to show the differences of the segmentation accuracy, time consumption, convergence and robustness for Salt & Pepper noise and Gaussian noise of these four algorithms. Through these comparisons, this paper gives qualitative analyses for the performance variance of the four algorithms. The conclusions in this paper would give a significant guide for the actual image segmentation.

Distributed Control of a Swarm of Autonomous Unmanned Aerial Vehicles

DTIC Science & Technology

2003-03-01

wisdom, and love have provided a firm anchor in rough times, and a light in the darkness . “Come to me, all you who are weary and burdened, and I will...time. The light-gray trails represent the area that has been covered in the past 50 timesteps. The dark -gray areas are overlapping areas calculated...during the current timestep. The dark line encloses the total contigu- ous sensor area for this example. Note that while agent 1’s footprint does not

Evolution of Control Programs for a Swarm of Autonomous Unmanned Aerial Vehicles

DTIC Science & Technology

2004-03-01

Game of Checkers,” IBM Journal of Research and Development , 3 (3):210–229 (July 1959). 91. Serway , R. A. Physics for Scientists and Engineers (Fourth...incomplete physics model is used for this research. Ignoring mass and the effects of gravity and friction greatly simplifies the model. At the same...of work on GAs [9, 60, 53]. The seminal work in GAs is the 1975 book Adaptation in Natural and Artificial Systems by John H. Holland [41]. In a GA

Research on an autonomous vision-guided helicopter

NASA Technical Reports Server (NTRS)

Amidi, Omead; Mesaki, Yuji; Kanade, Takeo

1994-01-01

Integration of computer vision with on-board sensors to autonomously fly helicopters was researched. The key components developed were custom designed vision processing hardware and an indoor testbed. The custom designed hardware provided flexible integration of on-board sensors with real-time image processing resulting in a significant improvement in vision-based state estimation. The indoor testbed provided convenient calibrated experimentation in constructing real autonomous systems.

A minimal model of predator–swarm interactions

PubMed Central

Chen, Yuxin; Kolokolnikov, Theodore

2014-01-01

We propose a minimal model of predator–swarm interactions which captures many of the essential dynamics observed in nature. Different outcomes are observed depending on the predator strength. For a ‘weak’ predator, the swarm is able to escape the predator completely. As the strength is increased, the predator is able to catch up with the swarm as a whole, but the individual prey is able to escape by ‘confusing’ the predator: the prey forms a ring with the predator at the centre. For higher predator strength, complex chasing dynamics are observed which can become chaotic. For even higher strength, the predator is able to successfully capture the prey. Our model is simple enough to be amenable to a full mathematical analysis, which is used to predict the shape of the swarm as well as the resulting predator–prey dynamics as a function of model parameters. We show that, as the predator strength is increased, there is a transition (owing to a Hopf bifurcation) from confusion state to chasing dynamics, and we compute the threshold analytically. Our analysis indicates that the swarming behaviour is not helpful in avoiding the predator, suggesting that there are other reasons why the species may swarm. The complex shape of the swarm in our model during the chasing dynamics is similar to the shape of a flock of sheep avoiding a shepherd. PMID:24598204

A minimal model of predator-swarm interactions.

PubMed

Chen, Yuxin; Kolokolnikov, Theodore

2014-05-06

We propose a minimal model of predator-swarm interactions which captures many of the essential dynamics observed in nature. Different outcomes are observed depending on the predator strength. For a 'weak' predator, the swarm is able to escape the predator completely. As the strength is increased, the predator is able to catch up with the swarm as a whole, but the individual prey is able to escape by 'confusing' the predator: the prey forms a ring with the predator at the centre. For higher predator strength, complex chasing dynamics are observed which can become chaotic. For even higher strength, the predator is able to successfully capture the prey. Our model is simple enough to be amenable to a full mathematical analysis, which is used to predict the shape of the swarm as well as the resulting predator-prey dynamics as a function of model parameters. We show that, as the predator strength is increased, there is a transition (owing to a Hopf bifurcation) from confusion state to chasing dynamics, and we compute the threshold analytically. Our analysis indicates that the swarming behaviour is not helpful in avoiding the predator, suggesting that there are other reasons why the species may swarm. The complex shape of the swarm in our model during the chasing dynamics is similar to the shape of a flock of sheep avoiding a shepherd.

Agent-Based Models in Social Physics

NASA Astrophysics Data System (ADS)

Quang, Le Anh; Jung, Nam; Cho, Eun Sung; Choi, Jae Han; Lee, Jae Woo

2018-06-01

We review the agent-based models (ABM) on social physics including econophysics. The ABM consists of agent, system space, and external environment. The agent is autonomous and decides his/her behavior by interacting with the neighbors or the external environment with the rules of behavior. Agents are irrational because they have only limited information when they make decisions. They adapt using learning from past memories. Agents have various attributes and are heterogeneous. ABM is a non-equilibrium complex system that exhibits various emergence phenomena. The social complexity ABM describes human behavioral characteristics. In ABMs of econophysics, we introduce the Sugarscape model and the artificial market models. We review minority games and majority games in ABMs of game theory. Social flow ABM introduces crowding, evacuation, traffic congestion, and pedestrian dynamics. We also review ABM for opinion dynamics and voter model. We discuss features and advantages and disadvantages of Netlogo, Repast, Swarm, and Mason, which are representative platforms for implementing ABM.

Chaotic particle swarm optimization with mutation for classification.

PubMed

Assarzadeh, Zahra; Naghsh-Nilchi, Ahmad Reza

2015-01-01

In this paper, a chaotic particle swarm optimization with mutation-based classifier particle swarm optimization is proposed to classify patterns of different classes in the feature space. The introduced mutation operators and chaotic sequences allows us to overcome the problem of early convergence into a local minima associated with particle swarm optimization algorithms. That is, the mutation operator sharpens the convergence and it tunes the best possible solution. Furthermore, to remove the irrelevant data and reduce the dimensionality of medical datasets, a feature selection approach using binary version of the proposed particle swarm optimization is introduced. In order to demonstrate the effectiveness of our proposed classifier, mutation-based classifier particle swarm optimization, it is checked out with three sets of data classifications namely, Wisconsin diagnostic breast cancer, Wisconsin breast cancer and heart-statlog, with different feature vector dimensions. The proposed algorithm is compared with different classifier algorithms including k-nearest neighbor, as a conventional classifier, particle swarm-classifier, genetic algorithm, and Imperialist competitive algorithm-classifier, as more sophisticated ones. The performance of each classifier was evaluated by calculating the accuracy, sensitivity, specificity and Matthews's correlation coefficient. The experimental results show that the mutation-based classifier particle swarm optimization unequivocally performs better than all the compared algorithms.

Acceleration of the Particle Swarm Optimization for Peierls-Nabarro modeling of dislocations in conventional and high-entropy alloys

NASA Astrophysics Data System (ADS)

Pei, Zongrui; Eisenbach, Markus

2017-06-01

Dislocations are among the most important defects in determining the mechanical properties of both conventional alloys and high-entropy alloys. The Peierls-Nabarro model supplies an efficient pathway to their geometries and mobility. The difficulty in solving the integro-differential Peierls-Nabarro equation is how to effectively avoid the local minima in the energy landscape of a dislocation core. Among the other methods to optimize the dislocation core structures, we choose the algorithm of Particle Swarm Optimization, an algorithm that simulates the social behaviors of organisms. By employing more particles (bigger swarm) and more iterative steps (allowing them to explore for longer time), the local minima can be effectively avoided. But this would require more computational cost. The advantage of this algorithm is that it is readily parallelized in modern high computing architecture. We demonstrate the performance of our parallelized algorithm scales linearly with the number of employed cores.

Particle swarm optimization and gravitational wave data analysis: Performance on a binary inspiral testbed

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang Yan; Mohanty, Soumya D.; Center for Gravitational Wave Astronomy, Department of Physics and Astronomy, University of Texas at Brownsville, 80 Fort Brown, Brownsville, Texas 78520

2010-03-15

The detection and estimation of gravitational wave signals belonging to a parameterized family of waveforms requires, in general, the numerical maximization of a data-dependent function of the signal parameters. Because of noise in the data, the function to be maximized is often highly multimodal with numerous local maxima. Searching for the global maximum then becomes computationally expensive, which in turn can limit the scientific scope of the search. Stochastic optimization is one possible approach to reducing computational costs in such applications. We report results from a first investigation of the particle swarm optimization method in this context. The method ismore » applied to a test bed motivated by the problem of detection and estimation of a binary inspiral signal. Our results show that particle swarm optimization works well in the presence of high multimodality, making it a viable candidate method for further applications in gravitational wave data analysis.« less

Comparison of Artificial Immune System and Particle Swarm Optimization Techniques for Error Optimization of Machine Vision Based Tool Movements

NASA Astrophysics Data System (ADS)

Mahapatra, Prasant Kumar; Sethi, Spardha; Kumar, Amod

2015-10-01

In conventional tool positioning technique, sensors embedded in the motion stages provide the accurate tool position information. In this paper, a machine vision based system and image processing technique for motion measurement of lathe tool from two-dimensional sequential images captured using charge coupled device camera having a resolution of 250 microns has been described. An algorithm was developed to calculate the observed distance travelled by the tool from the captured images. As expected, error was observed in the value of the distance traversed by the tool calculated from these images. Optimization of errors due to machine vision system, calibration, environmental factors, etc. in lathe tool movement was carried out using two soft computing techniques, namely, artificial immune system (AIS) and particle swarm optimization (PSO). The results show better capability of AIS over PSO.

Extending self-organizing particle systems to problem solving.

PubMed

Rodríguez, Alejandro; Reggia, James A

2004-01-01

Self-organizing particle systems consist of numerous autonomous, purely reflexive agents ("particles") whose collective movements through space are determined primarily by local influences they exert upon one another. Inspired by biological phenomena (bird flocking, fish schooling, etc.), particle systems have been used not only for biological modeling, but also increasingly for applications requiring the simulation of collective movements such as computer-generated animation. In this research, we take some first steps in extending particle systems so that they not only move collectively, but also solve simple problems. This is done by giving the individual particles (agents) a rudimentary intelligence in the form of a very limited memory and a top-down, goal-directed control mechanism that, triggered by appropriate conditions, switches them between different behavioral states and thus different movement dynamics. Such enhanced particle systems are shown to be able to function effectively in performing simulated search-and-collect tasks. Further, computational experiments show that collectively moving agent teams are more effective than similar but independently moving ones in carrying out such tasks, and that agent teams of either type that split off members of the collective to protect previously acquired resources are most effective. This work shows that the reflexive agents of contemporary particle systems can readily be extended to support goal-directed problem solving while retaining their collective movement behaviors. These results may prove useful not only for future modeling of animal behavior, but also in computer animation, coordinated movement control in robotic teams, particle swarm optimization, and computer games.

Position-adaptive explosive detection concepts for swarming micro-UAVs

NASA Astrophysics Data System (ADS)

Selmic, Rastko R.; Mitra, Atindra

2008-04-01

We have formulated a series of position-adaptive sensor concepts for explosive detection applications using swarms of micro-UAV's. These concepts are a generalization of position-adaptive radar concepts developed for challenging conditions such as urban environments. For radar applications, this concept is developed with platforms within a UAV swarm that spatially-adapt to signal leakage points on the perimeter of complex clutter environments to collect information on embedded objects-of-interest. The concept is generalized for additional sensors applications by, for example, considering a wooden cart that contains explosives. We can formulate system-of-systems concepts for a swarm of micro-UAV's in an effort to detect whether or not a given cart contains explosives. Under this new concept, some of the members of the UAV swarm can serve as position-adaptive "transmitters" by blowing air over the cart and some of the members of the UAV swarm can serve as position-adaptive "receivers" that are equipped with chem./bio sensors that function as "electronic noses". The final objective can be defined as improving the particle count for the explosives in the air that surrounds a cart via development of intelligent position-adaptive control algorithms in order to improve the detection and false-alarm statistics. We report on recent simulation results with regard to designing optimal sensor placement for explosive or other chemical agent detection. This type of information enables the development of intelligent control algorithms for UAV swarm applications and is intended for the design of future system-of-systems with adaptive intelligence for advanced surveillance of unknown regions. Results are reported as part of a parametric investigation where it is found that the probability of contaminant detection depends on the air flow that carries contaminant particles, geometry of the surrounding space, leakage areas, and other factors. We present a concept of position-adaptive detection (i.e. based on the example in the previous paragraph) consisting of position-adaptive fluid actuators (fans) and position-adaptive sensors. Based on these results, a preliminary analysis of sensor requirements for these fluid actuators and sensors is presented for small-UAVs in a field-enabled explosive detection environment. The computational fluid dynamics (CFD) simulation software Fluent is used to simulate the air flow in the corridor model containing a box with explosive particles. It is found that such flow is turbulent with Reynolds number greater than 106. Simulation methods and results are presented which show particle velocity and concentration distribution throughout the closed box. The results indicate that the CFD-based method can be used for other sensor placement and deployment optimization problems. These techniques and results can be applied towards the development of future system-of-system UAV swarms for defense, homeland defense, and security applications.

The influence of swarm deformation on the velocity behavior of falling swarms of particles

NASA Astrophysics Data System (ADS)

Mitchell, C. A.; Pyrak-Nolte, L. J.; Nitsche, L.

2017-12-01

Cohesive particle swarms have been shown to exhibit enhanced sedimentation in fractures for an optimal range of fracture apertures. Within this range, swarms travel farther and faster than a disperse (particulate) solution. This study aims to uncover the physics underlying the enhanced sedimentation. Swarm behavior at low Reynolds number in a quiescent unbounded fluid and between smooth rigid planar boundaries is investigated numerically using direct-summation, particle-mesh (PM) and particle-particle particle-mesh (P3M) methods - based upon mutually interacting viscous point forces (Stokeslet fields). Wall effects are treated with a least-squares boundary singularity method. Sub-structural effects beyond pseudo-liquid behavior (i.e., particle-scale interactions) are approximated by the P3M method much more efficiently than with direct summation. The model parameters are selected from particle swarm experiments to enable comparison. From the simulations, if the initial swarm geometry at release is unaffected by the fracture aperture, no enhanced transport occurs. The swarm velocity as a function of apertures increases monotonically until it asymptotes to the swarm velocity in an open tank. However, if the fracture aperture affects the initial swarm geometry, the swarm velocity no longer exhibits a monotonic behavior. When swarms are released between two parallel smooth walls with very small apertures, the swarm is forced to reorganize and quickly deform, which results in dramatically reduced swarm velocities. At large apertures, the swarm evolution is similar to that of a swarm in open tank and quickly flattens into a slow speed torus. In the optimal aperture range, the swarm maintains a cohesive unit behaving similarly to a falling sphere. Swarms falling in apertures less than or greater than the optimal aperture range, experience a level of anisotropy that considerably decreases velocities. Unraveling the physics that drives swarm behavior in fractured porous media is important for understanding particle sedimentation and contaminant spreading in the subsurface. Acknowledgment: This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Geosciences Research Program under Award Number (DE-FG02-09ER16022).

Evolution of Autonomous Self-Righting Behaviors for Articulated Nanorovers

NASA Technical Reports Server (NTRS)

Tunstel, Edward

1999-01-01

Miniature rovers with articulated mobility mechanisms are being developed for planetary surface exploration on Mars and small solar system bodies. These vehicles are designed to be capable of autonomous recovery from overturning during surface operations. This paper describes a computational means of developing motion behaviors that achieve the autonomous recovery function. It proposes a control software design approach aimed at reducing the effort involved in developing self-righting behaviors. The approach is based on the integration of evolutionary computing with a dynamics simulation environment for evolving and evaluating motion behaviors. The automated behavior design approach is outlined and its underlying genetic programming infrastructure is described.

Autonomous facial recognition system inspired by human visual system based logarithmical image visualization technique

NASA Astrophysics Data System (ADS)

Wan, Qianwen; Panetta, Karen; Agaian, Sos

2017-05-01

Autonomous facial recognition system is widely used in real-life applications, such as homeland border security, law enforcement identification and authentication, and video-based surveillance analysis. Issues like low image quality, non-uniform illumination as well as variations in poses and facial expressions can impair the performance of recognition systems. To address the non-uniform illumination challenge, we present a novel robust autonomous facial recognition system inspired by the human visual system based, so called, logarithmical image visualization technique. In this paper, the proposed method, for the first time, utilizes the logarithmical image visualization technique coupled with the local binary pattern to perform discriminative feature extraction for facial recognition system. The Yale database, the Yale-B database and the ATT database are used for computer simulation accuracy and efficiency testing. The extensive computer simulation demonstrates the method's efficiency, accuracy, and robustness of illumination invariance for facial recognition.

Particle Swarm Optimization with Double Learning Patterns.

PubMed

Shen, Yuanxia; Wei, Linna; Zeng, Chuanhua; Chen, Jian

2016-01-01

Particle Swarm Optimization (PSO) is an effective tool in solving optimization problems. However, PSO usually suffers from the premature convergence due to the quick losing of the swarm diversity. In this paper, we first analyze the motion behavior of the swarm based on the probability characteristic of learning parameters. Then a PSO with double learning patterns (PSO-DLP) is developed, which employs the master swarm and the slave swarm with different learning patterns to achieve a trade-off between the convergence speed and the swarm diversity. The particles in the master swarm and the slave swarm are encouraged to explore search for keeping the swarm diversity and to learn from the global best particle for refining a promising solution, respectively. When the evolutionary states of two swarms interact, an interaction mechanism is enabled. This mechanism can help the slave swarm in jumping out of the local optima and improve the convergence precision of the master swarm. The proposed PSO-DLP is evaluated on 20 benchmark functions, including rotated multimodal and complex shifted problems. The simulation results and statistical analysis show that PSO-DLP obtains a promising performance and outperforms eight PSO variants.

Swarm v2: highly-scalable and high-resolution amplicon clustering

PubMed Central

Quince, Christopher; de Vargas, Colomban; Dunthorn, Micah

2015-01-01

Previously we presented Swarm v1, a novel and open source amplicon clustering program that produced fine-scale molecular operational taxonomic units (OTUs), free of arbitrary global clustering thresholds and input-order dependency. Swarm v1 worked with an initial phase that used iterative single-linkage with a local clustering threshold (d), followed by a phase that used the internal abundance structures of clusters to break chained OTUs. Here we present Swarm v2, which has two important novel features: (1) a new algorithm for d = 1 that allows the computation time of the program to scale linearly with increasing amounts of data; and (2) the new fastidious option that reduces under-grouping by grafting low abundant OTUs (e.g., singletons and doubletons) onto larger ones. Swarm v2 also directly integrates the clustering and breaking phases, dereplicates sequencing reads with d = 0, outputs OTU representatives in fasta format, and plots individual OTUs as two-dimensional networks. PMID:26713226

Evolutionary Computation for the Identification of Emergent Behavior in Autonomous Systems

NASA Technical Reports Server (NTRS)

Terrile, Richard J.; Guillaume, Alexandre

2009-01-01

Over the past several years the Center for Evolutionary Computation and Automated Design at the Jet Propulsion Laboratory has developed a technique based on Evolutionary Computational Methods (ECM) that allows for the automated optimization of complex computationally modeled systems. An important application of this technique is for the identification of emergent behaviors in autonomous systems. Mobility platforms such as rovers or airborne vehicles are now being designed with autonomous mission controllers that can find trajectories over a solution space that is larger than can reasonably be tested. It is critical to identify control behaviors that are not predicted and can have surprising results (both good and bad). These emergent behaviors need to be identified, characterized and either incorporated into or isolated from the acceptable range of control characteristics. We use cluster analysis of automatically retrieved solutions to identify isolated populations of solutions with divergent behaviors.

Analysis of swarm behaviors based on an inversion of the fluctuation theorem.

PubMed

Hamann, Heiko; Schmickl, Thomas; Crailsheim, Karl

2014-01-01

A grand challenge in the field of artificial life is to find a general theory of emergent self-organizing systems. In swarm systems most of the observed complexity is based on motion of simple entities. Similarly, statistical mechanics focuses on collective properties induced by the motion of many interacting particles. In this article we apply methods from statistical mechanics to swarm systems. We try to explain the emergent behavior of a simulated swarm by applying methods based on the fluctuation theorem. Empirical results indicate that swarms are able to produce negative entropy within an isolated subsystem due to frozen accidents. Individuals of a swarm are able to locally detect fluctuations of the global entropy measure and store them, if they are negative entropy productions. By accumulating these stored fluctuations over time the swarm as a whole is producing negative entropy and the system ends up in an ordered state. We claim that this indicates the existence of an inverted fluctuation theorem for emergent self-organizing dissipative systems. This approach bears the potential of general applicability.

Autonomic Computing for Spacecraft Ground Systems

NASA Technical Reports Server (NTRS)

Li, Zhenping; Savkli, Cetin; Jones, Lori

2007-01-01

Autonomic computing for spacecraft ground systems increases the system reliability and reduces the cost of spacecraft operations and software maintenance. In this paper, we present an autonomic computing solution for spacecraft ground systems at NASA Goddard Space Flight Center (GSFC), which consists of an open standard for a message oriented architecture referred to as the GMSEC architecture (Goddard Mission Services Evolution Center), and an autonomic computing tool, the Criteria Action Table (CAT). This solution has been used in many upgraded ground systems for NASA 's missions, and provides a framework for developing solutions with higher autonomic maturity.

Seismogenic structures activated during the pre-eruptive and intrusive swarms of Piton de la Fournaise volcano (La Réunion island) between 2008 and 2011

NASA Astrophysics Data System (ADS)

Battaglia, J.; Brenguier, F.

2011-12-01

Piton de la Fournaise is a frequently active basaltic volcano with more than 30 fissure eruptions since 1998. These eruptions are always preceded by pre-eruptive swarms of volcano-tectonic earthquakes which accompany dike propagation. Occasionally, intrusion swarms occur without leading to any eruption. From October 2008 to May 2011, as part of the research project Undervolc, a temporary network of 15 broadband stations has been installed on the volcano to complement the local monitoring network. We examined in detail the 6 intrusive and 5 pre-eruptive swarms which occurred during the temporary experiment. All the crises lasted for a few hours and only included shallow events clustered below the summit craters, around and above sea level, showing no signs of deeper magma transfers. These characteristics are common to most swarms observed at Piton de la Fournaise arising questions about the origin of the seismicity which seems to be poorly linked with dike propagation. With the aim to identify the main seismogenic structures active during the swarms, we applied precise earthquake detection and classification techniques based on waveform cross-correlation. For each swarm, the onsets of all transients, including small amplitude ones, have been precisely detected at a single station by scanning the continuous data with reference waveforms. The classification of the detected transients indicates the presence of several families of similar earthquakes. The two main families (F01 and F02) include several hundred events. They are systematically activated at the beginning of each pre-eruptive swarm but are inactive during the intrusive ones. They group more than 50 percent of the detected events for the corresponding crises. The other clusters are mostly associated with single swarms. To determine the spatial characteristics of the structures corresponding to the main families, we applied precise relocation techniques. Based on the one-station classification, the events have first been picked at all available stations by cross-correlating waveforms with those of master events whose arrival times have been manually determined. All events have been located using a 3D velocity model to determine accurate hypocentral azimuths and take-off angles. Precise relative locations have been computed for each multiplet using cross-correlation delays calculated for all available stations between all pairs of events. The results indicate the presence at sea level of a major structure grouping families F01 and F02 and describing an East-West elongated pattern with sub-vertical extension. Small scale earthquake migrations, mostly horizontal, occur during the pre-eruptive swarms along that structure. The smaller multiplets define vertically elongated patterns extending around and above the main F01-F02 multiplet. Our results show that different processes are involved in pre-eruptive and intrusive crises and that a structure located around 2.5 km below the summit controls the occurrence of recent eruptions of Piton de la Fournaise volcano.

Autonomous Navigation for Autonomous Underwater Vehicles Based on Information Filters and Active Sensing

PubMed Central

He, Bo; Zhang, Hongjin; Li, Chao; Zhang, Shujing; Liang, Yan; Yan, Tianhong

2011-01-01

This paper addresses an autonomous navigation method for the autonomous underwater vehicle (AUV) C-Ranger applying information-filter-based simultaneous localization and mapping (SLAM), and its sea trial experiments in Tuandao Bay (Shangdong Province, P.R. China). Weak links in the information matrix in an extended information filter (EIF) can be pruned to achieve an efficient approach-sparse EIF algorithm (SEIF-SLAM). All the basic update formulae can be implemented in constant time irrespective of the size of the map; hence the computational complexity is significantly reduced. The mechanical scanning imaging sonar is chosen as the active sensing device for the underwater vehicle, and a compensation method based on feedback of the AUV pose is presented to overcome distortion of the acoustic images due to the vehicle motion. In order to verify the feasibility of the navigation methods proposed for the C-Ranger, a sea trial was conducted in Tuandao Bay. Experimental results and analysis show that the proposed navigation approach based on SEIF-SLAM improves the accuracy of the navigation compared with conventional method; moreover the algorithm has a low computational cost when compared with EKF-SLAM. PMID:22346682

Autonomous navigation for autonomous underwater vehicles based on information filters and active sensing.

PubMed

He, Bo; Zhang, Hongjin; Li, Chao; Zhang, Shujing; Liang, Yan; Yan, Tianhong

2011-01-01

This paper addresses an autonomous navigation method for the autonomous underwater vehicle (AUV) C-Ranger applying information-filter-based simultaneous localization and mapping (SLAM), and its sea trial experiments in Tuandao Bay (Shangdong Province, P.R. China). Weak links in the information matrix in an extended information filter (EIF) can be pruned to achieve an efficient approach-sparse EIF algorithm (SEIF-SLAM). All the basic update formulae can be implemented in constant time irrespective of the size of the map; hence the computational complexity is significantly reduced. The mechanical scanning imaging sonar is chosen as the active sensing device for the underwater vehicle, and a compensation method based on feedback of the AUV pose is presented to overcome distortion of the acoustic images due to the vehicle motion. In order to verify the feasibility of the navigation methods proposed for the C-Ranger, a sea trial was conducted in Tuandao Bay. Experimental results and analysis show that the proposed navigation approach based on SEIF-SLAM improves the accuracy of the navigation compared with conventional method; moreover the algorithm has a low computational cost when compared with EKF-SLAM.

Evolution of Collective Behaviors for a Real Swarm of Aquatic Surface Robots.

PubMed

Duarte, Miguel; Costa, Vasco; Gomes, Jorge; Rodrigues, Tiago; Silva, Fernando; Oliveira, Sancho Moura; Christensen, Anders Lyhne

2016-01-01

Swarm robotics is a promising approach for the coordination of large numbers of robots. While previous studies have shown that evolutionary robotics techniques can be applied to obtain robust and efficient self-organized behaviors for robot swarms, most studies have been conducted in simulation, and the few that have been conducted on real robots have been confined to laboratory environments. In this paper, we demonstrate for the first time a swarm robotics system with evolved control successfully operating in a real and uncontrolled environment. We evolve neural network-based controllers in simulation for canonical swarm robotics tasks, namely homing, dispersion, clustering, and monitoring. We then assess the performance of the controllers on a real swarm of up to ten aquatic surface robots. Our results show that the evolved controllers transfer successfully to real robots and achieve a performance similar to the performance obtained in simulation. We validate that the evolved controllers display key properties of swarm intelligence-based control, namely scalability, flexibility, and robustness on the real swarm. We conclude with a proof-of-concept experiment in which the swarm performs a complete environmental monitoring task by combining multiple evolved controllers.

Evolution of Collective Behaviors for a Real Swarm of Aquatic Surface Robots

PubMed Central

Duarte, Miguel; Costa, Vasco; Gomes, Jorge; Rodrigues, Tiago; Silva, Fernando; Oliveira, Sancho Moura; Christensen, Anders Lyhne

2016-01-01

Swarm robotics is a promising approach for the coordination of large numbers of robots. While previous studies have shown that evolutionary robotics techniques can be applied to obtain robust and efficient self-organized behaviors for robot swarms, most studies have been conducted in simulation, and the few that have been conducted on real robots have been confined to laboratory environments. In this paper, we demonstrate for the first time a swarm robotics system with evolved control successfully operating in a real and uncontrolled environment. We evolve neural network-based controllers in simulation for canonical swarm robotics tasks, namely homing, dispersion, clustering, and monitoring. We then assess the performance of the controllers on a real swarm of up to ten aquatic surface robots. Our results show that the evolved controllers transfer successfully to real robots and achieve a performance similar to the performance obtained in simulation. We validate that the evolved controllers display key properties of swarm intelligence-based control, namely scalability, flexibility, and robustness on the real swarm. We conclude with a proof-of-concept experiment in which the swarm performs a complete environmental monitoring task by combining multiple evolved controllers. PMID:26999614

Spectral method for a kinetic swarming model

DOE PAGES

Gamba, Irene M.; Haack, Jeffrey R.; Motsch, Sebastien

2015-04-28

Here we present the first numerical method for a kinetic description of the Vicsek swarming model. The kinetic model poses a unique challenge, as there is a distribution dependent collision invariant to satisfy when computing the interaction term. We use a spectral representation linked with a discrete constrained optimization to compute these interactions. To test the numerical scheme we investigate the kinetic model at different scales and compare the solution with the microscopic and macroscopic descriptions of the Vicsek model. Lastly, we observe that the kinetic model captures key features such as vortex formation and traveling waves.

Tissue cell assisted fabrication of tubular catalytic platinum microengines

NASA Astrophysics Data System (ADS)

Wang, Hong; Moo, James Guo Sheng; Pumera, Martin

2014-09-01

We report a facile platform for mass production of robust self-propelled tubular microengines. Tissue cells extracted from fruits of banana and apple, Musa acuminata and Malus domestica, are used as the support on which a thin platinum film is deposited by means of physical vapor deposition. Upon sonication of the cells/Pt-coated substrate in water, microscrolls of highly uniform sizes are spontaneously formed. Tubular microengines fabricated with the fruit cell assisted method exhibit a fast motion of ~100 bodylengths per s (~1 mm s-1). An extremely simple and affordable platform for mass production of the micromotors is crucial for the envisioned swarms of thousands and millions of autonomous micromotors performing biomedical and environmental remediation tasks.We report a facile platform for mass production of robust self-propelled tubular microengines. Tissue cells extracted from fruits of banana and apple, Musa acuminata and Malus domestica, are used as the support on which a thin platinum film is deposited by means of physical vapor deposition. Upon sonication of the cells/Pt-coated substrate in water, microscrolls of highly uniform sizes are spontaneously formed. Tubular microengines fabricated with the fruit cell assisted method exhibit a fast motion of ~100 bodylengths per s (~1 mm s-1). An extremely simple and affordable platform for mass production of the micromotors is crucial for the envisioned swarms of thousands and millions of autonomous micromotors performing biomedical and environmental remediation tasks. Electronic supplementary information (ESI) available: Related video. See DOI: 10.1039/c4nr03720k

Chaotic Particle Swarm Optimization with Mutation for Classification

PubMed Central

Assarzadeh, Zahra; Naghsh-Nilchi, Ahmad Reza

2015-01-01

In this paper, a chaotic particle swarm optimization with mutation-based classifier particle swarm optimization is proposed to classify patterns of different classes in the feature space. The introduced mutation operators and chaotic sequences allows us to overcome the problem of early convergence into a local minima associated with particle swarm optimization algorithms. That is, the mutation operator sharpens the convergence and it tunes the best possible solution. Furthermore, to remove the irrelevant data and reduce the dimensionality of medical datasets, a feature selection approach using binary version of the proposed particle swarm optimization is introduced. In order to demonstrate the effectiveness of our proposed classifier, mutation-based classifier particle swarm optimization, it is checked out with three sets of data classifications namely, Wisconsin diagnostic breast cancer, Wisconsin breast cancer and heart-statlog, with different feature vector dimensions. The proposed algorithm is compared with different classifier algorithms including k-nearest neighbor, as a conventional classifier, particle swarm-classifier, genetic algorithm, and Imperialist competitive algorithm-classifier, as more sophisticated ones. The performance of each classifier was evaluated by calculating the accuracy, sensitivity, specificity and Matthews's correlation coefficient. The experimental results show that the mutation-based classifier particle swarm optimization unequivocally performs better than all the compared algorithms. PMID:25709937

Symbiosis-Based Alternative Learning Multi-Swarm Particle Swarm Optimization.

PubMed

Niu, Ben; Huang, Huali; Tan, Lijing; Duan, Qiqi

2017-01-01

Inspired by the ideas from the mutual cooperation of symbiosis in natural ecosystem, this paper proposes a new variant of PSO, named Symbiosis-based Alternative Learning Multi-swarm Particle Swarm Optimization (SALMPSO). A learning probability to select one exemplar out of the center positions, the local best position, and the historical best position including the experience of internal and external multiple swarms, is used to keep the diversity of the population. Two different levels of social interaction within and between multiple swarms are proposed. In the search process, particles not only exchange social experience with others that are from their own sub-swarms, but also are influenced by the experience of particles from other fellow sub-swarms. According to the different exemplars and learning strategy, this model is instantiated as four variants of SALMPSO and a set of 15 test functions are conducted to compare with some variants of PSO including 10, 30 and 50 dimensions, respectively. Experimental results demonstrate that the alternative learning strategy in each SALMPSO version can exhibit better performance in terms of the convergence speed and optimal values on most multimodal functions in our simulation.

Han's model parameters for microalgae grown under intermittent illumination: Determined using particle swarm optimization.

PubMed

Pozzobon, Victor; Perre, Patrick

2018-01-21

This work provides a model and the associated set of parameters allowing for microalgae population growth computation under intermittent lightning. Han's model is coupled with a simple microalgae growth model to yield a relationship between illumination and population growth. The model parameters were obtained by fitting a dataset available in literature using Particle Swarm Optimization method. In their work, authors grew microalgae in excess of nutrients under flashing conditions. Light/dark cycles used for these experimentations are quite close to those found in photobioreactor, i.e. ranging from several seconds to one minute. In this work, in addition to producing the set of parameters, Particle Swarm Optimization robustness was assessed. To do so, two different swarm initialization techniques were used, i.e. uniform and random distribution throughout the search-space. Both yielded the same results. In addition, swarm distribution analysis reveals that the swarm converges to a unique minimum. Thus, the produced set of parameters can be trustfully used to link light intensity to population growth rate. Furthermore, the set is capable to describe photodamages effects on population growth. Hence, accounting for light overexposure effect on algal growth. Copyright © 2017 Elsevier Ltd. All rights reserved.

Parameter Estimation of Fractional-Order Chaotic Systems by Using Quantum Parallel Particle Swarm Optimization Algorithm

PubMed Central

Huang, Yu; Guo, Feng; Li, Yongling; Liu, Yufeng

2015-01-01

Parameter estimation for fractional-order chaotic systems is an important issue in fractional-order chaotic control and synchronization and could be essentially formulated as a multidimensional optimization problem. A novel algorithm called quantum parallel particle swarm optimization (QPPSO) is proposed to solve the parameter estimation for fractional-order chaotic systems. The parallel characteristic of quantum computing is used in QPPSO. This characteristic increases the calculation of each generation exponentially. The behavior of particles in quantum space is restrained by the quantum evolution equation, which consists of the current rotation angle, individual optimal quantum rotation angle, and global optimal quantum rotation angle. Numerical simulation based on several typical fractional-order systems and comparisons with some typical existing algorithms show the effectiveness and efficiency of the proposed algorithm. PMID:25603158

Do small swarms have an advantage when house hunting? The effect of swarm size on nest-site selection by Apis mellifera.

PubMed

Schaerf, T M; Makinson, J C; Myerscough, M R; Beekman, M

2013-10-06

Reproductive swarms of honeybees are faced with the problem of finding a good site to establish a new colony. We examined the potential effects of swarm size on the quality of nest-site choice through a combination of modelling and field experiments. We used an individual-based model to examine the effects of swarm size on decision accuracy under the assumption that the number of bees actively involved in the decision-making process (scouts) is an increasing function of swarm size. We found that the ability of a swarm to choose the best of two nest sites decreases as swarm size increases when there is some time-lag between discovering the sites, consistent with Janson & Beekman (Janson & Beekman 2007 Proceedings of European Conference on Complex Systems, pp. 204-211.). However, when simulated swarms were faced with a realistic problem of choosing between many nest sites discoverable at all times, larger swarms were more accurate in their decisions than smaller swarms owing to their ability to discover nest sites more rapidly. Our experimental fieldwork showed that large swarms invest a larger number of scouts into the decision-making process than smaller swarms. Preliminary analysis of waggle dances from experimental swarms also suggested that large swarms could indeed discover and advertise nest sites at a faster rate than small swarms.

Do small swarms have an advantage when house hunting? The effect of swarm size on nest-site selection by Apis mellifera

PubMed Central

Schaerf, T. M.; Makinson, J. C.; Myerscough, M. R.; Beekman, M.

2013-01-01

Reproductive swarms of honeybees are faced with the problem of finding a good site to establish a new colony. We examined the potential effects of swarm size on the quality of nest-site choice through a combination of modelling and field experiments. We used an individual-based model to examine the effects of swarm size on decision accuracy under the assumption that the number of bees actively involved in the decision-making process (scouts) is an increasing function of swarm size. We found that the ability of a swarm to choose the best of two nest sites decreases as swarm size increases when there is some time-lag between discovering the sites, consistent with Janson & Beekman (Janson & Beekman 2007 Proceedings of European Conference on Complex Systems, pp. 204–211.). However, when simulated swarms were faced with a realistic problem of choosing between many nest sites discoverable at all times, larger swarms were more accurate in their decisions than smaller swarms owing to their ability to discover nest sites more rapidly. Our experimental fieldwork showed that large swarms invest a larger number of scouts into the decision-making process than smaller swarms. Preliminary analysis of waggle dances from experimental swarms also suggested that large swarms could indeed discover and advertise nest sites at a faster rate than small swarms. PMID:23904590

Swarmie User Manual: A Rover Used for Multi-agent Swarm Research

NASA Technical Reports Server (NTRS)

Montague, Gilbert

2014-01-01

The ability to create multiple functional yet cost effective robots is crucial for conducting swarming robotics research. The Center Innovation Fund (CIF) swarming robotics project is a collaboration among the KSC Granular Mechanics and Regolith Operations (GMRO) group, the University of New Mexico Biological Computation Lab, and the NASA Ames Intelligent Robotics Group (IRG) that uses rovers, dubbed "Swarmies", as test platforms for genetic search algorithms. This fall, I assisted in the development of the software modules used on the Swarmies and created this guide to provide thorough instructions on how to configure your workspace to operate a Swarmie both in simulation and out in the field.

Vision Based Autonomous Robotic Control for Advanced Inspection and Repair

NASA Technical Reports Server (NTRS)

Wehner, Walter S.

2014-01-01

The advanced inspection system is an autonomous control and analysis system that improves the inspection and remediation operations for ground and surface systems. It uses optical imaging technology with intelligent computer vision algorithms to analyze physical features of the real-world environment to make decisions and learn from experience. The advanced inspection system plans to control a robotic manipulator arm, an unmanned ground vehicle and cameras remotely, automatically and autonomously. There are many computer vision, image processing and machine learning techniques available as open source for using vision as a sensory feedback in decision-making and autonomous robotic movement. My responsibilities for the advanced inspection system are to create a software architecture that integrates and provides a framework for all the different subsystem components; identify open-source algorithms and techniques; and integrate robot hardware.

Particle Swarm Optimization with Double Learning Patterns

PubMed Central

Shen, Yuanxia; Wei, Linna; Zeng, Chuanhua; Chen, Jian

2016-01-01

Particle Swarm Optimization (PSO) is an effective tool in solving optimization problems. However, PSO usually suffers from the premature convergence due to the quick losing of the swarm diversity. In this paper, we first analyze the motion behavior of the swarm based on the probability characteristic of learning parameters. Then a PSO with double learning patterns (PSO-DLP) is developed, which employs the master swarm and the slave swarm with different learning patterns to achieve a trade-off between the convergence speed and the swarm diversity. The particles in the master swarm and the slave swarm are encouraged to explore search for keeping the swarm diversity and to learn from the global best particle for refining a promising solution, respectively. When the evolutionary states of two swarms interact, an interaction mechanism is enabled. This mechanism can help the slave swarm in jumping out of the local optima and improve the convergence precision of the master swarm. The proposed PSO-DLP is evaluated on 20 benchmark functions, including rotated multimodal and complex shifted problems. The simulation results and statistical analysis show that PSO-DLP obtains a promising performance and outperforms eight PSO variants. PMID:26858747

From Present Surveying to Future Prospecting of the Asteroid Belt

NASA Technical Reports Server (NTRS)

Clark, P. E.; Curtis, S. A.; Rilee, M.; Cheung, C.

2004-01-01

We have applied a future mission architecture, the Autonomous Nano-Technology Swarm (ANTS), to a proposed mission for in situ survey, or prospecting, of the asteroid belt, the Prospecting Asteroid Mission (PAM) as part of a NASA 2003 Revolutionary Aerospace Concept (RASC) study. ANTS architecture builds on and advances recent trends in robotics, artificial intelligence, and materials processing to minimize costs and maximize effectiveness of space operations. PAM and other applications have been proposed for the survey of inaccessible, high surface area populations of great interest from the standpoint of resources and/or solar system origin. The ANTS architecture is inspired by the success of social insect colonies, a success based on the division of labor within the colonies in two key ways: 1) within their specialties, individual specialists generally outperform generalists, and 2) with sufficiently efficient social interaction and coordination, the group of specialists generally outperforms the group of generalists. Thus systems designed as ANTS are built from potentially very large numbers of highly autonomous, yet socially interactive, elements. The architecture is self-similar in that elements and sub-elements of the system may also be recursively structured as ANTS on scales ranging from microscopic to interplanetary distances. Here, we analyze requirements for the mission application at the low gravity target end of the spectrum, the Prospecting Asteroid Mission (PAM), and for specialized autonomous operations which would support this mission. ANTS as applied to PAM involves the activities of hundreds of individual specialist 'sciencecraft'. Most of them, called Workers, carry and operate eight to nine different scientific instruments, as listed in the table, including spectrometers, ranging and radio science devices, and imagers. The remaining specialists, Messenger/Rulers, provide communication and coordination functions among specialists operating autonomously as individuals, team members, and subswarms.

Experiences with a Requirements-Based Programming Approach to the Development of a NASA Autonomous Ground Control System

NASA Technical Reports Server (NTRS)

Hinchey, Michael G.; Rash, James L.; Rouff, Christopher A.; Gracanin, Denis; Erickson, John

2005-01-01

Requirements-to-Design-to-Code (R2D2C) is an approach to the engineering of computer-based systems that embodies the idea of requirements-based programming in system development. It goes further; however, in that the approach offers not only an underlying formalism, but full formal development from requirements capture through to the automatic generation of provably-correct code. As such, the approach has direct application to the development of systems requiring autonomic properties. We describe a prototype tool to support the method, and illustrate its applicability to the development of LOGOS, a NASA autonomous ground control system, which exhibits autonomic behavior. Finally, we briefly discuss other areas where the approach and prototype tool are being considered for application.

Virtual spring damper method for nonholonomic robotic swarm self-organization and leader following

NASA Astrophysics Data System (ADS)

Wiech, Jakub; Eremeyev, Victor A.; Giorgio, Ivan

2018-04-01

In this paper, we demonstrate a method for self-organization and leader following of nonholonomic robotic swarm based on spring damper mesh. By self-organization of swarm robots we mean the emergence of order in a swarm as the result of interactions among the single robots. In other words the self-organization of swarm robots mimics some natural behavior of social animals like ants among others. The dynamics of two-wheel robot is derived, and a relation between virtual forces and robot control inputs is defined in order to establish stable swarm formation. Two cases of swarm control are analyzed. In the first case the swarm cohesion is achieved by virtual spring damper mesh connecting nearest neighboring robots without designated leader. In the second case we introduce a swarm leader interacting with nearest and second neighbors allowing the swarm to follow the leader. The paper ends with numeric simulation for performance evaluation of the proposed control method.

Harnessing wake vortices for efficient collective swimming via deep reinfrcement learning

NASA Astrophysics Data System (ADS)

Verma, Siddartha; Novati, Guido; Koumoutsakos, Petros; ChairComputing Science Team

2017-11-01

Collective motion may bestow evolutionary advantages to a number of animal species. Soaring flocks of birds, teeming swarms of insects, and swirling masses of schooling fish, all to some extent enjoy anti-predator benefits, increased foraging success, and enhanced problem-solving abilities. Coordinated activity may also provide energetic benefits, as in the case of large groups of fish where swimmers exploit unsteady flow-patterns generated in the wake. Both experimental and computational investigations of such scenarios are hampered by difficulties associated with studying multiple swimmers. Consequentially, the precise energy-saving mechanisms at play remain largely unknown. We combine high-fidelity numerical simulations of multiple, self propelled swimmers with novel deep reinforcement learning algorithms to discover optimal ways for swimmers to interact with unsteady wakes, in a fully unsupervised manner. We identify optimal flow-interaction strategies devised by the resulting autonomous swimmers, and use it to formulate an effective control-logic. We demonstrate, via 3D simulations of controlled groups that swimmers exploiting the learned strategy exhibit a significant reduction in energy-expenditure. ERC Advanced Investigator Award 341117.

A review on economic emission dispatch problems using quantum computational intelligence

NASA Astrophysics Data System (ADS)

Mahdi, Fahad Parvez; Vasant, Pandian; Kallimani, Vish; Abdullah-Al-Wadud, M.

2016-11-01

Economic emission dispatch (EED) problems are one of the most crucial problems in power systems. Growing energy demand, limitation of natural resources and global warming make this topic into the center of discussion and research. This paper reviews the use of Quantum Computational Intelligence (QCI) in solving Economic Emission Dispatch problems. QCI techniques like Quantum Genetic Algorithm (QGA) and Quantum Particle Swarm Optimization (QPSO) algorithm are discussed here. This paper will encourage the researcher to use more QCI based algorithm to get better optimal result for solving EED problems.

Self-organizing control strategy for asteroid intelligent detection swarm based on attraction and repulsion

NASA Astrophysics Data System (ADS)

An, Meiyan; Wang, Zhaokui; Zhang, Yulin

2017-01-01

The self-organizing control strategy for asteroid intelligent detection swarm, which is considered as a space application instance of intelligent swarm, is developed. The leader-follower model for the asteroid intelligent detection swarm is established, and the further analysis is conducted for massive asteroid and small asteroid. For a massive asteroid, the leader spacecraft flies under the gravity field of the asteroid. For a small asteroid, the asteroid gravity is negligible, and a trajectory planning method is proposed based on elliptic cavity virtual potential field. The self-organizing control strategy for the follower spacecraft is developed based on a mechanism of velocity planning and velocity tracking. The simulation results show that the self-organizing control strategy is valid for both massive asteroid and small asteroid, and the exploration swarm forms a stable configuration.

Feature and Intensity Based Medical Image Registration Using Particle Swarm Optimization.

PubMed

Abdel-Basset, Mohamed; Fakhry, Ahmed E; El-Henawy, Ibrahim; Qiu, Tie; Sangaiah, Arun Kumar

2017-11-03

Image registration is an important aspect in medical image analysis, and kinds use in a variety of medical applications. Examples include diagnosis, pre/post surgery guidance, comparing/merging/integrating images from multi-modal like Magnetic Resonance Imaging (MRI), and Computed Tomography (CT). Whether registering images across modalities for a single patient or registering across patients for a single modality, registration is an effective way to combine information from different images into a normalized frame for reference. Registered datasets can be used for providing information relating to the structure, function, and pathology of the organ or individual being imaged. In this paper a hybrid approach for medical images registration has been developed. It employs a modified Mutual Information (MI) as a similarity metric and Particle Swarm Optimization (PSO) method. Computation of mutual information is modified using a weighted linear combination of image intensity and image gradient vector flow (GVF) intensity. In this manner, statistical as well as spatial image information is included into the image registration process. Maximization of the modified mutual information is effected using the versatile Particle Swarm Optimization which is developed easily with adjusted less parameter. The developed approach has been tested and verified successfully on a number of medical image data sets that include images with missing parts, noise contamination, and/or of different modalities (CT, MRI). The registration results indicate the proposed model as accurate and effective, and show the posture contribution in inclusion of both statistical and spatial image data to the developed approach.

From organized internal traffic to collective navigation of bacterial swarms

NASA Astrophysics Data System (ADS)

Ariel, Gil; Shklarsh, Adi; Kalisman, Oren; Ingham, Colin; Ben-Jacob, Eshel

2013-12-01

Bacterial swarming resulting in collective navigation over surfaces provides a valuable example of cooperative colonization of new territories. The social bacterium Paenibacillus vortex exhibits successful and diverse swarming strategies. When grown on hard agar surfaces with peptone, P. vortex develops complex colonies of vortices (rotating bacterial aggregates). In contrast, during growth on Mueller-Hinton broth gelled into a soft agar surface, a new strategy of multi-level organization is revealed: the colonies are organized into a special network of swarms (or ‘snakes’ of a fraction of millimeter in width) with intricate internal traffic. More specifically, cell movement is organized in two or three lanes of bacteria traveling between the back and the front of the swarm. This special form of cellular logistics suggests new methods in which bacteria can share resources and risk while searching for food or migrating into new territories. While the vortices-based organization on hard agar surfaces has been modeled before, here, we introduce a new multi-agent bacterial swarming model devised to capture the swarms-based organization on soft surfaces. We test two putative generic mechanisms that may underlie the observed swarming logistics: (i) chemo-activated taxis in response to chemical cues and (ii) special align-and-push interactions between the bacteria and the boundary of the layer of lubricant collectively generated by the swarming bacteria. Using realistic parameters, the model captures the observed phenomena with semi-quantitative agreement in terms of the velocity as well as the dynamics of the swarm and its envelope. This agreement implies that the bacteria interactions with the swarm boundary play a crucial role in mediating the interplay between the collective movement of the swarm and the internal traffic dynamics.

Regenerative patterning in Swarm Robots: mutual benefits of research in robotics and stem cell biology.

PubMed

Rubenstein, Michael; Sai, Ying; Chuong, Cheng-Ming; Shen, Wei-Min

2009-01-01

This paper presents a novel perspective of Robotic Stem Cells (RSCs), defined as the basic non-biological elements with stem cell like properties that can self-reorganize to repair damage to their swarming organization. Self here means that the elements can autonomously decide and execute their actions without requiring any preset triggers, commands, or help from external sources. We develop this concept for two purposes. One is to develop a new theory for self-organization and self-assembly of multi-robots systems that can detect and recover from unforeseen errors or attacks. This self-healing and self-regeneration is used to minimize the compromise of overall function for the robot team. The other is to decipher the basic algorithms of regenerative behaviors in multi-cellular animal models, so that we can understand the fundamental principles used in the regeneration of biological systems. RSCs are envisioned to be basic building elements for future systems that are capable of self-organization, self-assembly, self-healing and self-regeneration. We first discuss the essential features of biological stem cells for such a purpose, and then propose the functional requirements of robotic stem cells with properties equivalent to gene controller, program selector and executor. We show that RSCs are a novel robotic model for scalable self-organization and self-healing in computer simulations and physical implementation. As our understanding of stem cells advances, we expect that future robots will be more versatile, resilient and complex, and such new robotic systems may also demand and inspire new knowledge from stem cell biology and related fields, such as artificial intelligence and tissue engineering.

Regenerative patterning in Swarm Robots: mutual benefits of research in robotics and stem cell biology

PubMed Central

RUBENSTEIN, MICHAEL; SAI, YING; CHUONG, CHENG-MING; SHEN, WEI-MIN

2010-01-01

This paper presents a novel perspective of Robotic Stem Cells (RSCs), defined as the basic non-biological elements with stem cell like properties that can self-reorganize to repair damage to their swarming organization. “Self” here means that the elements can autonomously decide and execute their actions without requiring any preset triggers, commands, or help from external sources. We develop this concept for two purposes. One is to develop a new theory for self-organization and self-assembly of multi-robots systems that can detect and recover from unforeseen errors or attacks. This self-healing and self-regeneration is used to minimize the compromise of overall function for the robot team. The other is to decipher the basic algorithms of regenerative behaviors in multi-cellular animal models, so that we can understand the fundamental principles used in the regeneration of biological systems. RSCs are envisioned to be basic building elements for future systems that are capable of self-organization, self-assembly, self-healing and self-regeneration. We first discuss the essential features of biological stem cells for such a purpose, and then propose the functional requirements of robotic stem cells with properties equivalent to gene controller, program selector and executor. We show that RSCs are a novel robotic model for scalable self-organization and self-healing in computer simulations and physical implementation. As our understanding of stem cells advances, we expect that future robots will be more versatile, resilient and complex, and such new robotic systems may also demand and inspire new knowledge from stem cell biology and related fields, such as artificial intelligence and tissue engineering. PMID:19557691

Acceleration of the Particle Swarm Optimization for Peierls–Nabarro modeling of dislocations in conventional and high-entropy alloys

DOE PAGES

Pei, Zongrui; Max-Planck-Inst. fur Eisenforschung, Duseldorf; Eisenbach, Markus

2017-02-06

Dislocations are among the most important defects in determining the mechanical properties of both conventional alloys and high-entropy alloys. The Peierls-Nabarro model supplies an efficient pathway to their geometries and mobility. The difficulty in solving the integro-differential Peierls-Nabarro equation is how to effectively avoid the local minima in the energy landscape of a dislocation core. Among the other methods to optimize the dislocation core structures, we choose the algorithm of Particle Swarm Optimization, an algorithm that simulates the social behaviors of organisms. By employing more particles (bigger swarm) and more iterative steps (allowing them to explore for longer time), themore » local minima can be effectively avoided. But this would require more computational cost. The advantage of this algorithm is that it is readily parallelized in modern high computing architecture. We demonstrate the performance of our parallelized algorithm scales linearly with the number of employed cores.« less

An enhancement of binary particle swarm optimization for gene selection in classifying cancer classes

PubMed Central

2013-01-01

Background Gene expression data could likely be a momentous help in the progress of proficient cancer diagnoses and classification platforms. Lately, many researchers analyze gene expression data using diverse computational intelligence methods, for selecting a small subset of informative genes from the data for cancer classification. Many computational methods face difficulties in selecting small subsets due to the small number of samples compared to the huge number of genes (high-dimension), irrelevant genes, and noisy genes. Methods We propose an enhanced binary particle swarm optimization to perform the selection of small subsets of informative genes which is significant for cancer classification. Particle speed, rule, and modified sigmoid function are introduced in this proposed method to increase the probability of the bits in a particle’s position to be zero. The method was empirically applied to a suite of ten well-known benchmark gene expression data sets. Results The performance of the proposed method proved to be superior to other previous related works, including the conventional version of binary particle swarm optimization (BPSO) in terms of classification accuracy and the number of selected genes. The proposed method also requires lower computational time compared to BPSO. PMID:23617960

A Lego Mindstorms NXT based test bench for multiagent exploratory systems and distributed network partitioning

NASA Astrophysics Data System (ADS)

Patil, Riya Raghuvir

Networks of communicating agents require distributed algorithms for a variety of tasks in the field of network analysis and control. For applications such as swarms of autonomous vehicles, ad hoc and wireless sensor networks, and such military and civilian applications as exploring and patrolling a robust autonomous system that uses a distributed algorithm for selfpartitioning can be significantly helpful. A single team of autonomous vehicles in a field may need to self-dissemble into multiple teams, conducive to completing multiple control tasks. Moreover, because communicating agents are subject to changes, namely, addition or failure of an agent or link, a distributed or decentralized algorithm is favorable over having a central agent. A framework to help with the study of self-partitioning of such multi agent systems that have most basic mobility model not only saves our time in conception but also gives us a cost effective prototype without negotiating the physical realization of the proposed idea. In this thesis I present my work on the implementation of a flexible and distributed stochastic partitioning algorithm on the LegoRTM Mindstorms' NXT on a graphical programming platform using National Instruments' LabVIEW(TM) forming a team of communicating agents via NXT-Bee radio module. We single out mobility, communication and self-partition as the core elements of the work. The goal is to randomly explore a precinct for reference sites. Agents who have discovered the reference sites announce their target acquisition to form a network formed based upon the distance of each agent with the other wherein the self-partitioning begins to find an optimal partition. Further, to illustrate the work, an experimental test-bench of five Lego NXT robots is presented.

Towards an Autonomic Cluster Management System (ACMS) with Reflex Autonomicity

NASA Technical Reports Server (NTRS)

Truszkowski, Walt; Hinchey, Mike; Sterritt, Roy

2005-01-01

Cluster computing, whereby a large number of simple processors or nodes are combined together to apparently function as a single powerful computer, has emerged as a research area in its own right. The approach offers a relatively inexpensive means of providing a fault-tolerant environment and achieving significant computational capabilities for high-performance computing applications. However, the task of manually managing and configuring a cluster quickly becomes daunting as the cluster grows in size. Autonomic computing, with its vision to provide self-management, can potentially solve many of the problems inherent in cluster management. We describe the development of a prototype Autonomic Cluster Management System (ACMS) that exploits autonomic properties in automating cluster management and its evolution to include reflex reactions via pulse monitoring.

Autonomous Driver Based on an Intelligent System of Decision-Making.

PubMed

Czubenko, Michał; Kowalczuk, Zdzisław; Ordys, Andrew

The paper presents and discusses a system ( xDriver ) which uses an Intelligent System of Decision-making (ISD) for the task of car driving. The principal subject is the implementation, simulation and testing of the ISD system described earlier in our publications (Kowalczuk and Czubenko in artificial intelligence and soft computing lecture notes in computer science, lecture notes in artificial intelligence, Springer, Berlin, 2010, 2010, In Int J Appl Math Comput Sci 21(4):621-635, 2011, In Pomiary Autom Robot 2(17):60-5, 2013) for the task of autonomous driving. The design of the whole ISD system is a result of a thorough modelling of human psychology based on an extensive literature study. Concepts somehow similar to the ISD system can be found in the literature (Muhlestein in Cognit Comput 5(1):99-105, 2012; Wiggins in Cognit Comput 4(3):306-319, 2012), but there are no reports of a system which would model the human psychology for the purpose of autonomously driving a car. The paper describes assumptions for simulation, the set of needs and reactions (characterizing the ISD system), the road model and the vehicle model, as well as presents some results of simulation. It proves that the xDriver system may behave on the road as a very inexperienced driver.

Reconstruction of F-Region Electric Current Densities from more than 2 Years of Swarm Satellite Magnetic data

NASA Astrophysics Data System (ADS)

Tozzi, R.; Pezzopane, M.; De Michelis, P.; Pignalberi, A.; Siciliano, F.

2016-12-01

The constellation geometry adopted by ESA for Swarm satellites has opened the way to new investigations based on magnetic data. An example is the curl-B technique that allows reconstructing F-region electric current density in terms of its radial, meridional, and zonal components based on data from two satellites of Swarm constellation (Swarm A and B) which fly at different altitudes. Here, we apply this technique to more than 2 years of Swarm magnetic vector data and investigate the average large scale behaviour of F-region current densities as a function of local time, season and different interplanetary conditions (different strength and direction of the three IMF components and/or geomagnetic activity levels).

A new bio-inspired optimisation algorithm: Bird Swarm Algorithm

NASA Astrophysics Data System (ADS)

Meng, Xian-Bing; Gao, X. Z.; Lu, Lihua; Liu, Yu; Zhang, Hengzhen

2016-07-01

A new bio-inspired algorithm, namely Bird Swarm Algorithm (BSA), is proposed for solving optimisation applications. BSA is based on the swarm intelligence extracted from the social behaviours and social interactions in bird swarms. Birds mainly have three kinds of behaviours: foraging behaviour, vigilance behaviour and flight behaviour. Birds may forage for food and escape from the predators by the social interactions to obtain a high chance of survival. By modelling these social behaviours, social interactions and the related swarm intelligence, four search strategies associated with five simplified rules are formulated in BSA. Simulations and comparisons based on eighteen benchmark problems demonstrate the effectiveness, superiority and stability of BSA. Some proposals for future research about BSA are also discussed.

Smart materials on the way to theranostic nanorobots: Molecular machines and nanomotors, advanced biosensors, and intelligent vehicles for drug delivery.

PubMed

Sokolov, Ilya L; Cherkasov, Vladimir R; Tregubov, Andrey A; Buiucli, Sveatoslav R; Nikitin, Maxim P

2017-06-01

Theranostics, a fusion of two key parts of modern medicine - diagnostics and therapy of the organism's disorders, promises to bring the efficacy of medical treatment to a fundamentally new level and to become the basis of personalized medicine. Extrapolating today's progress in the field of smart materials to the long-run prospect, we can imagine future intelligent agents capable of performing complex analysis of different physiological factors inside the living organism and implementing a built-in program thereby triggering a series of therapeutic actions. These agents, by analogy with their macroscopic counterparts, can be called nanorobots. It is quite obscure what these devices are going to look like but they will be more or less based on today's achievements in nanobiotechnology. The present Review is an attempt to systematize highly diverse nanomaterials, which may potentially serve as modules for theranostic nanorobotics, e.g., nanomotors, sensing units, and payload carriers. Biocomputing-based sensing, externally actuated or chemically "fueled" autonomous movement, swarm inter-agent communication behavior are just a few inspiring examples that nanobiotechnology can offer today for construction of truly intelligent drug delivery systems. The progress of smart nanomaterials toward fully autonomous drug delivery nanorobots is an exciting prospect for disease treatment. Synergistic combination of the available approaches and their further development may produce intelligent drugs of unmatched functionality. Copyright © 2017 Elsevier B.V. All rights reserved.

Robust electromagnetically guided endoscopic procedure using enhanced particle swarm optimization for multimodal information fusion.

PubMed

Luo, Xiongbiao; Wan, Ying; He, Xiangjian

2015-04-01

Electromagnetically guided endoscopic procedure, which aims at accurately and robustly localizing the endoscope, involves multimodal sensory information during interventions. However, it still remains challenging in how to integrate these information for precise and stable endoscopic guidance. To tackle such a challenge, this paper proposes a new framework on the basis of an enhanced particle swarm optimization method to effectively fuse these information for accurate and continuous endoscope localization. The authors use the particle swarm optimization method, which is one of stochastic evolutionary computation algorithms, to effectively fuse the multimodal information including preoperative information (i.e., computed tomography images) as a frame of reference, endoscopic camera videos, and positional sensor measurements (i.e., electromagnetic sensor outputs). Since the evolutionary computation method usually limits its possible premature convergence and evolutionary factors, the authors introduce the current (endoscopic camera and electromagnetic sensor's) observation to boost the particle swarm optimization and also adaptively update evolutionary parameters in accordance with spatial constraints and the current observation, resulting in advantageous performance in the enhanced algorithm. The experimental results demonstrate that the authors' proposed method provides a more accurate and robust endoscopic guidance framework than state-of-the-art methods. The average guidance accuracy of the authors' framework was about 3.0 mm and 5.6° while the previous methods show at least 3.9 mm and 7.0°. The average position and orientation smoothness of their method was 1.0 mm and 1.6°, which is significantly better than the other methods at least with (2.0 mm and 2.6°). Additionally, the average visual quality of the endoscopic guidance was improved to 0.29. A robust electromagnetically guided endoscopy framework was proposed on the basis of an enhanced particle swarm optimization method with using the current observation information and adaptive evolutionary factors. The authors proposed framework greatly reduced the guidance errors from (4.3, 7.8) to (3.0 mm, 5.6°), compared to state-of-the-art methods.

Speed control for a mobile robot

NASA Astrophysics Data System (ADS)

Kolli, Kaylan C.; Mallikarjun, Sreeram; Kola, Krishnamohan; Hall, Ernest L.

1997-09-01

Automated guided vehicles (AGVs) have many potential applications in manufacturing, medicine, space and defense. The purpose of this paper is to describe exploratory research on the design of a speed control for a modular autonomous mobile robot controller. The speed control of the traction motor is essential for safe operation of a mobile robot. The challenges of autonomous operation of a vehicle require safe, runaway and collision free operation. A mobile robot test-bed has been constructed using a golf cart base. The computer controlled speed control has been implemented and works with guidance provided by vision system and obstacle avoidance using ultrasonic sensors systems. A 486 computer through a 3- axis motion controller supervises the speed control. The traction motor is controlled via the computer by an EV-1 speed control. Testing of the system was done both in the lab and on an outside course with positive results. This design is a prototype and suggestions for improvements are also given. The autonomous speed controller is applicable for any computer controlled electric drive mobile vehicle.

Operating Small Sat Swarms as a Single Entity: Introducing SODA

NASA Technical Reports Server (NTRS)

Conn, Tracie; Dono Perez, Andres

2017-01-01

Swarm concepts are a growing topic of interest in the small satellite community. Compared to a small satellite constellation, a swarm has the distinction of being multiple spacecraft in close proximity, in approximately the same orbit. Furthermore, we envision swarms to have capabilities for cross-link communication and station-keeping. Of particular interest is a means to maintain operator-specified geometry, alignment, and/or separation.From NASA's decadal survey, it is clear that simultaneous measurements from a 3D volume of space are desired for a variety of Earth scientific studies. As this mission concept is ultimately extended to deep space, some degree of local control for the swarm to self-correct its configuration is required. We claim that the practicality of ground commanding each individual satellite in the swarm is simply not a feasible concept of operations. In other words, the current state-of-practice does not scale to very large swarms (e.g. 100 spacecraft or more) without becoming cost prohibitive. To contain the operations costs and complexity, a new approach is required: the swarm must be operated as a unit, responding to high-level specifications for relative position and velocity.The Mission Design Division at NASA Ames Research Center is looking to the near future for opportunities to develop satellite swarm technology. As part of this effort, we are developing SODA (Swarm Orbital Dynamics Advisor), a tool that provides the orbital maneuvers required to achieve a desired type of relative swarm motion. The purpose of SODA is two-fold. First, it encompasses the algorithms and orbital dynamics model to enable the desired relative motion of the swarm satellites. The process starts with the user specifying the properties of a swarm configuration. This could be as simple as varying in-track spacing of the swarm in one orbit, or as complex as maintaining a specified 3D geometrical orientation. We presume that science objectives will drive this choice. Given these inputs, the tool provides the most efficient maneuver(s) to achieve the objective.Second, SODA provides a variety of visualization tools. We acknowledge that the relationship between a desired relative motion amongst the swarm, and the corresponding orbital parameters for each individual satellite may not be immediately apparent for ground controllers and mission planners. The purpose of SODA's visualization tools is to illustrate this concept clearly with a variety of graphics and animations. After computing the optimal orbital maneuvers to modify the swarm, these results are simulated to demonstrate successful swarm control.Our emphasis in this paper is on the importance of relating the desired motion of the swarm satellites relative to one another with the required orbital element changes. One cannot joystick a drifting swarm satellite back into position; the underlying orbital mechanics dictate the most efficient recovery maneuvers. To illustrate this point, results from several case study simulations are presented. We conclude with our forward work for ongoing SODA development and potential science applications.

Hard-real-time resource management for autonomous spacecraft

NASA Technical Reports Server (NTRS)

Gat, E.

2000-01-01

This paper describes tickets, a computational mechanism for hard-real-time autonomous resource management. Autonomous spacecraftcontrol can be considered abstractly as a computational process whose outputs are spacecraft commands.

Cognitive computing and eScience in health and life science research: artificial intelligence and obesity intervention programs.

PubMed

Marshall, Thomas; Champagne-Langabeer, Tiffiany; Castelli, Darla; Hoelscher, Deanna

2017-12-01

To present research models based on artificial intelligence and discuss the concept of cognitive computing and eScience as disruptive factors in health and life science research methodologies. The paper identifies big data as a catalyst to innovation and the development of artificial intelligence, presents a framework for computer-supported human problem solving and describes a transformation of research support models. This framework includes traditional computer support; federated cognition using machine learning and cognitive agents to augment human intelligence; and a semi-autonomous/autonomous cognitive model, based on deep machine learning, which supports eScience. The paper provides a forward view of the impact of artificial intelligence on our human-computer support and research methods in health and life science research. By augmenting or amplifying human task performance with artificial intelligence, cognitive computing and eScience research models are discussed as novel and innovative systems for developing more effective adaptive obesity intervention programs.

Swarming behavior of gradient-responsive Brownian particles in a porous medium.

PubMed

Grančič, Peter; Štěpánek, František

2012-07-01

Active targeting by Brownian particles in a fluid-filled porous environment is investigated by computer simulation. The random motion of the particles is enhanced by diffusiophoresis with respect to concentration gradients of chemical signals released by the particles in the proximity of a target. The mathematical model, based on a combination of the Brownian dynamics method and a diffusion problem is formulated in terms of key parameters that include the particle diffusiophoretic mobility and the signaling threshold (the distance from the target at which the particles release their chemical signals). The results demonstrate that even a relatively simple chemical signaling scheme can lead to a complex collective behavior of the particles and can be a very efficient way of guiding a swarm of Brownian particles towards a target, similarly to the way colonies of living cells communicate via secondary messengers.

Application of Genetic Algorithm and Particle Swarm Optimization techniques for improved image steganography systems

NASA Astrophysics Data System (ADS)

Jude Hemanth, Duraisamy; Umamaheswari, Subramaniyan; Popescu, Daniela Elena; Naaji, Antoanela

2016-01-01

Image steganography is one of the ever growing computational approaches which has found its application in many fields. The frequency domain techniques are highly preferred for image steganography applications. However, there are significant drawbacks associated with these techniques. In transform based approaches, the secret data is embedded in random manner in the transform coefficients of the cover image. These transform coefficients may not be optimal in terms of the stego image quality and embedding capacity. In this work, the application of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) have been explored in the context of determining the optimal coefficients in these transforms. Frequency domain transforms such as Bandelet Transform (BT) and Finite Ridgelet Transform (FRIT) are used in combination with GA and PSO to improve the efficiency of the image steganography system.

A Computational Intelligence (CI) Approach to the Precision Mars Lander Problem

NASA Technical Reports Server (NTRS)

Birge, Brian; Walberg, Gerald

2002-01-01

A Mars precision landing requires a landed footprint of no more than 100 meters. Obstacles to reducing the landed footprint include trajectory dispersions due to initial atmospheric entry conditions such as entry angle, parachute deployment height, environment parameters such as wind, atmospheric density, parachute deployment dynamics, unavoidable injection error or propagated error from launch, etc. Computational Intelligence (CI) techniques such as Artificial Neural Nets and Particle Swarm Optimization have been shown to have great success with other control problems. The research period extended previous work on investigating applicability of the computational intelligent approaches. The focus of this investigation was on Particle Swarm Optimization and basic Neural Net architectures. The research investigating these issues was performed for the grant cycle from 5/15/01 to 5/15/02. Matlab 5.1 and 6.0 along with NASA's POST were the primary computational tools.

Compact cancer biomarkers discovery using a swarm intelligence feature selection algorithm.

PubMed

Martinez, Emmanuel; Alvarez, Mario Moises; Trevino, Victor

2010-08-01

Biomarker discovery is a typical application from functional genomics. Due to the large number of genes studied simultaneously in microarray data, feature selection is a key step. Swarm intelligence has emerged as a solution for the feature selection problem. However, swarm intelligence settings for feature selection fail to select small features subsets. We have proposed a swarm intelligence feature selection algorithm based on the initialization and update of only a subset of particles in the swarm. In this study, we tested our algorithm in 11 microarray datasets for brain, leukemia, lung, prostate, and others. We show that the proposed swarm intelligence algorithm successfully increase the classification accuracy and decrease the number of selected features compared to other swarm intelligence methods. Copyright © 2010 Elsevier Ltd. All rights reserved.

On the capability of Swarm for surface mass variation monitoring: Quantitative assessment based on orbit information from CHAMP, GRACE and GOCE

NASA Astrophysics Data System (ADS)

Baur, Oliver; Weigelt, Matthias; Zehentner, Norbert; Mayer-Gürr, Torsten; Jäggi, Adrian

2014-05-01

In the last decade, temporal variations of the gravity field from GRACE observations have become one of the most ubiquitous and valuable sources of information for geophysical and environmental studies. In the context of global climate change, mass balance of the Arctic and Antarctic ice sheets gained particular attention. Because GRACE has outlived its predicted lifetime by several years already, it is very likely that a gap between GRACE and its successor GRACE follow-on (supposed to be launched in 2017, at the earliest) occurs. The Swarm mission - launched on November 22, 2013 - is the most promising candidate to bridge this potential gap, i.e., to directly acquire large-scale mass variation information on the Earth's surface in case of a gap between the present GRACE and the upcoming GRACE follow-on projects. Although the magnetometry mission Swarm has not been designed for gravity field purposes, its three satellites have the characteristics for such an endeavor: (i) low, near-circular and near-polar orbits, (ii) precise positioning with high-quality GNSS receivers, (iii) on-board accelerometers to measure the influence of non-gravitational forces. Hence, from an orbit analysis point of view the Swarm satellites are comparable to the CHAMP, GRACE and GOCE spacecraft. Indeed and as data analysis from CHAMP has been shown, the detection of annual signals and trends from orbit analysis is possible for long-wavelength features of the gravity field, although the accuracy associated with the inter-satellite GRACE measurements cannot be reached. We assess the capability of the (non-dedicated) mission Swarm for mass variation detection in a real-case environment (opposed to simulation studies). For this purpose, we "approximate" the Swarm scenario by the GRACE+CHAMP and GRACE+GOCE constellations. In a first step, kinematic orbits of the individual satellites are derived from GNSS observations. From these orbits, we compute monthly combined GRACE+CHAMP and GRACE+GOCE time-variable gravity fields; sophisticated techniques based on Kalman filtering are applied to reduce noise in the time series. Finally, we infer mass variation in selected areas from to gravity signal. These results are compared to the findings obtained from mass variation detection exploiting CSR-RL05 gravity fields; due to their superior quality (which is due to the fact that they are derived from inter-satellite GRACE measurements), the CSR-RL05 solutions serve as benchmark. Our quantitative assessment shows the potential and limitations of what can be expected from Swarm with regard to surface mass variation monitoring.

Autonomous self-organizing resource manager for multiple networked platforms

NASA Astrophysics Data System (ADS)

Smith, James F., III

2002-08-01

A fuzzy logic based expert system for resource management has been developed that automatically allocates electronic attack (EA) resources in real-time over many dissimilar autonomous naval platforms defending their group against attackers. The platforms can be very general, e.g., ships, planes, robots, land based facilities, etc. Potential foes the platforms deal with can also be general. This paper provides an overview of the resource manager including the four fuzzy decision trees that make up the resource manager; the fuzzy EA model; genetic algorithm based optimization; co-evolutionary data mining through gaming; and mathematical, computational and hardware based validation. Methods of automatically designing new multi-platform EA techniques are considered. The expert system runs on each defending platform rendering it an autonomous system requiring no human intervention. There is no commanding platform. Instead the platforms work cooperatively as a function of battlespace geometry; sensor data such as range, bearing, ID, uncertainty measures for sensor output; intelligence reports; etc. Computational experiments will show the defending networked platform's ability to self- organize. The platforms' ability to self-organize is illustrated through the output of the scenario generator, a software package that automates the underlying data mining problem and creates a computer movie of the platforms' interaction for evaluation.

Design and implementation of intelligent electronic warfare decision making algorithm

NASA Astrophysics Data System (ADS)

Peng, Hsin-Hsien; Chen, Chang-Kuo; Hsueh, Chi-Shun

2017-05-01

Electromagnetic signals and the requirements of timely response have been a rapid growth in modern electronic warfare. Although jammers are limited resources, it is possible to achieve the best electronic warfare efficiency by tactical decisions. This paper proposes the intelligent electronic warfare decision support system. In this work, we develop a novel hybrid algorithm, Digital Pheromone Particle Swarm Optimization, based on Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and Shuffled Frog Leaping Algorithm (SFLA). We use PSO to solve the problem and combine the concept of pheromones in ACO to accumulate more useful information in spatial solving process and speed up finding the optimal solution. The proposed algorithm finds the optimal solution in reasonable computation time by using the method of matrix conversion in SFLA. The results indicated that jammer allocation was more effective. The system based on the hybrid algorithm provides electronic warfare commanders with critical information to assist commanders in effectively managing the complex electromagnetic battlefield.

Log-linear model based behavior selection method for artificial fish swarm algorithm.

PubMed

Huang, Zhehuang; Chen, Yidong

2015-01-01

Artificial fish swarm algorithm (AFSA) is a population based optimization technique inspired by social behavior of fishes. In past several years, AFSA has been successfully applied in many research and application areas. The behavior of fishes has a crucial impact on the performance of AFSA, such as global exploration ability and convergence speed. How to construct and select behaviors of fishes are an important task. To solve these problems, an improved artificial fish swarm algorithm based on log-linear model is proposed and implemented in this paper. There are three main works. Firstly, we proposed a new behavior selection algorithm based on log-linear model which can enhance decision making ability of behavior selection. Secondly, adaptive movement behavior based on adaptive weight is presented, which can dynamically adjust according to the diversity of fishes. Finally, some new behaviors are defined and introduced into artificial fish swarm algorithm at the first time to improve global optimization capability. The experiments on high dimensional function optimization showed that the improved algorithm has more powerful global exploration ability and reasonable convergence speed compared with the standard artificial fish swarm algorithm.

Autonomic Computing: Freedom or a Threat?

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fink, Glenn A.; Frincke, Deb

2007-12-01

No longer is the question whether autonomic computing will gain general acceptance but when. Experts expect autonomic computing to be widely used within 10 years. When it does become mainstream, how will autonomics change system administration and corporations, and will the change be for better or worse? The answer depends on how well we anticipate the limitations of what autonomic systems are suited to do, whether we can collectively address the vulnerabilities of autonomic approaches as we draw upon the advantages, and whether administrators, companies, partners, and users are prepared for the transition. This article presents some design considerations tomore » address the first two issues and some suggested survival techniques for the third.« less

An Application of Artificial Intelligence to the Implementation of Electronic Commerce

NASA Astrophysics Data System (ADS)

Srivastava, Anoop Kumar

In this paper, we present an application of Artificial Intelligence (AI) to the implementation of Electronic Commerce. We provide a multi autonomous agent based framework. Our agent based architecture leads to flexible design of a spectrum of multiagent system (MAS) by distributing computation and by providing a unified interface to data and programs. Autonomous agents are intelligent enough and provide autonomy, simplicity of communication, computation, and a well developed semantics. The steps of design and implementation are discussed in depth, structure of Electronic Marketplace, an ontology, the agent model, and interaction pattern between agents is given. We have developed mechanisms for coordination between agents using a language, which is called Virtual Enterprise Modeling Language (VEML). VEML is a integration of Java and Knowledge Query and Manipulation Language (KQML). VEML provides application programmers with potential to globally develop different kinds of MAS based on their requirements and applications. We have implemented a multi autonomous agent based system called VE System. We demonstrate efficacy of our system by discussing experimental results and its salient features.

Agent-Based Simulation and Analysis of a Defensive UAV Swarm Against an Enemy UAV Swarm

DTIC Science & Technology

2011-06-01

de Investigacion, Programas y Desarrollo de la Armada Armada de Chile CHILE 10. CAPT Jeffrey Kline, USN(ret.) Naval Postgraduate School Monterey, California 91 ...this de - fensive swarm system, an agent-based simulation model is developed, and appropriate designs of experiments and statistical analyses are... de - velopment and implementation of counter UAV technology from readily-available commercial products. The organization leverages the “largest

The Design of a Polymorphous Cognitive Agent Architecture (PCAA)

DTIC Science & Technology

2008-05-01

tree, and search agents may search the tree for documents or clusters, depositing pheromones on the way down the tree. 46 19 The quality of SODAS...location in the lattice is a node, connected to its neighbors by links, and agents roam across the lattice, depositing pheromones . 49 21 A possible FPGA...provided by swarming, and also figure out a way for learning in ACT-R to trickle down to swarming computations, e.g., through the pheromones . Integration

A persistent homology approach to collective behavior in insect swarms

NASA Astrophysics Data System (ADS)

Sinhuber, Michael; Ouellette, Nicholas T.

Various animals from birds and fish to insects tend to form aggregates, displaying self-organized collective swarming behavior. Due to their frequent occurrence in nature and their implications for engineered, collective systems, these systems have been investigated and modeled thoroughly for decades. Common approaches range from modeling them with coupled differential equations on the individual level up to continuum approaches. We present an alternative, topology-based approach for describing swarming behavior at the macroscale rather than the microscale. We study laboratory swarms of Chironomus riparius, a flying, non-biting midge. To obtain the time-resolved three-dimensional trajectories of individual insects, we use a multi-camera stereoimaging and particle-tracking setup. To investigate the swarming behavior in a topological sense, we employ a persistent homology approach to identify persisting structures and features in the insect swarm that elude a direct, ensemble-averaging approach. We are able to identify features of sub-clusters in the swarm that show behavior distinct from that of the remaining swarm members. The coexistence of sub-swarms with different features resembles some non-biological systems such as active colloids or even thermodynamic systems.

Agent-based modeling and systems dynamics model reproduction.

DOE Office of Scientific and Technical Information (OSTI.GOV)

North, M. J.; Macal, C. M.

2009-01-01

Reproducibility is a pillar of the scientific endeavour. We view computer simulations as laboratories for electronic experimentation and therefore as tools for science. Recent studies have addressed model reproduction and found it to be surprisingly difficult to replicate published findings. There have been enough failed simulation replications to raise the question, 'can computer models be fully replicated?' This paper answers in the affirmative by reporting on a successful reproduction study using Mathematica, Repast and Swarm for the Beer Game supply chain model. The reproduction process was valuable because it demonstrated the original result's robustness across modelling methodologies and implementation environments.

Seismic monitoring in the oceans by autonomous floats.

PubMed

Sukhovich, Alexey; Bonnieux, Sébastien; Hello, Yann; Irisson, Jean-Olivier; Simons, Frederik J; Nolet, Guust

2015-08-20

Our understanding of the internal dynamics of the Earth is largely based on images of seismic velocity variations in the mantle obtained with global tomography. However, our ability to image the mantle is severely hampered by a lack of seismic data collected in marine areas. Here we report observations made under different noise conditions (in the Mediterranean Sea, the Indian and Pacific Oceans) by a submarine floating seismograph, and show that such floats are able to fill the oceanic data gap. Depending on the ambient noise level, the floats can record between 35 and 63% of distant earthquakes with a moment magnitude M≥6.5. Even magnitudes <6.0 can be successfully observed under favourable noise conditions. The serendipitous recording of an earthquake swarm near the Indian Ocean triple junction enabled us to establish a threshold magnitude between 2.7 and 3.4 for local earthquakes in the noisiest of the three environments.

Seismic monitoring in the oceans by autonomous floats

PubMed Central

Sukhovich, Alexey; Bonnieux, Sébastien; Hello, Yann; Irisson, Jean-Olivier; Simons, Frederik J.; Nolet, Guust

2015-01-01

Our understanding of the internal dynamics of the Earth is largely based on images of seismic velocity variations in the mantle obtained with global tomography. However, our ability to image the mantle is severely hampered by a lack of seismic data collected in marine areas. Here we report observations made under different noise conditions (in the Mediterranean Sea, the Indian and Pacific Oceans) by a submarine floating seismograph, and show that such floats are able to fill the oceanic data gap. Depending on the ambient noise level, the floats can record between 35 and 63% of distant earthquakes with a moment magnitude M≥6.5. Even magnitudes <6.0 can be successfully observed under favourable noise conditions. The serendipitous recording of an earthquake swarm near the Indian Ocean triple junction enabled us to establish a threshold magnitude between 2.7 and 3.4 for local earthquakes in the noisiest of the three environments. PMID:26289598

Optimal path planning for a mobile robot using cuckoo search algorithm

NASA Astrophysics Data System (ADS)

Mohanty, Prases K.; Parhi, Dayal R.

2016-03-01

The shortest/optimal path planning is essential for efficient operation of autonomous vehicles. In this article, a new nature-inspired meta-heuristic algorithm has been applied for mobile robot path planning in an unknown or partially known environment populated by a variety of static obstacles. This meta-heuristic algorithm is based on the levy flight behaviour and brood parasitic behaviour of cuckoos. A new objective function has been formulated between the robots and the target and obstacles, which satisfied the conditions of obstacle avoidance and target-seeking behaviour of robots present in the terrain. Depending upon the objective function value of each nest (cuckoo) in the swarm, the robot avoids obstacles and proceeds towards the target. The smooth optimal trajectory is framed with this algorithm when the robot reaches its goal. Some simulation and experimental results are presented at the end of the paper to show the effectiveness of the proposed navigational controller.

SWARM: A 32 GHz Correlator and VLBI Beamformer for the Submillimeter Array

NASA Astrophysics Data System (ADS)

Primiani, Rurik A.; Young, Kenneth H.; Young, André; Patel, Nimesh; Wilson, Robert W.; Vertatschitsch, Laura; Chitwood, Billie B.; Srinivasan, Ranjani; MacMahon, David; Weintroub, Jonathan

2016-03-01

A 32GHz bandwidth VLBI capable correlator and phased array has been designed and deployeda at the Smithsonian Astrophysical Observatory’s Submillimeter Array (SMA). The SMA Wideband Astronomical ROACH2 Machine (SWARM) integrates two instruments: a correlator with 140kHz spectral resolution across its full 32GHz band, used for connected interferometric observations, and a phased array summer used when the SMA participates as a station in the Event Horizon Telescope (EHT) very long baseline interferometry (VLBI) array. For each SWARM quadrant, Reconfigurable Open Architecture Computing Hardware (ROACH2) units shared under open-source from the Collaboration for Astronomy Signal Processing and Electronics Research (CASPER) are equipped with a pair of ultra-fast analog-to-digital converters (ADCs), a field programmable gate array (FPGA) processor, and eight 10 Gigabit Ethernet (GbE) ports. A VLBI data recorder interface designated the SWARM digital back end, or SDBE, is implemented with a ninth ROACH2 per quadrant, feeding four Mark6 VLBI recorders with an aggregate recording rate of 64 Gbps. This paper describes the design and implementation of SWARM, as well as its deployment at SMA with reference to verification and science data.

Reversals and collisions optimize protein exchange in bacterial swarms

DOE Office of Scientific and Technical Information (OSTI.GOV)

Amiri, Aboutaleb; Harvey, Cameron; Buchmann, Amy

Swarming groups of bacteria coordinate their behavior by self-organizing as a population to move over surfaces in search of nutrients and optimal niches for colonization. Many open questions remain about the cues used by swarming bacteria to achieve this self-organization. While chemical cue signaling known as quorum sensing is well-described, swarming bacteria often act and coordinate on time scales that could not be achieved via these extracellular quorum sensing cues. Here, cell-cell contact-dependent protein exchange is explored as amechanism of intercellular signaling for the bacterium Myxococcus xanthus. A detailed biologically calibrated computational model is used to study how M. xanthusmore » optimizes the connection rate between cells and maximizes the spread of an extracellular protein within the population. The maximum rate of protein spreading is observed for cells that reverse direction optimally for swarming. Cells that reverse too slowly or too fast fail to spread extracellular protein efficiently. In particular, a specific range of cell reversal frequencies was observed to maximize the cell-cell connection rate and minimize the time of protein spreading. Furthermore, our findings suggest that predesigned motion reversal can be employed to enhance the collective behavior of biological synthetic active systems.« less

Swarm intelligence-based approach for optimal design of CMOS differential amplifier and comparator circuit using a hybrid salp swarm algorithm

NASA Astrophysics Data System (ADS)

Asaithambi, Sasikumar; Rajappa, Muthaiah

2018-05-01

In this paper, an automatic design method based on a swarm intelligence approach for CMOS analog integrated circuit (IC) design is presented. The hybrid meta-heuristics optimization technique, namely, the salp swarm algorithm (SSA), is applied to the optimal sizing of a CMOS differential amplifier and the comparator circuit. SSA is a nature-inspired optimization algorithm which mimics the navigating and hunting behavior of salp. The hybrid SSA is applied to optimize the circuit design parameters and to minimize the MOS transistor sizes. The proposed swarm intelligence approach was successfully implemented for an automatic design and optimization of CMOS analog ICs using Generic Process Design Kit (GPDK) 180 nm technology. The circuit design parameters and design specifications are validated through a simulation program for integrated circuit emphasis simulator. To investigate the efficiency of the proposed approach, comparisons have been carried out with other simulation-based circuit design methods. The performances of hybrid SSA based CMOS analog IC designs are better than the previously reported studies.

Swarm intelligence-based approach for optimal design of CMOS differential amplifier and comparator circuit using a hybrid salp swarm algorithm.

PubMed

Asaithambi, Sasikumar; Rajappa, Muthaiah

2018-05-01

In this paper, an automatic design method based on a swarm intelligence approach for CMOS analog integrated circuit (IC) design is presented. The hybrid meta-heuristics optimization technique, namely, the salp swarm algorithm (SSA), is applied to the optimal sizing of a CMOS differential amplifier and the comparator circuit. SSA is a nature-inspired optimization algorithm which mimics the navigating and hunting behavior of salp. The hybrid SSA is applied to optimize the circuit design parameters and to minimize the MOS transistor sizes. The proposed swarm intelligence approach was successfully implemented for an automatic design and optimization of CMOS analog ICs using Generic Process Design Kit (GPDK) 180 nm technology. The circuit design parameters and design specifications are validated through a simulation program for integrated circuit emphasis simulator. To investigate the efficiency of the proposed approach, comparisons have been carried out with other simulation-based circuit design methods. The performances of hybrid SSA based CMOS analog IC designs are better than the previously reported studies.

Particle swarm optimization using multi-information characteristics of all personal-best information.

PubMed

Huang, Song; Tian, Na; Wang, Yan; Ji, Zhicheng

2016-01-01

Convergence stagnation is the chief difficulty to solve hard optimization problems for most particle swarm optimization variants. To address this issue, a novel particle swarm optimization using multi-information characteristics of all personal-best information is developed in our research. In the modified algorithm, two positions are defined by personal-best positions and an improved cognition term with three positions of all personal-best information is used in velocity update equation to enhance the search capability. This strategy could make particles fly to a better direction by discovering useful information from all the personal-best positions. The validity of the proposed algorithm is assessed on twenty benchmark problems including unimodal, multimodal, rotated and shifted functions, and the results are compared with that obtained by some published variants of particle swarm optimization in the literature. Computational results demonstrate that the proposed algorithm finds several global optimum and high-quality solutions in most case with a fast convergence speed.

Synthetic collective intelligence.

PubMed

Solé, Ricard; Amor, Daniel R; Duran-Nebreda, Salva; Conde-Pueyo, Núria; Carbonell-Ballestero, Max; Montañez, Raúl

2016-10-01

Intelligent systems have emerged in our biosphere in different contexts and achieving different levels of complexity. The requirement of communication in a social context has been in all cases a determinant. The human brain, probably co-evolving with language, is an exceedingly successful example. Similarly, social insects complex collective decisions emerge from information exchanges between many agents. The difference is that such processing is obtained out of a limited individual cognitive power. Computational models and embodied versions using non-living systems, particularly involving robot swarms, have been used to explore the potentiality of collective intelligence. Here we suggest a novel approach to the problem grounded in the genetic engineering of unicellular systems, which can be modified in order to interact, store memories or adapt to external stimuli in collective ways. What we label as Synthetic Swarm Intelligence defines a parallel approach to the evolution of computation and swarm intelligence and allows to explore potential embodied scenarios for decision making at the microscale. Here, we consider several relevant examples of collective intelligence and their synthetic organism counterparts. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Impact of Noise on a Dynamical System: Prediction and Uncertainties from a Swarm-Optimized Neural Network

PubMed Central

López-Caraballo, C. H.; Lazzús, J. A.; Salfate, I.; Rojas, P.; Rivera, M.; Palma-Chilla, L.

2015-01-01

An artificial neural network (ANN) based on particle swarm optimization (PSO) was developed for the time series prediction. The hybrid ANN+PSO algorithm was applied on Mackey-Glass chaotic time series in the short-term x(t + 6). The performance prediction was evaluated and compared with other studies available in the literature. Also, we presented properties of the dynamical system via the study of chaotic behaviour obtained from the predicted time series. Next, the hybrid ANN+PSO algorithm was complemented with a Gaussian stochastic procedure (called stochastic hybrid ANN+PSO) in order to obtain a new estimator of the predictions, which also allowed us to compute the uncertainties of predictions for noisy Mackey-Glass chaotic time series. Thus, we studied the impact of noise for several cases with a white noise level (σ N) from 0.01 to 0.1. PMID:26351449

Impact of Noise on a Dynamical System: Prediction and Uncertainties from a Swarm-Optimized Neural Network.

PubMed

López-Caraballo, C H; Lazzús, J A; Salfate, I; Rojas, P; Rivera, M; Palma-Chilla, L

2015-01-01

An artificial neural network (ANN) based on particle swarm optimization (PSO) was developed for the time series prediction. The hybrid ANN+PSO algorithm was applied on Mackey-Glass chaotic time series in the short-term x(t + 6). The performance prediction was evaluated and compared with other studies available in the literature. Also, we presented properties of the dynamical system via the study of chaotic behaviour obtained from the predicted time series. Next, the hybrid ANN+PSO algorithm was complemented with a Gaussian stochastic procedure (called stochastic hybrid ANN+PSO) in order to obtain a new estimator of the predictions, which also allowed us to compute the uncertainties of predictions for noisy Mackey-Glass chaotic time series. Thus, we studied the impact of noise for several cases with a white noise level (σ(N)) from 0.01 to 0.1.

A Dynamic Connectome Supports the Emergence of Stable Computational Function of Neural Circuits through Reward-Based Learning.

PubMed

Kappel, David; Legenstein, Robert; Habenschuss, Stefan; Hsieh, Michael; Maass, Wolfgang

2018-01-01

Synaptic connections between neurons in the brain are dynamic because of continuously ongoing spine dynamics, axonal sprouting, and other processes. In fact, it was recently shown that the spontaneous synapse-autonomous component of spine dynamics is at least as large as the component that depends on the history of pre- and postsynaptic neural activity. These data are inconsistent with common models for network plasticity and raise the following questions: how can neural circuits maintain a stable computational function in spite of these continuously ongoing processes, and what could be functional uses of these ongoing processes? Here, we present a rigorous theoretical framework for these seemingly stochastic spine dynamics and rewiring processes in the context of reward-based learning tasks. We show that spontaneous synapse-autonomous processes, in combination with reward signals such as dopamine, can explain the capability of networks of neurons in the brain to configure themselves for specific computational tasks, and to compensate automatically for later changes in the network or task. Furthermore, we show theoretically and through computer simulations that stable computational performance is compatible with continuously ongoing synapse-autonomous changes. After reaching good computational performance it causes primarily a slow drift of network architecture and dynamics in task-irrelevant dimensions, as observed for neural activity in motor cortex and other areas. On the more abstract level of reinforcement learning the resulting model gives rise to an understanding of reward-driven network plasticity as continuous sampling of network configurations.

A Dynamic Connectome Supports the Emergence of Stable Computational Function of Neural Circuits through Reward-Based Learning

PubMed Central

Habenschuss, Stefan; Hsieh, Michael

2018-01-01

Synaptic connections between neurons in the brain are dynamic because of continuously ongoing spine dynamics, axonal sprouting, and other processes. In fact, it was recently shown that the spontaneous synapse-autonomous component of spine dynamics is at least as large as the component that depends on the history of pre- and postsynaptic neural activity. These data are inconsistent with common models for network plasticity and raise the following questions: how can neural circuits maintain a stable computational function in spite of these continuously ongoing processes, and what could be functional uses of these ongoing processes? Here, we present a rigorous theoretical framework for these seemingly stochastic spine dynamics and rewiring processes in the context of reward-based learning tasks. We show that spontaneous synapse-autonomous processes, in combination with reward signals such as dopamine, can explain the capability of networks of neurons in the brain to configure themselves for specific computational tasks, and to compensate automatically for later changes in the network or task. Furthermore, we show theoretically and through computer simulations that stable computational performance is compatible with continuously ongoing synapse-autonomous changes. After reaching good computational performance it causes primarily a slow drift of network architecture and dynamics in task-irrelevant dimensions, as observed for neural activity in motor cortex and other areas. On the more abstract level of reinforcement learning the resulting model gives rise to an understanding of reward-driven network plasticity as continuous sampling of network configurations. PMID:29696150

QPSO-Based Adaptive DNA Computing Algorithm

PubMed Central

Karakose, Mehmet; Cigdem, Ugur

2013-01-01

DNA (deoxyribonucleic acid) computing that is a new computation model based on DNA molecules for information storage has been increasingly used for optimization and data analysis in recent years. However, DNA computing algorithm has some limitations in terms of convergence speed, adaptability, and effectiveness. In this paper, a new approach for improvement of DNA computing is proposed. This new approach aims to perform DNA computing algorithm with adaptive parameters towards the desired goal using quantum-behaved particle swarm optimization (QPSO). Some contributions provided by the proposed QPSO based on adaptive DNA computing algorithm are as follows: (1) parameters of population size, crossover rate, maximum number of operations, enzyme and virus mutation rate, and fitness function of DNA computing algorithm are simultaneously tuned for adaptive process, (2) adaptive algorithm is performed using QPSO algorithm for goal-driven progress, faster operation, and flexibility in data, and (3) numerical realization of DNA computing algorithm with proposed approach is implemented in system identification. Two experiments with different systems were carried out to evaluate the performance of the proposed approach with comparative results. Experimental results obtained with Matlab and FPGA demonstrate ability to provide effective optimization, considerable convergence speed, and high accuracy according to DNA computing algorithm. PMID:23935409

Collective Cellular Decision-Making Gives Developmental Plasticity: A Model of Signaling in Branching Roots

NASA Astrophysics Data System (ADS)

McCleery, W. Tyler; Mohd-Radzman, Nadiatul A.; Grieneisen, Veronica A.

Cells within tissues can be regarded as autonomous entities that respond to their local environment and signaling from neighbors. Cell coordination is particularly important in plants, where root architecture must strategically invest resources for growth to optimize nutrient acquisition. Thus, root cells are constantly adapting to environmental cues and neighbor communication in a non-linear manner. To explain such plasticity, we view the root as a swarm of coupled multi-cellular structures, ''metamers'', rather than as a continuum of identical cells. These metamers are individually programmed to achieve a local objective - developing a lateral root primordia, which aids in local foraging of nutrients. Collectively, such individual attempts may be halted, structuring root architecture as an emergent behavior. Each metamer's decision to branch is coordinated locally and globally through hormone signaling, including processes of controlled diffusion, active polar transport, and dynamic feedback. We present a physical model of the signaling mechanism that coordinates branching decisions in response to the environment. This work was funded by the European Commission 7th Framework Program, Project No. 601062, SWARM-ORGAN.

Log-Linear Model Based Behavior Selection Method for Artificial Fish Swarm Algorithm

PubMed Central

Huang, Zhehuang; Chen, Yidong

2015-01-01

Artificial fish swarm algorithm (AFSA) is a population based optimization technique inspired by social behavior of fishes. In past several years, AFSA has been successfully applied in many research and application areas. The behavior of fishes has a crucial impact on the performance of AFSA, such as global exploration ability and convergence speed. How to construct and select behaviors of fishes are an important task. To solve these problems, an improved artificial fish swarm algorithm based on log-linear model is proposed and implemented in this paper. There are three main works. Firstly, we proposed a new behavior selection algorithm based on log-linear model which can enhance decision making ability of behavior selection. Secondly, adaptive movement behavior based on adaptive weight is presented, which can dynamically adjust according to the diversity of fishes. Finally, some new behaviors are defined and introduced into artificial fish swarm algorithm at the first time to improve global optimization capability. The experiments on high dimensional function optimization showed that the improved algorithm has more powerful global exploration ability and reasonable convergence speed compared with the standard artificial fish swarm algorithm. PMID:25691895

Swarming Robot Design, Construction and Software Implementation

NASA Technical Reports Server (NTRS)

Stolleis, Karl A.

2014-01-01

In this paper is presented an overview of the hardware design, construction overview, software design and software implementation for a small, low-cost robot to be used for swarming robot development. In addition to the work done on the robot, a full simulation of the robotic system was developed using Robot Operating System (ROS) and its associated simulation. The eventual use of the robots will be exploration of evolving behaviors via genetic algorithms and builds on the work done at the University of New Mexico Biological Computation Lab.

Finding Minimal Addition Chains with a Particle Swarm Optimization Algorithm

NASA Astrophysics Data System (ADS)

León-Javier, Alejandro; Cruz-Cortés, Nareli; Moreno-Armendáriz, Marco A.; Orantes-Jiménez, Sandra

The addition chains with minimal length are the basic block to the optimal computation of finite field exponentiations. It has very important applications in the areas of error-correcting codes and cryptography. However, obtaining the shortest addition chains for a given exponent is a NP-hard problem. In this work we propose the adaptation of a Particle Swarm Optimization algorithm to deal with this problem. Our proposal is tested on several exponents whose addition chains are considered hard to find. We obtained very promising results.

Emergence of Swarming Behavior: Foraging Agents Evolve Collective Motion Based on Signaling.

PubMed

Witkowski, Olaf; Ikegami, Takashi

2016-01-01

Swarming behavior is common in biology, from cell colonies to insect swarms and bird flocks. However, the conditions leading to the emergence of such behavior are still subject to research. Since Reynolds' boids, many artificial models have reproduced swarming behavior, focusing on details ranging from obstacle avoidance to the introduction of fixed leaders. This paper presents a model of evolved artificial agents, able to develop swarming using only their ability to listen to each other's signals. The model simulates a population of agents looking for a vital resource they cannot directly detect, in a 3D environment. Instead of a centralized algorithm, each agent is controlled by an artificial neural network, whose weights are encoded in a genotype and adapted by an original asynchronous genetic algorithm. The results demonstrate that agents progressively evolve the ability to use the information exchanged between each other via signaling to establish temporary leader-follower relations. These relations allow agents to form swarming patterns, emerging as a transient behavior that improves the agents' ability to forage for the resource. Once they have acquired the ability to swarm, the individuals are able to outperform the non-swarmers at finding the resource. The population hence reaches a neutral evolutionary space which leads to a genetic drift of the genotypes. This reductionist approach to signal-based swarming not only contributes to shed light on the minimal conditions for the evolution of a swarming behavior, but also more generally it exemplifies the effect communication can have on optimal search patterns in collective groups of individuals.

Markerless human motion tracking using hierarchical multi-swarm cooperative particle swarm optimization.

PubMed

Saini, Sanjay; Zakaria, Nordin; Rambli, Dayang Rohaya Awang; Sulaiman, Suziah

2015-01-01

The high-dimensional search space involved in markerless full-body articulated human motion tracking from multiple-views video sequences has led to a number of solutions based on metaheuristics, the most recent form of which is Particle Swarm Optimization (PSO). However, the classical PSO suffers from premature convergence and it is trapped easily into local optima, significantly affecting the tracking accuracy. To overcome these drawbacks, we have developed a method for the problem based on Hierarchical Multi-Swarm Cooperative Particle Swarm Optimization (H-MCPSO). The tracking problem is formulated as a non-linear 34-dimensional function optimization problem where the fitness function quantifies the difference between the observed image and a projection of the model configuration. Both the silhouette and edge likelihoods are used in the fitness function. Experiments using Brown and HumanEva-II dataset demonstrated that H-MCPSO performance is better than two leading alternative approaches-Annealed Particle Filter (APF) and Hierarchical Particle Swarm Optimization (HPSO). Further, the proposed tracking method is capable of automatic initialization and self-recovery from temporary tracking failures. Comprehensive experimental results are presented to support the claims.

A crab swarm at an ecological hotspot: patchiness and population density from AUV observations at a coastal, tropical seamount.

PubMed

Pineda, Jesús; Cho, Walter; Starczak, Victoria; Govindarajan, Annette F; Guzman, Héctor M; Girdhar, Yogesh; Holleman, Rusty C; Churchill, James; Singh, Hanumant; Ralston, David K

2016-01-01

A research cruise to Hannibal Bank, a seamount and an ecological hotspot in the coastal eastern tropical Pacific Ocean off Panama, explored the zonation, biodiversity, and the ecological processes that contribute to the seamount's elevated biomass. Here we describe the spatial structure of a benthic anomuran red crab population, using submarine video and autonomous underwater vehicle (AUV) photographs. High density aggregations and a swarm of red crabs were associated with a dense turbid layer 4-10 m above the bottom. The high density aggregations were constrained to 355-385 m water depth over the Northwest flank of the seamount, although the crabs also occurred at lower densities in shallower waters (∼280 m) and in another location of the seamount. The crab aggregations occurred in hypoxic water, with oxygen levels of 0.04 ml/l. Barcoding of Hannibal red crabs, and pelagic red crabs sampled in a mass stranding event in 2015 at a beach in San Diego, California, USA, revealed that the Panamanian and the Californian crabs are likely the same species, Pleuroncodes planipes, and these findings represent an extension of the southern endrange of this species. Measurements along a 1.6 km transect revealed three high density aggregations, with the highest density up to 78 crabs/m(2), and that the crabs were patchily distributed. Crab density peaked in the middle of the patch, a density structure similar to that of swarming insects.

A crab swarm at an ecological hotspot: patchiness and population density from AUV observations at a coastal, tropical seamount

PubMed Central

Cho, Walter; Starczak, Victoria; Govindarajan, Annette F.; Guzman, Héctor M.; Girdhar, Yogesh; Holleman, Rusty C.; Churchill, James; Singh, Hanumant; Ralston, David K.

2016-01-01

A research cruise to Hannibal Bank, a seamount and an ecological hotspot in the coastal eastern tropical Pacific Ocean off Panama, explored the zonation, biodiversity, and the ecological processes that contribute to the seamount’s elevated biomass. Here we describe the spatial structure of a benthic anomuran red crab population, using submarine video and autonomous underwater vehicle (AUV) photographs. High density aggregations and a swarm of red crabs were associated with a dense turbid layer 4–10 m above the bottom. The high density aggregations were constrained to 355–385 m water depth over the Northwest flank of the seamount, although the crabs also occurred at lower densities in shallower waters (∼280 m) and in another location of the seamount. The crab aggregations occurred in hypoxic water, with oxygen levels of 0.04 ml/l. Barcoding of Hannibal red crabs, and pelagic red crabs sampled in a mass stranding event in 2015 at a beach in San Diego, California, USA, revealed that the Panamanian and the Californian crabs are likely the same species, Pleuroncodes planipes, and these findings represent an extension of the southern endrange of this species. Measurements along a 1.6 km transect revealed three high density aggregations, with the highest density up to 78 crabs/m2, and that the crabs were patchily distributed. Crab density peaked in the middle of the patch, a density structure similar to that of swarming insects. PMID:27114859

Robust electromagnetically guided endoscopic procedure using enhanced particle swarm optimization for multimodal information fusion

DOE Office of Scientific and Technical Information (OSTI.GOV)

Luo, Xiongbiao, E-mail: xluo@robarts.ca, E-mail: Ying.Wan@student.uts.edu.au; Wan, Ying, E-mail: xluo@robarts.ca, E-mail: Ying.Wan@student.uts.edu.au; He, Xiangjian

Purpose: Electromagnetically guided endoscopic procedure, which aims at accurately and robustly localizing the endoscope, involves multimodal sensory information during interventions. However, it still remains challenging in how to integrate these information for precise and stable endoscopic guidance. To tackle such a challenge, this paper proposes a new framework on the basis of an enhanced particle swarm optimization method to effectively fuse these information for accurate and continuous endoscope localization. Methods: The authors use the particle swarm optimization method, which is one of stochastic evolutionary computation algorithms, to effectively fuse the multimodal information including preoperative information (i.e., computed tomography images) asmore » a frame of reference, endoscopic camera videos, and positional sensor measurements (i.e., electromagnetic sensor outputs). Since the evolutionary computation method usually limits its possible premature convergence and evolutionary factors, the authors introduce the current (endoscopic camera and electromagnetic sensor’s) observation to boost the particle swarm optimization and also adaptively update evolutionary parameters in accordance with spatial constraints and the current observation, resulting in advantageous performance in the enhanced algorithm. Results: The experimental results demonstrate that the authors’ proposed method provides a more accurate and robust endoscopic guidance framework than state-of-the-art methods. The average guidance accuracy of the authors’ framework was about 3.0 mm and 5.6° while the previous methods show at least 3.9 mm and 7.0°. The average position and orientation smoothness of their method was 1.0 mm and 1.6°, which is significantly better than the other methods at least with (2.0 mm and 2.6°). Additionally, the average visual quality of the endoscopic guidance was improved to 0.29. Conclusions: A robust electromagnetically guided endoscopy framework was proposed on the basis of an enhanced particle swarm optimization method with using the current observation information and adaptive evolutionary factors. The authors proposed framework greatly reduced the guidance errors from (4.3, 7.8) to (3.0 mm, 5.6°), compared to state-of-the-art methods.« less

Data acquisition and path selection decision making for an autonomous roving vehicle

NASA Technical Reports Server (NTRS)

Frederick, D. K.; Shen, C. N.; Yerazunis, S. W.

1976-01-01

Problems related to the guidance of an autonomous rover for unmanned planetary exploration were investigated. Topics included in these studies were: simulation on an interactive graphics computer system of the Rapid Estimation Technique for detection of discrete obstacles; incorporation of a simultaneous Bayesian estimate of states and inputs in the Rapid Estimation Scheme; development of methods for estimating actual laser rangefinder errors and their application to date provided by Jet Propulsion Laboratory; and modification of a path selection system simulation computer code for evaluation of a hazard detection system based on laser rangefinder data.

Behavior-based multi-robot collaboration for autonomous construction tasks

NASA Technical Reports Server (NTRS)

Stroupe, Ashley; Huntsberger, Terry; Okon, Avi; Aghazarian, Hrand; Robinson, Matthew

2005-01-01

The Robot Construction Crew (RCC) is a heterogeneous multi-robot system for autonomous construction of a structure through assembly of Long components. The two robot team demonstrates component placement into an existing structure in a realistic environment. The task requires component acquisition, cooperative transport, and cooperative precision manipulation. A behavior-based architecture provides adaptability. The RCC approach minimizes computation, power, communication, and sensing for applicability to space-related construction efforts, but the techniques are applicable to terrestrial construction tasks.

Behavior-Based Multi-Robot Collaboration for Autonomous Construction Tasks

NASA Technical Reports Server (NTRS)

Stroupe, Ashley; Huntsberger, Terry; Okon, Avi; Aghazarian, Hrand; Robinson, Matthew

2005-01-01

We present a heterogeneous multi-robot system for autonomous construction of a structure through assembly of long components. Placement of a component within an existing structure in a realistic environment is demonstrated on a two-robot team. The task requires component acquisition, cooperative transport, and cooperative precision manipulation. Far adaptability, the system is designed as a behavior-based architecture. Far applicability to space-related construction efforts, computation, power, communication, and sensing are minimized, though the techniques developed are also applicable to terrestrial construction tasks.

Fuzzy Behavior Modulation with Threshold Activation for Autonomous Vehicle Navigation

NASA Technical Reports Server (NTRS)

Tunstel, Edward

2000-01-01

This paper describes fuzzy logic techniques used in a hierarchical behavior-based architecture for robot navigation. An architectural feature for threshold activation of fuzzy-behaviors is emphasized, which is potentially useful for tuning navigation performance in real world applications. The target application is autonomous local navigation of a small planetary rover. Threshold activation of low-level navigation behaviors is the primary focus. A preliminary assessment of its impact on local navigation performance is provided based on computer simulations.

Evolving self-assembly in autonomous homogeneous robots: experiments with two physical robots.

PubMed

Ampatzis, Christos; Tuci, Elio; Trianni, Vito; Christensen, Anders Lyhne; Dorigo, Marco

2009-01-01

This research work illustrates an approach to the design of controllers for self-assembling robots in which the self-assembly is initiated and regulated by perceptual cues that are brought forth by the physical robots through their dynamical interactions. More specifically, we present a homogeneous control system that can achieve assembly between two modules (two fully autonomous robots) of a mobile self-reconfigurable system without a priori introduced behavioral or morphological heterogeneities. The controllers are dynamic neural networks evolved in simulation that directly control all the actuators of the two robots. The neurocontrollers cause the dynamic specialization of the robots by allocating roles between them based solely on their interaction. We show that the best evolved controller proves to be successful when tested on a real hardware platform, the swarm-bot. The performance achieved is similar to the one achieved by existing modular or behavior-based approaches, also due to the effect of an emergent recovery mechanism that was neither explicitly rewarded by the fitness function, nor observed during the evolutionary simulation. Our results suggest that direct access to the orientations or intentions of the other agents is not a necessary condition for robot coordination: Our robots coordinate without direct or explicit communication, contrary to what is assumed by most research works in collective robotics. This work also contributes to strengthening the evidence that evolutionary robotics is a design methodology that can tackle real-world tasks demanding fine sensory-motor coordination.

An improved swarm optimization for parameter estimation and biological model selection.

PubMed

Abdullah, Afnizanfaizal; Deris, Safaai; Mohamad, Mohd Saberi; Anwar, Sohail

2013-01-01

One of the key aspects of computational systems biology is the investigation on the dynamic biological processes within cells. Computational models are often required to elucidate the mechanisms and principles driving the processes because of the nonlinearity and complexity. The models usually incorporate a set of parameters that signify the physical properties of the actual biological systems. In most cases, these parameters are estimated by fitting the model outputs with the corresponding experimental data. However, this is a challenging task because the available experimental data are frequently noisy and incomplete. In this paper, a new hybrid optimization method is proposed to estimate these parameters from the noisy and incomplete experimental data. The proposed method, called Swarm-based Chemical Reaction Optimization, integrates the evolutionary searching strategy employed by the Chemical Reaction Optimization, into the neighbouring searching strategy of the Firefly Algorithm method. The effectiveness of the method was evaluated using a simulated nonlinear model and two biological models: synthetic transcriptional oscillators, and extracellular protease production models. The results showed that the accuracy and computational speed of the proposed method were better than the existing Differential Evolution, Firefly Algorithm and Chemical Reaction Optimization methods. The reliability of the estimated parameters was statistically validated, which suggests that the model outputs produced by these parameters were valid even when noisy and incomplete experimental data were used. Additionally, Akaike Information Criterion was employed to evaluate the model selection, which highlighted the capability of the proposed method in choosing a plausible model based on the experimental data. In conclusion, this paper presents the effectiveness of the proposed method for parameter estimation and model selection problems using noisy and incomplete experimental data. This study is hoped to provide a new insight in developing more accurate and reliable biological models based on limited and low quality experimental data.

A comparison between metaheuristics as strategies for minimizing cyclic instability in Ambient Intelligence.

PubMed

Romero, Leoncio A; Zamudio, Victor; Baltazar, Rosario; Mezura, Efren; Sotelo, Marco; Callaghan, Vic

2012-01-01

In this paper we present a comparison between six novel approaches to the fundamental problem of cyclic instability in Ambient Intelligence. These approaches are based on different optimization algorithms, Particle Swarm Optimization (PSO), Bee Swarm Optimization (BSO), micro Particle Swarm Optimization (μ-PSO), Artificial Immune System (AIS), Genetic Algorithm (GA) and Mutual Information Maximization for Input Clustering (MIMIC). In order to be able to use these algorithms, we introduced the concept of Average Cumulative Oscillation (ACO), which enabled us to measure the average behavior of the system. This approach has the advantage that it does not need to analyze the topological properties of the system, in particular the loops, which can be computationally expensive. In order to test these algorithms we used the well-known discrete system called the Game of Life for 9, 25, 49 and 289 agents. It was found that PSO and μ-PSO have the best performance in terms of the number of agents locked. These results were confirmed using the Wilcoxon Signed Rank Test. This novel and successful approach is very promising and can be used to remove instabilities in real scenarios with a large number of agents (including nomadic agents) and complex interactions and dependencies among them.

An Adaptive Image Enhancement Technique by Combining Cuckoo Search and Particle Swarm Optimization Algorithm

PubMed Central

Ye, Zhiwei; Wang, Mingwei; Hu, Zhengbing; Liu, Wei

2015-01-01

Image enhancement is an important procedure of image processing and analysis. This paper presents a new technique using a modified measure and blending of cuckoo search and particle swarm optimization (CS-PSO) for low contrast images to enhance image adaptively. In this way, contrast enhancement is obtained by global transformation of the input intensities; it employs incomplete Beta function as the transformation function and a novel criterion for measuring image quality considering three factors which are threshold, entropy value, and gray-level probability density of the image. The enhancement process is a nonlinear optimization problem with several constraints. CS-PSO is utilized to maximize the objective fitness criterion in order to enhance the contrast and detail in an image by adapting the parameters of a novel extension to a local enhancement technique. The performance of the proposed method has been compared with other existing techniques such as linear contrast stretching, histogram equalization, and evolutionary computing based image enhancement methods like backtracking search algorithm, differential search algorithm, genetic algorithm, and particle swarm optimization in terms of processing time and image quality. Experimental results demonstrate that the proposed method is robust and adaptive and exhibits the better performance than other methods involved in the paper. PMID:25784928

An adaptive image enhancement technique by combining cuckoo search and particle swarm optimization algorithm.

PubMed

Ye, Zhiwei; Wang, Mingwei; Hu, Zhengbing; Liu, Wei

2015-01-01

Image enhancement is an important procedure of image processing and analysis. This paper presents a new technique using a modified measure and blending of cuckoo search and particle swarm optimization (CS-PSO) for low contrast images to enhance image adaptively. In this way, contrast enhancement is obtained by global transformation of the input intensities; it employs incomplete Beta function as the transformation function and a novel criterion for measuring image quality considering three factors which are threshold, entropy value, and gray-level probability density of the image. The enhancement process is a nonlinear optimization problem with several constraints. CS-PSO is utilized to maximize the objective fitness criterion in order to enhance the contrast and detail in an image by adapting the parameters of a novel extension to a local enhancement technique. The performance of the proposed method has been compared with other existing techniques such as linear contrast stretching, histogram equalization, and evolutionary computing based image enhancement methods like backtracking search algorithm, differential search algorithm, genetic algorithm, and particle swarm optimization in terms of processing time and image quality. Experimental results demonstrate that the proposed method is robust and adaptive and exhibits the better performance than other methods involved in the paper.

A Comparison between Metaheuristics as Strategies for Minimizing Cyclic Instability in Ambient Intelligence

PubMed Central

Romero, Leoncio A.; Zamudio, Victor; Baltazar, Rosario; Mezura, Efren; Sotelo, Marco; Callaghan, Vic

2012-01-01

In this paper we present a comparison between six novel approaches to the fundamental problem of cyclic instability in Ambient Intelligence. These approaches are based on different optimization algorithms, Particle Swarm Optimization (PSO), Bee Swarm Optimization (BSO), micro Particle Swarm Optimization (μ-PSO), Artificial Immune System (AIS), Genetic Algorithm (GA) and Mutual Information Maximization for Input Clustering (MIMIC). In order to be able to use these algorithms, we introduced the concept of Average Cumulative Oscillation (ACO), which enabled us to measure the average behavior of the system. This approach has the advantage that it does not need to analyze the topological properties of the system, in particular the loops, which can be computationally expensive. In order to test these algorithms we used the well-known discrete system called the Game of Life for 9, 25, 49 and 289 agents. It was found that PSO and μ-PSO have the best performance in terms of the number of agents locked. These results were confirmed using the Wilcoxon Signed Rank Test. This novel and successful approach is very promising and can be used to remove instabilities in real scenarios with a large number of agents (including nomadic agents) and complex interactions and dependencies among them. PMID:23112643

Quantum-behaved particle swarm optimization for the synthesis of fibre Bragg gratings filter

NASA Astrophysics Data System (ADS)

Yu, Xuelian; Sun, Yunxu; Yao, Yong; Tian, Jiajun; Cong, Shan

2011-12-01

A method based on the quantum-behaved particle swarm optimization algorithm is presented to design a bandpass filter of the fibre Bragg gratings. In contrast to the other optimization algorithms such as the genetic algorithm and particle swarm optimization algorithm, this method is simpler and easier to implement. To demonstrate the effectiveness of the QPSO algorithm, we consider a bandpass filter. With the parameters the half the bandwidth of the filter 0.05 nm, the Bragg wavelength 1550 nm, the grating length with 2cm is divided into 40 uniform sections and its index modulation is what should be optimized and whole feasible solution space is searched for the index modulation. After the index modulation profile is known for all the sections, the transfer matrix method is used to verify the final optimal index modulation by calculating the refection spectrum. The results show the group delay is less than 12ps in band and the calculated dispersion is relatively flat inside the passband. It is further found that the reflective spectrum has sidelobes around -30dB and the worst in-band dispersion value is less than 200ps/nm . In addition, for this design, it takes approximately several minutes to find the acceptable index modulation values with a notebook computer.

AltiVec performance increases for autonomous robotics for the MARSSCAPE architecture program

NASA Astrophysics Data System (ADS)

Gothard, Benny M.

2002-02-01

One of the main tall poles that must be overcome to develop a fully autonomous vehicle is the inability of the computer to understand its surrounding environment to a level that is required for the intended task. The military mission scenario requires a robot to interact in a complex, unstructured, dynamic environment. Reference A High Fidelity Multi-Sensor Scene Understanding System for Autonomous Navigation The Mobile Autonomous Robot Software Self Composing Adaptive Programming Environment (MarsScape) perception research addresses three aspects of the problem; sensor system design, processing architectures, and algorithm enhancements. A prototype perception system has been demonstrated on robotic High Mobility Multi-purpose Wheeled Vehicle and All Terrain Vehicle testbeds. This paper addresses the tall pole of processing requirements and the performance improvements based on the selected MarsScape Processing Architecture. The processor chosen is the Motorola Altivec-G4 Power PC(PPC) (1998 Motorola, Inc.), a highly parallized commercial Single Instruction Multiple Data processor. Both derived perception benchmarks and actual perception subsystems code will be benchmarked and compared against previous Demo II-Semi-autonomous Surrogate Vehicle processing architectures along with desktop Personal Computers(PC). Performance gains are highlighted with progress to date, and lessons learned and future directions are described.

Multiscale modelling and analysis of collective decision making in swarm robotics.

PubMed

Vigelius, Matthias; Meyer, Bernd; Pascoe, Geoffrey

2014-01-01

We present a unified approach to describing certain types of collective decision making in swarm robotics that bridges from a microscopic individual-based description to aggregate properties. Our approach encompasses robot swarm experiments, microscopic and probabilistic macroscopic-discrete simulations as well as an analytic mathematical model. Following up on previous work, we identify the symmetry parameter, a measure of the progress of the swarm towards a decision, as a fundamental integrated swarm property and formulate its time evolution as a continuous-time Markov process. Contrary to previous work, which justified this approach only empirically and a posteriori, we justify it from first principles and derive hard limits on the parameter regime in which it is applicable.

Autonomic Cluster Management System (ACMS): A Demonstration of Autonomic Principles at Work

NASA Technical Reports Server (NTRS)

Baldassari, James D.; Kopec, Christopher L.; Leshay, Eric S.; Truszkowski, Walt; Finkel, David

2005-01-01

Cluster computing, whereby a large number of simple processors or nodes are combined together to apparently function as a single powerful computer, has emerged as a research area in its own right. The approach offers a relatively inexpensive means of achieving significant computational capabilities for high-performance computing applications, while simultaneously affording the ability to. increase that capability simply by adding more (inexpensive) processors. However, the task of manually managing and con.guring a cluster quickly becomes impossible as the cluster grows in size. Autonomic computing is a relatively new approach to managing complex systems that can potentially solve many of the problems inherent in cluster management. We describe the development of a prototype Automatic Cluster Management System (ACMS) that exploits autonomic properties in automating cluster management.

Autonomic Closure for Turbulent Flows Using Approximate Bayesian Computation

NASA Astrophysics Data System (ADS)

Doronina, Olga; Christopher, Jason; Hamlington, Peter; Dahm, Werner

2017-11-01

Autonomic closure is a new technique for achieving fully adaptive and physically accurate closure of coarse-grained turbulent flow governing equations, such as those solved in large eddy simulations (LES). Although autonomic closure has been shown in recent a priori tests to more accurately represent unclosed terms than do dynamic versions of traditional LES models, the computational cost of the approach makes it challenging to implement for simulations of practical turbulent flows at realistically high Reynolds numbers. The optimization step used in the approach introduces large matrices that must be inverted and is highly memory intensive. In order to reduce memory requirements, here we propose to use approximate Bayesian computation (ABC) in place of the optimization step, thereby yielding a computationally-efficient implementation of autonomic closure that trades memory-intensive for processor-intensive computations. The latter challenge can be overcome as co-processors such as general purpose graphical processing units become increasingly available on current generation petascale and exascale supercomputers. In this work, we outline the formulation of ABC-enabled autonomic closure and present initial results demonstrating the accuracy and computational cost of the approach.

SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware

PubMed Central

Birgiolas, Justas; Jernigan, Christopher M.; Gerkin, Richard C.; Smith, Brian H.; Crook, Sharon M.

2017-01-01

Many scientifically and agriculturally important insects use antennae to detect the presence of volatile chemical compounds and extend their proboscis during feeding. The ability to rapidly obtain high-resolution measurements of natural antenna and proboscis movements and assess how they change in response to chemical, developmental, and genetic manipulations can aid the understanding of insect behavior. By extending our previous work on assessing aggregate insect swarm or animal group movements from natural and laboratory videos using the video analysis software SwarmSight, we developed a novel, free, and open-source software module, SwarmSight Appendage Tracking (SwarmSight.org) for frame-by-frame tracking of insect antenna and proboscis positions from conventional web camera videos using conventional computers. The software processes frames about 120 times faster than humans, performs at better than human accuracy, and, using 30 frames per second (fps) videos, can capture antennal dynamics up to 15 Hz. The software was used to track the antennal response of honey bees to two odors and found significant mean antennal retractions away from the odor source about 1 s after odor presentation. We observed antenna position density heat map cluster formation and cluster and mean angle dependence on odor concentration. PMID:29364251

Autonomous Systems, Robotics, and Computing Systems Capability Roadmap: NRC Dialogue

NASA Technical Reports Server (NTRS)

Zornetzer, Steve; Gage, Douglas

2005-01-01

Contents include the following: Introduction. Process, Mission Drivers, Deliverables, and Interfaces. Autonomy. Crew-Centered and Remote Operations. Integrated Systems Health Management. Autonomous Vehicle Control. Autonomous Process Control. Robotics. Robotics for Solar System Exploration. Robotics for Lunar and Planetary Habitation. Robotics for In-Space Operations. Computing Systems. Conclusion.

Where neuroscience and dynamic system theory meet autonomous robotics: a contracting basal ganglia model for action selection.

PubMed

Girard, B; Tabareau, N; Pham, Q C; Berthoz, A; Slotine, J-J

2008-05-01

Action selection, the problem of choosing what to do next, is central to any autonomous agent architecture. We use here a multi-disciplinary approach at the convergence of neuroscience, dynamical system theory and autonomous robotics, in order to propose an efficient action selection mechanism based on a new model of the basal ganglia. We first describe new developments of contraction theory regarding locally projected dynamical systems. We exploit these results to design a stable computational model of the cortico-baso-thalamo-cortical loops. Based on recent anatomical data, we include usually neglected neural projections, which participate in performing accurate selection. Finally, the efficiency of this model as an autonomous robot action selection mechanism is assessed in a standard survival task. The model exhibits valuable dithering avoidance and energy-saving properties, when compared with a simple if-then-else decision rule.

Adaptive Remote-Sensing Techniques Implementing Swarms of Mobile Agents

DOE Office of Scientific and Technical Information (OSTI.GOV)

Asher, R.B.; Cameron, S.M.; Loubriel, G.M.

1998-11-25

In many situations, stand-off remote-sensing and hazard-interdiction techniques over realistic operational areas are often impractical "and difficult to characterize. An alternative approach is to implement an adap- tively deployable array of sensitive agent-specific devices. Our group has been studying the collective be- havior of an autonomous, multi-agent system applied to chedbio detection and related emerging threat applications, The current physics-based models we are using coordinate a sensor array for mukivanate sig- nal optimization and coverage as re,alized by a swarm of robots or mobile vehicles. These intelligent control systems integrate'glob"ally operating decision-making systems and locally cooperative learning neural net- worksmore » to enhance re+-timp operational responses to dynarnical environments examples of which include obstacle avoidance, res~onding to prevailing wind patterns, and overcoming other natural obscurants or in- terferences. Collectively',tkensor nefirons with simple properties, interacting according to basic community rules, can accomplish complex interconnecting functions such as generalization, error correction, pattern recognition, sensor fusion, and localization. Neural nets provide a greater degree of robusmess and fault tolerance than conventional systems in that minor variations or imperfections do not impair performance. The robotic platforms would be equipped with sensor devices that perform opticaI detection of biologicais in combination with multivariate chemical analysis tools based on genetic and neural network algorithms, laser-diode LIDAR analysis, ultra-wideband short-pulsed transmitting and receiving antennas, thermal im- a:ing sensors, and optical Communication technology providing robust data throughput pathways. Mission scenarios under consideration include ground penetrating radar (GPR) for detection of underground struc- tures, airborne systems, and plume migration and mitigation. We will describe our research in these areas anti give a status report on our progress.« less

A dynamic feedforward neural network based on gaussian particle swarm optimization and its application for predictive control.

PubMed

Han, Min; Fan, Jianchao; Wang, Jun

2011-09-01

A dynamic feedforward neural network (DFNN) is proposed for predictive control, whose adaptive parameters are adjusted by using Gaussian particle swarm optimization (GPSO) in the training process. Adaptive time-delay operators are added in the DFNN to improve its generalization for poorly known nonlinear dynamic systems with long time delays. Furthermore, GPSO adopts a chaotic map with Gaussian function to balance the exploration and exploitation capabilities of particles, which improves the computational efficiency without compromising the performance of the DFNN. The stability of the particle dynamics is analyzed, based on the robust stability theory, without any restrictive assumption. A stability condition for the GPSO+DFNN model is derived, which ensures a satisfactory global search and quick convergence, without the need for gradients. The particle velocity ranges could change adaptively during the optimization process. The results of a comparative study show that the performance of the proposed algorithm can compete with selected algorithms on benchmark problems. Additional simulation results demonstrate the effectiveness and accuracy of the proposed combination algorithm in identifying and controlling nonlinear systems with long time delays.

Characteristics of Swarm Seismicity in Northern California

NASA Astrophysics Data System (ADS)

Chiorini, S.; Lekic, V.

2017-12-01

Seismic swarms are characterized by an anomalously large number of earthquakes compared to the background rate of seismicity that are tightly clustered in space (typically, one to tens of kilometers) and time (typically, days to weeks). However, why and how swarms occur is poorly understood, partly because of the difficulty of identifying the range of swarm behaviors within large seismic catalogs. Previous studies have found that swarms, compared to other earthquake sequences, appear to be more common in extensional (Vidale & Shearer, 2006) and volcanic settings (Hayashi & Morita, 2003). In addition, swarms more commonly exhibit migration patterns, consistent with either fluid diffusion (Chen & Shearer, 2011; Chen et al., 2012) or aseismic creep (Lohman & McGuire, 2007), and are preferentially found in areas of enhanced heat flow (Enescu, 2009; Zaliapin & Ben Zion, 2016). While the swarm seismicity of Southern California has been studied extensively, that of Northern California has not been systematically documented and characterized. We employed two complementary methods of swarm identification: the approach of Vidale and Shearer (2006; henceforth VS2006) based on a priori threshold distances and timings of quakes, and the spatio-temporal distance metric proposed by Zaliapin et al. (2008; henceforth Z2008) in order to build a complete catalog of swarm seismicity in Northern California spanning 1984-2016 (Waldhauser & Schaff, 2008). Once filtered for aftershocks, the catalog allows us to describe the main features of swarm seismicity in Northern California, including spatial distribution, association or lack thereof with known faults and volcanic systems, and seismically quiescent regions. We then apply a robust technique to characterize the morphology of swarms, leading to subsets of swarms that are oriented either vertically or horizontally in space. When mapped, vertical swarms show a significant association with volcanic regions, and horizontal swarms with tectonic and volcanic settings. Finally, we demonstrate some swarms underlie areas that are typically quiescent, like Redding and the Sutter Buttes. Our catalog presents a more complete snapshot of the diversity of swarm characteristics in Northern California, and motivates further study of their relationship to tectonic and volcanic processes.

Engineering Ultimate Self-Protection in Autonomic Agents for Space Exploration Missions

NASA Technical Reports Server (NTRS)

Sterritt, Roy; Hinchey, Mike

2005-01-01

NASA's Exploration Initiative (EI) will push space exploration missions to the limit. Future missions will be required to be self-managing as well as self-directed, in order to meet the challenges of human and robotic space exploration. We discuss security and self protection in autonomic agent based-systems, and propose the ultimate self-protection mechanism for such systems-self-destruction. Like other metaphors in Autonomic Computing, this is inspired by biological systems, and is the analog of biological apoptosis. Finally, we discus the role it might play in future NASA space exploration missions.

Small- and Large-scale Morphology of the Near-Earth Energetic Charged Particle Environment from a Ten-element CubeSat Constellation

NASA Astrophysics Data System (ADS)

Klumpar, D. M.; Gunderson, A.

2014-12-01

A 10-satellite constellation placed in Low Earth Orbit (LEO) will carry high geometric factor omnidirectional integrating energetic particle detectors responsive to electrons greater than ~500 keV to characterize the near-Earth distribution of Van Allen Belt electrons precipitating or mirroring at altitudes between ~350 and ~500 km. The full constellation will be constructed by two deployments of identical 1.5U CubeSats into LEO. The first launch will deploy eight satellites into a polar sun-synchronous orbit from the Island of Kauai in the Hawaiian Islands to form the NASA/Ames Research Center "Edison Demonstration of Smallsat Networks" (EDSN) swarm of satellites. The on-board Energetic Particle Integrating Space Environment Monitor (EPISEM) instrument built by the Space Science and Engineering Laboratory at Montana State University consists of a cylindrical 12 cm*2-ster omnidirectional Geiger counter sensitive to electrons above about 500 keV. The eight EDSN satellites are expected to deploy in late November 2014 into an 410 x 485 km orbit at ~92 degrees inclination forming two slowly-separating groups of four measurement platforms each to set up the initial 8-satellite swarm. Separately, two additional copies of the EDSN satellites will deploy from the International Space Station as elements of the NODES mission into a 52 degree inclination orbit at about 375 km altitude. Together the 10 satellites will characterize the distribution of low altitude penetrating electrons over spatial scales from 10's to thousands of km. The paper will describe the mission concept, the implementation of the spacecraft, and the unusual operations concept that allows stored science data to be collected from all eight satellites of the EDSN swarm through an intersatellite communications link and transferred to the ground by a single member of the swarm. The EDSN satellites operate completely autonomously without ground uplink. The paper will also include early scientific results if available by mid-December, 2014.

Research on particle swarm optimization algorithm based on optimal movement probability

NASA Astrophysics Data System (ADS)

Ma, Jianhong; Zhang, Han; He, Baofeng

2017-01-01

The particle swarm optimization algorithm to improve the control precision, and has great application value training neural network and fuzzy system control fields etc.The traditional particle swarm algorithm is used for the training of feed forward neural networks,the search efficiency is low, and easy to fall into local convergence.An improved particle swarm optimization algorithm is proposed based on error back propagation gradient descent. Particle swarm optimization for Solving Least Squares Problems to meme group, the particles in the fitness ranking, optimization problem of the overall consideration, the error back propagation gradient descent training BP neural network, particle to update the velocity and position according to their individual optimal and global optimization, make the particles more to the social optimal learning and less to its optimal learning, it can avoid the particles fall into local optimum, by using gradient information can accelerate the PSO local search ability, improve the multi beam particle swarm depth zero less trajectory information search efficiency, the realization of improved particle swarm optimization algorithm. Simulation results show that the algorithm in the initial stage of rapid convergence to the global optimal solution can be near to the global optimal solution and keep close to the trend, the algorithm has faster convergence speed and search performance in the same running time, it can improve the convergence speed of the algorithm, especially the later search efficiency.

A PSO-Based Hybrid Metaheuristic for Permutation Flowshop Scheduling Problems

PubMed Central

Zhang, Le; Wu, Jinnan

2014-01-01

This paper investigates the permutation flowshop scheduling problem (PFSP) with the objectives of minimizing the makespan and the total flowtime and proposes a hybrid metaheuristic based on the particle swarm optimization (PSO). To enhance the exploration ability of the hybrid metaheuristic, a simulated annealing hybrid with a stochastic variable neighborhood search is incorporated. To improve the search diversification of the hybrid metaheuristic, a solution replacement strategy based on the pathrelinking is presented to replace the particles that have been trapped in local optimum. Computational results on benchmark instances show that the proposed PSO-based hybrid metaheuristic is competitive with other powerful metaheuristics in the literature. PMID:24672389

A PSO-based hybrid metaheuristic for permutation flowshop scheduling problems.

PubMed

Zhang, Le; Wu, Jinnan

2014-01-01

This paper investigates the permutation flowshop scheduling problem (PFSP) with the objectives of minimizing the makespan and the total flowtime and proposes a hybrid metaheuristic based on the particle swarm optimization (PSO). To enhance the exploration ability of the hybrid metaheuristic, a simulated annealing hybrid with a stochastic variable neighborhood search is incorporated. To improve the search diversification of the hybrid metaheuristic, a solution replacement strategy based on the pathrelinking is presented to replace the particles that have been trapped in local optimum. Computational results on benchmark instances show that the proposed PSO-based hybrid metaheuristic is competitive with other powerful metaheuristics in the literature.

Cultural-based particle swarm for dynamic optimisation problems

NASA Astrophysics Data System (ADS)

Daneshyari, Moayed; Yen, Gary G.

2012-07-01

Many practical optimisation problems are with the existence of uncertainties, among which a significant number belong to the dynamic optimisation problem (DOP) category in which the fitness function changes through time. In this study, we propose the cultural-based particle swarm optimisation (PSO) to solve DOP problems. A cultural framework is adopted incorporating the required information from the PSO into five sections of the belief space, namely situational, temporal, domain, normative and spatial knowledge. The stored information will be adopted to detect the changes in the environment and assists response to the change through a diversity-based repulsion among particles and migration among swarms in the population space, and also helps in selecting the leading particles in three different levels, personal, swarm and global levels. Comparison of the proposed heuristics over several difficult dynamic benchmark problems demonstrates the better or equal performance with respect to most of other selected state-of-the-art dynamic PSO heuristics.

Observatory data and the Swarm mission

NASA Astrophysics Data System (ADS)

Macmillan, S.; Olsen, N.

2013-11-01

The ESA Swarm mission to identify and measure very accurately the different magnetic signals that arise in the Earth's core, mantle, crust, oceans, ionosphere and magnetosphere, which together form the magnetic field around the Earth, has increased interest in magnetic data collected on the surface of the Earth at observatories. The scientific use of Swarm data and Swarm-derived products is greatly enhanced by combination with observatory data and indices. As part of the Swarm Level-2 data activities plans are in place to distribute such ground-based data along with the Swarm data as auxiliary data products. We describe here the preparation of the data set of ground observatory hourly mean values, including procedures to check and select observatory data spanning the modern magnetic survey satellite era. We discuss other possible combined uses of satellite and observatory data, in particular those that may use higher cadence 1-second and 1-minute data from observatories.

Remote Agent Demonstration

NASA Technical Reports Server (NTRS)

Dorais, Gregory A.; Kurien, James; Rajan, Kanna

1999-01-01

We describe the computer demonstration of the Remote Agent Experiment (RAX). The Remote Agent is a high-level, model-based, autonomous control agent being validated on the NASA Deep Space 1 spacecraft.

Multiscale Modelling and Analysis of Collective Decision Making in Swarm Robotics

PubMed Central

Vigelius, Matthias; Meyer, Bernd; Pascoe, Geoffrey

2014-01-01

We present a unified approach to describing certain types of collective decision making in swarm robotics that bridges from a microscopic individual-based description to aggregate properties. Our approach encompasses robot swarm experiments, microscopic and probabilistic macroscopic-discrete simulations as well as an analytic mathematical model. Following up on previous work, we identify the symmetry parameter, a measure of the progress of the swarm towards a decision, as a fundamental integrated swarm property and formulate its time evolution as a continuous-time Markov process. Contrary to previous work, which justified this approach only empirically and a posteriori, we justify it from first principles and derive hard limits on the parameter regime in which it is applicable. PMID:25369026

Investigating the Use of Cloudbursts for High-Throughput Medical Image Registration

PubMed Central

Kim, Hyunjoo; Parashar, Manish; Foran, David J.; Yang, Lin

2010-01-01

This paper investigates the use of clouds and autonomic cloudbursting to support a medical image registration. The goal is to enable a virtual computational cloud that integrates local computational environments and public cloud services on-the-fly, and support image registration requests from different distributed researcher groups with varied computational requirements and QoS constraints. The virtual cloud essentially implements shared and coordinated task-spaces, which coordinates the scheduling of jobs submitted by a dynamic set of research groups to their local job queues. A policy-driven scheduling agent uses the QoS constraints along with performance history and the state of the resources to determine the appropriate size and mix of the public and private cloud resource that should be allocated to a specific request. The virtual computational cloud and the medical image registration service have been developed using the CometCloud engine and have been deployed on a combination of private clouds at Rutgers University and the Cancer Institute of New Jersey and Amazon EC2. An experimental evaluation is presented and demonstrates the effectiveness of autonomic cloudbursts and policy-based autonomic scheduling for this application. PMID:20640235

Applying Adaptive Swarm Intelligence Technology with Structuration in Web-Based Collaborative Learning

ERIC Educational Resources Information Center

Huang, Yueh-Min; Liu, Chien-Hung

2009-01-01

One of the key challenges in the promotion of web-based learning is the development of effective collaborative learning environments. We posit that the structuration process strongly influences the effectiveness of technology used in web-based collaborative learning activities. In this paper, we propose an ant swarm collaborative learning (ASCL)…

A Winner Determination Algorithm for Combinatorial Auctions Based on Hybrid Artificial Fish Swarm Algorithm

NASA Astrophysics Data System (ADS)

Zheng, Genrang; Lin, ZhengChun

The problem of winner determination in combinatorial auctions is a hotspot electronic business, and a NP hard problem. A Hybrid Artificial Fish Swarm Algorithm(HAFSA), which is combined with First Suite Heuristic Algorithm (FSHA) and Artificial Fish Swarm Algorithm (AFSA), is proposed to solve the problem after probing it base on the theories of AFSA. Experiment results show that the HAFSA is a rapidly and efficient algorithm for The problem of winner determining. Compared with Ant colony Optimization Algorithm, it has a good performance with broad and prosperous application.

Embodied Computation: An Active-Learning Approach to Mobile Robotics Education

ERIC Educational Resources Information Center

Riek, L. D.

2013-01-01

This paper describes a newly designed upper-level undergraduate and graduate course, Autonomous Mobile Robots. The course employs active, cooperative, problem-based learning and is grounded in the fundamental computational problems in mobile robotics defined by Dudek and Jenkin. Students receive a broad survey of robotics through lectures, weekly…

Autonomic and Coevolutionary Sensor Networking

NASA Astrophysics Data System (ADS)

Boonma, Pruet; Suzuki, Junichi

(WSNs) applications are often required to balance the tradeoffs among conflicting operational objectives (e.g., latency and power consumption) and operate at an optimal tradeoff. This chapter proposes and evaluates a architecture, called BiSNET/e, which allows WSN applications to overcome this issue. BiSNET/e is designed to support three major types of WSN applications: , and hybrid applications. Each application is implemented as a decentralized group of, which is analogous to a bee colony (application) consisting of bees (agents). Agents collect sensor data or detect an event (a significant change in sensor reading) on individual nodes, and carry sensor data to base stations. They perform these data collection and event detection functionalities by sensing their surrounding network conditions and adaptively invoking behaviors such as pheromone emission, reproduction, migration, swarming and death. Each agent has its own behavior policy, as a set of genes, which defines how to invoke its behaviors. BiSNET/e allows agents to evolve their behavior policies (genes) across generations and autonomously adapt their performance to given objectives. Simulation results demonstrate that, in all three types of applications, agents evolve to find optimal tradeoffs among conflicting objectives and adapt to dynamic network conditions such as traffic fluctuations and node failures/additions. Simulation results also illustrate that, in hybrid applications, data collection agents and event detection agents coevolve to augment their adaptability and performance.

Particle Swarm Transport across the Fracture-Matrix Interface

NASA Astrophysics Data System (ADS)

Malenda, M. G.; Pyrak-Nolte, L. J.

2016-12-01

A fundamental understanding of particle transport is required for many diverse applications such as effective proppant injection, for deployment of subsurface imaging micro-particles, and for removal of particulate contaminants from subsurface water systems. One method of particulate transport is the use of particle swarms that act as coherent entities. Previous work found that particle swarms travel farther and faster in single fractures than individual particles when compared to dispersions and emulsions. In this study, gravity-driven experiments were performed to characterize swarm transport across the fracture-matrix interface. Synthetic porous media with a horizontal fracture were created from layers of square-packed 3D printed (PMMA) spherical grains (12 mm diameter). The minimum fracture aperture ranged from 0 - 10 mm. Swarms (5 and 25 µL) were composed of 3.2 micron diameter fluorescent polystryene beads (1-2% by mass). Swarms were released into a fractured porous medium that was submerged in water and was illuminated with a green (528 nm) LED array. Descending swarms were imaged with a CCD camera (2 fps). Whether an intact swarm was transported across a fracture depended on the volume of the swarm, the aperture of the fracture, and the alignment of pores on the two fracture walls. Large aperture fractures caused significant deceleration of a swarm because the swarm was free to expand laterally in the fracture. Swarms tended to remain intact when the pores on the two fracture walls were vertically aligned and traveled in the lower porous medium with speeds that were 30%-50% of their original speed in the upper matrix. When the pores on opposing walls were no longer aligned, swarms were observed to bifurcate around the grain into two smaller slower-moving swarms. Understanding the physics of particle swarms in fractured porous media has important implications for enhancing target particulate injection into the subsurface as well as for contaminant particulate transport. Acknowledgment: This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Geosciences Research Program under Award Number (DE-FG02-09ER16022) and by National Science Foundation REU program under Award Number (PHY-1460899) at Purdue University.

New evidence of mating swarms of the malaria vector, Anopheles arabiensis in Tanzania

PubMed Central

Kaindoa, Emmanuel W.; Ngowo, Halfan S.; Limwagu, Alex; Mkandawile, Gustav; Kihonda, Japhet; Masalu, John Paliga; Bwanary, Hamis; Diabate, Abdoulaye; Okumu, Fredros O.

2017-01-01

Background: Malaria mosquitoes form mating swarms around sunset, often at the same locations for months or years. Unfortunately, studies of Anopheles swarms are rare in East Africa, the last recorded field observations in Tanzania having been in 1983. Methods: Mosquito swarms were surveyed by trained volunteers between August-2016 and June-2017 in Ulanga district, Tanzania. Identified Anopheles swarms were sampled using sweep nets, and collected mosquitoes killed by refrigeration then identified by sex and taxa. Sub-samples were further identified by PCR, and spermatheca of females examined for mating status. Mosquito ages were estimated by observing female ovarian tracheoles and rotation of male genitalia. GPS locations, types of swarm markers, start/end times of swarming, heights above ground, mosquito counts/swarm, and copulation events were recorded. Results: A total of 216 Anopheles swarms were identified, characterized and mapped, from which 7,142 Anopheles gambiae s.l and 13 Anopheles funestus were sampled. The An. gambiae s.l were 99.6% males and 0.4% females, while the An. funestus were all males. Of all An. gambiae s.l analyzed by PCR, 86.7% were An. arabiensis, while 13.3% returned non-amplified DNA. Mean height (±SD) of swarms was 2.74±0.64m, and median duration was 20 (IQR; 15-25) minutes. Confirmed swarm markers included rice fields (25.5%), burned grounds (17.2%), banana trees (13%), brick piles (8.8%), garbage heaps (7.9%) and ant-hills (7.4%). Visual estimates of swarm sizes by the volunteers was strongly correlated to actual sizes by sweep nets (R=0.94; P=<0.001). All females examined were nulliparous and 95.6% [N=6787] of males had rotated genitalia, indicating sexual maturity. Conclusions: This is the first report of Anopheles swarms in Tanzania in more than three decades. The study demonstrates that the swarms can be identified and characterized by trained community-based volunteers, and highlights potential new interventions, for example targeted aerosol spraying of the swarms to improve malaria control. PMID:29184918

Geodynamic Setting of Proterozoic Dyke Swarms of the Leo-Man Craton, West Africa, Based on New U-Pb Dating and Geochemistry

NASA Astrophysics Data System (ADS)

Baratoux, L.; Jessell, M.; Söderlund, U.; Ernst, R. E.; Benoit, M.; Naba, S.; Cournede, C.; Perrouty, S.; Metelka, V.; Yatte, D.; Diallo, D. P.; Ndiaye, P. M.; Dioh, E.; Baratoux, D.

2016-12-01

Over 20 sets of dolerite dykes crosscutting Paleoproterozoic basement in West Africa were distinguished via the interpretation of regional and high-resolution airborne magnetic data available over the West African Craton. Some of the dykes reach over 300 km in length and are considered parts of much larger systems of mafic dyke swarms which form the plumbing system of Large Igneous Provinces (LIPs). Five different dyke swarms in Burkina Faso, Niger, Ghana and Senegal were investigated. In terms of petrography and composition, the mafic dykes correspond to tholeiitic basalts and are typically composed of plagioclase + clinopyroxene ± orthopyroxene ± olivine. They display a doleritic texture of variable grain size. Eleven ID-TIMS U-Pb ages obtained on baddeleyite define five generations of Proterozoic age. The N10 Libiri dyke swarm, found in western Niger, yielded an age of ca. 1790 Ma. The N40 Bassari swarm in Senegal was dated at ca. 1764 Ma, and is potentially linked to the 1790 Ma Libiri swarm, 1400 km away. The 300 by 400 km Korsimoro N100 dyke swarm transects central Burkina Faso and was dated at ca. 1575 Ma. Five ca. 1520 Ma ages were obtained for dykes of the Essakane swarm, three in Burkina Faso, one from Ghana (N130 orientation) and one from Senegal (E-W orientation), and document a large extent (600 km wide and 1500 km long) and short duration of dyke emplacement. The Manso N350 dyke swarm in southern Ghana, which is about 400 km long and about 200 km wide, yields a preliminary age of ca 870 Ma. A mantle plume origin is suggested for these swarms, especially the 1790-1765 Ma Libiri-Bassari swarm and the 1520 Ma Essakane swarms (which have lithosphere-contaminated E-MORB chemistry), whose scale is similar to largest giant radiating swarms (e.g. CAMP and Mackenzie). The 870 Ma Manso swarm has composition closer to OIB, consistent with a plume/hotspot origin. The 1575 Ma Korsimoro swarm has composition between EMORB and NMORB, which suggests a rift setting.

Artificial Exo-Society Modeling: a New Tool for SETI Research

NASA Astrophysics Data System (ADS)

Gardner, James N.

2002-01-01

One of the newest fields of complexity research is artificial society modeling. Methodologically related to artificial life research, artificial society modeling utilizes agent-based computer simulation tools like SWARM and SUGARSCAPE developed by the Santa Fe Institute, Los Alamos National Laboratory and the Bookings Institution in an effort to introduce an unprecedented degree of rigor and quantitative sophistication into social science research. The broad aim of artificial society modeling is to begin the development of a more unified social science that embeds cultural evolutionary processes in a computational environment that simulates demographics, the transmission of culture, conflict, economics, disease, the emergence of groups and coadaptation with an environment in a bottom-up fashion. When an artificial society computer model is run, artificial societal patterns emerge from the interaction of autonomous software agents (the "inhabitants" of the artificial society). Artificial society modeling invites the interpretation of society as a distributed computational system and the interpretation of social dynamics as a specialized category of computation. Artificial society modeling techniques offer the potential of computational simulation of hypothetical alien societies in much the same way that artificial life modeling techniques offer the potential to model hypothetical exobiological phenomena. NASA recently announced its intention to begin exploring the possibility of including artificial life research within the broad portfolio of scientific fields comprised by the interdisciplinary astrobiology research endeavor. It may be appropriate for SETI researchers to likewise commence an exploration of the possible inclusion of artificial exo-society modeling within the SETI research endeavor. Artificial exo-society modeling might be particularly useful in a post-detection environment by (1) coherently organizing the set of data points derived from a detected ETI signal, (2) mapping trends in the data points over time (assuming receipt of an extended ETI signal), and (3) projecting such trends forward to derive alternative cultural evolutionary scenarios for the exo-society under analysis. The latter exercise might be particularly useful to compensate for the inevitable time lag between generation of an ETI signal and receipt of an ETI signal on Earth. For this reason, such an exercise might be a helpful adjunct to the decisional process contemplated by Paragraph 9 of the Declaration of Principles Concerning Activities Following the Detection of Extraterrestrial Intelligence.

Research on autonomous identification of airport targets based on Gabor filtering and Radon transform

NASA Astrophysics Data System (ADS)

Yi, Juan; Du, Qingyu; Zhang, Hong jiang; Zhang, Yao lei

2017-11-01

Target recognition is a leading key technology in intelligent image processing and application development at present, with the enhancement of computer processing ability, autonomous target recognition algorithm, gradually improve intelligence, and showed good adaptability. Taking the airport target as the research object, analysis the airport layout characteristics, construction of knowledge model, Gabor filter and Radon transform based on the target recognition algorithm of independent design, image processing and feature extraction of the airport, the algorithm was verified, and achieved better recognition results.

The Dynamics of Interacting Swarms

DTIC Science & Technology

2018-04-04

Unlimited 16 Ira Schwartzt (202) 404-8359 Swarms are self-organized dynamical coupled agents which evolve from simple rules of communication. They are ...when delay is introduced to the communicating agents. One of our major findings is that interacting swarms are far less likely to flock cohesively if...they are coupled with delay. In addition, parameter ranges based on coupling strength, incidence angle of collision, and delay change dramatically

A parallel implementation of a multisensor feature-based range-estimation method

NASA Technical Reports Server (NTRS)

Suorsa, Raymond E.; Sridhar, Banavar

1993-01-01

There are many proposed vision based methods to perform obstacle detection and avoidance for autonomous or semi-autonomous vehicles. All methods, however, will require very high processing rates to achieve real time performance. A system capable of supporting autonomous helicopter navigation will need to extract obstacle information from imagery at rates varying from ten frames per second to thirty or more frames per second depending on the vehicle speed. Such a system will need to sustain billions of operations per second. To reach such high processing rates using current technology, a parallel implementation of the obstacle detection/ranging method is required. This paper describes an efficient and flexible parallel implementation of a multisensor feature-based range-estimation algorithm, targeted for helicopter flight, realized on both a distributed-memory and shared-memory parallel computer.

A Briefing on Metrics and Risks for Autonomous Decision-Making in Aerospace Applications

NASA Technical Reports Server (NTRS)

Frost, Susan; Goebel, Kai Frank; Galvan, Jose Ramon

2012-01-01

Significant technology advances will enable future aerospace systems to safely and reliably make decisions autonomously, or without human interaction. The decision-making may result in actions that enable an aircraft or spacecraft in an off-nominal state or with slightly degraded components to achieve mission performance and safety goals while reducing or avoiding damage to the aircraft or spacecraft. Some key technology enablers for autonomous decision-making include: a continuous state awareness through the maturation of the prognostics health management field, novel sensor development, and the considerable gains made in computation power and data processing bandwidth versus system size. Sophisticated algorithms and physics based models coupled with these technological advances allow reliable assessment of a system, subsystem, or components. Decisions that balance mission objectives and constraints with remaining useful life predictions can be made autonomously to maintain safety requirements, optimal performance, and ensure mission objectives. This autonomous approach to decision-making will come with new risks and benefits, some of which will be examined in this paper. To start, an account of previous work to categorize or quantify autonomy in aerospace systems will be presented. In addition, a survey of perceived risks in autonomous decision-making in the context of piloted aircraft and remotely piloted or completely autonomous unmanned autonomous systems (UAS) will be presented based on interviews that were conducted with individuals from industry, academia, and government.

Autonomous Navigation by a Mobile Robot

NASA Technical Reports Server (NTRS)

Huntsberger, Terrance; Aghazarian, Hrand

2005-01-01

ROAMAN is a computer program for autonomous navigation of a mobile robot on a long (as much as hundreds of meters) traversal of terrain. Developed for use aboard a robotic vehicle (rover) exploring the surface of a remote planet, ROAMAN could also be adapted to similar use on terrestrial mobile robots. ROAMAN implements a combination of algorithms for (1) long-range path planning based on images acquired by mast-mounted, wide-baseline stereoscopic cameras, and (2) local path planning based on images acquired by body-mounted, narrow-baseline stereoscopic cameras. The long-range path-planning algorithm autonomously generates a series of waypoints that are passed to the local path-planning algorithm, which plans obstacle-avoiding legs between the waypoints. Both the long- and short-range algorithms use an occupancy-grid representation in computations to detect obstacles and plan paths. Maps that are maintained by the long- and short-range portions of the software are not shared because substantial localization errors can accumulate during any long traverse. ROAMAN is not guaranteed to generate an optimal shortest path, but does maintain the safety of the rover.

Optimal configuration of power grid sources based on optimal particle swarm algorithm

NASA Astrophysics Data System (ADS)

Wen, Yuanhua

2018-04-01

In order to optimize the distribution problem of power grid sources, an optimized particle swarm optimization algorithm is proposed. First, the concept of multi-objective optimization and the Pareto solution set are enumerated. Then, the performance of the classical genetic algorithm, the classical particle swarm optimization algorithm and the improved particle swarm optimization algorithm are analyzed. The three algorithms are simulated respectively. Compared with the test results of each algorithm, the superiority of the algorithm in convergence and optimization performance is proved, which lays the foundation for subsequent micro-grid power optimization configuration solution.

Multiswarm comprehensive learning particle swarm optimization for solving multiobjective optimization problems.

PubMed

Yu, Xiang; Zhang, Xueqing

2017-01-01

Comprehensive learning particle swarm optimization (CLPSO) is a powerful state-of-the-art single-objective metaheuristic. Extending from CLPSO, this paper proposes multiswarm CLPSO (MSCLPSO) for multiobjective optimization. MSCLPSO involves multiple swarms, with each swarm associated with a separate original objective. Each particle's personal best position is determined just according to the corresponding single objective. Elitists are stored externally. MSCLPSO differs from existing multiobjective particle swarm optimizers in three aspects. First, each swarm focuses on optimizing the associated objective using CLPSO, without learning from the elitists or any other swarm. Second, mutation is applied to the elitists and the mutation strategy appropriately exploits the personal best positions and elitists. Third, a modified differential evolution (DE) strategy is applied to some extreme and least crowded elitists. The DE strategy updates an elitist based on the differences of the elitists. The personal best positions carry useful information about the Pareto set, and the mutation and DE strategies help MSCLPSO discover the true Pareto front. Experiments conducted on various benchmark problems demonstrate that MSCLPSO can find nondominated solutions distributed reasonably over the true Pareto front in a single run.

Autonomous, Computer-Based Behavioral Health Countermeasure Evaluation at HI-SEAS Mars Analog.

PubMed

Anderson, Allison P; Fellows, Abigail M; Binsted, Kim A; Hegel, Mark T; Buckey, Jay C

Living in an isolated, confined environment (ICE) can induce conflict, stress, and depression. Computer-based behavioral health countermeasures are appealing for training and treatment in ICEs because they provide confidentiality and do not require communication with the outside environment. We evaluated the Virtual Space Station (VSS), a suite of interactive computer-delivered psychological training and treatment programs, at the Hawaii Space Exploration Analog and Simulation (HI-SEAS) III expedition. Six subjects (3 male, 3 female) spent 8 mo in group-isolation and used the Conflict, Stress, and Depression modules in the VSS. Survey evaluations, data collected within the program, and postdeployment interviews were collected. This crew dealt with behavioral health issues common to ICEs. The VSS proved to be a valuable resource and was used both as intended, and in unanticipated ways, to help maintain behavioral health. The Conflict and Stress Modules were rated as highly acceptable (1.8 on a 7-point Likert scale). The crew identified a total of 13 stressors and worked on 9 problems through the VSS. Opinions about the modules were highly individualized. Crewmembers identified exercises in the VSS that were applicable and not applicable to their needs. Additional content to improve the program was identified. Autonomous, confidential training and treatment for behavioral health issues will need to be a critical component of long duration spaceflight travel. This work provides an evaluation of such a tool in a relevant ICE. Anderson AP, Fellows AM, Binsted KA, Hegel MT, Buckey JC. Autonomous, computer-based behavioral health countermeasure evaluation at HI-SEAS Mars analog. Aerosp Med Hum Perform. 2016; 87(11):912-920.

Reconciling ocean mass content change based on direct and inverse approaches by utilizing data from GRACE, altimetry and Swarm

NASA Astrophysics Data System (ADS)

Rietbroek, R.; Uebbing, B.; Lück, C.; Kusche, J.

2017-12-01

Ocean mass content (OMC) change due to the melting of the ice-sheets in Greenland and Antarctica, melting of glaciers and changes in terrestrial hydrology is a major contributor to present-day sea level rise. Since 2002, the GRACE satellite mission serves as a valuable tool for directly measuring the variations in OMC. As GRACE has almost reached the end of its lifetime, efforts are being made to utilize the Swarm mission for the recovery of low degree time-variable gravity fields to bridge a possible gap until the GRACE-FO mission and to fill up periods where GRACE data was not existent. To this end we compute Swarm monthly normal equations and spherical harmonics that are found competitive to other solutions. In addition to directly measuring the OMC, combination of GRACE gravity data with altimetry data in a global inversion approach allows to separate the total sea level change into individual mass-driven and steric contributions. However, published estimates of OMC from the direct and inverse methods differ not only depending on the time window, but also are influenced by numerous post-processing choices. Here, we will look into sources of such differences between direct and inverse approaches and evaluate the capabilities of Swarm to derive OMC. Deriving time series of OMC requires several processing steps; choosing a GRACE (and altimetry) product, data coverage, masks and filters to be applied in either spatial or spectral domain, corrections related to spatial leakage, GIA and geocenter motion. In this study, we compare and quantify the effects of the different processing choices of the direct and inverse methods. Our preliminary results point to the GIA correction as the major source of difference between the two approaches.

An autonomous satellite architecture integrating deliberative reasoning and behavioural intelligence

NASA Technical Reports Server (NTRS)

Lindley, Craig A.

1993-01-01

This paper describes a method for the design of autonomous spacecraft, based upon behavioral approaches to intelligent robotics. First, a number of previous spacecraft automation projects are reviewed. A methodology for the design of autonomous spacecraft is then presented, drawing upon both the European Space Agency technological center (ESTEC) automation and robotics methodology and the subsumption architecture for autonomous robots. A layered competency model for autonomous orbital spacecraft is proposed. A simple example of low level competencies and their interaction is presented in order to illustrate the methodology. Finally, the general principles adopted for the control hardware design of the AUSTRALIS-1 spacecraft are described. This system will provide an orbital experimental platform for spacecraft autonomy studies, supporting the exploration of different logical control models, different computational metaphors within the behavioral control framework, and different mappings from the logical control model to its physical implementation.

Advanced training systems

NASA Technical Reports Server (NTRS)

Savely, Robert T.; Loftin, R. Bowen

1990-01-01

Training is a major endeavor in all modern societies. Common training methods include training manuals, formal classes, procedural computer programs, simulations, and on-the-job training. NASA's training approach has focussed primarily on on-the-job training in a simulation environment for both crew and ground based personnel. NASA must explore new approaches to training for the 1990's and beyond. Specific autonomous training systems are described which are based on artificial intelligence technology for use by NASA astronauts, flight controllers, and ground based support personnel that show an alternative to current training systems. In addition to these specific systems, the evolution of a general architecture for autonomous intelligent training systems that integrates many of the features of traditional training programs with artificial intelligence techniques is presented. These Intelligent Computer Aided Training (ICAT) systems would provide much of the same experience that could be gained from the best on-the-job training.

RNA secondary structure prediction using soft computing.

PubMed

Ray, Shubhra Sankar; Pal, Sankar K

2013-01-01

Prediction of RNA structure is invaluable in creating new drugs and understanding genetic diseases. Several deterministic algorithms and soft computing-based techniques have been developed for more than a decade to determine the structure from a known RNA sequence. Soft computing gained importance with the need to get approximate solutions for RNA sequences by considering the issues related with kinetic effects, cotranscriptional folding, and estimation of certain energy parameters. A brief description of some of the soft computing-based techniques, developed for RNA secondary structure prediction, is presented along with their relevance. The basic concepts of RNA and its different structural elements like helix, bulge, hairpin loop, internal loop, and multiloop are described. These are followed by different methodologies, employing genetic algorithms, artificial neural networks, and fuzzy logic. The role of various metaheuristics, like simulated annealing, particle swarm optimization, ant colony optimization, and tabu search is also discussed. A relative comparison among different techniques, in predicting 12 known RNA secondary structures, is presented, as an example. Future challenging issues are then mentioned.

A Novel Flexible Inertia Weight Particle Swarm Optimization Algorithm.

PubMed

Amoshahy, Mohammad Javad; Shamsi, Mousa; Sedaaghi, Mohammad Hossein

2016-01-01

Particle swarm optimization (PSO) is an evolutionary computing method based on intelligent collective behavior of some animals. It is easy to implement and there are few parameters to adjust. The performance of PSO algorithm depends greatly on the appropriate parameter selection strategies for fine tuning its parameters. Inertia weight (IW) is one of PSO's parameters used to bring about a balance between the exploration and exploitation characteristics of PSO. This paper proposes a new nonlinear strategy for selecting inertia weight which is named Flexible Exponential Inertia Weight (FEIW) strategy because according to each problem we can construct an increasing or decreasing inertia weight strategy with suitable parameters selection. The efficacy and efficiency of PSO algorithm with FEIW strategy (FEPSO) is validated on a suite of benchmark problems with different dimensions. Also FEIW is compared with best time-varying, adaptive, constant and random inertia weights. Experimental results and statistical analysis prove that FEIW improves the search performance in terms of solution quality as well as convergence rate.

A Novel Flexible Inertia Weight Particle Swarm Optimization Algorithm

PubMed Central

Shamsi, Mousa; Sedaaghi, Mohammad Hossein

2016-01-01

Particle swarm optimization (PSO) is an evolutionary computing method based on intelligent collective behavior of some animals. It is easy to implement and there are few parameters to adjust. The performance of PSO algorithm depends greatly on the appropriate parameter selection strategies for fine tuning its parameters. Inertia weight (IW) is one of PSO’s parameters used to bring about a balance between the exploration and exploitation characteristics of PSO. This paper proposes a new nonlinear strategy for selecting inertia weight which is named Flexible Exponential Inertia Weight (FEIW) strategy because according to each problem we can construct an increasing or decreasing inertia weight strategy with suitable parameters selection. The efficacy and efficiency of PSO algorithm with FEIW strategy (FEPSO) is validated on a suite of benchmark problems with different dimensions. Also FEIW is compared with best time-varying, adaptive, constant and random inertia weights. Experimental results and statistical analysis prove that FEIW improves the search performance in terms of solution quality as well as convergence rate. PMID:27560945

Atmospheric Balloon Swarms for Persistent In-Situ Measurements in Hurricanes

NASA Astrophysics Data System (ADS)

Meneghello, G.; Bewley, T.

2015-12-01

Real-time measurements within hurricanes are essential to improve forecasts, protect property and save lives. Current methods for obtaining in-situ data, including radar and satellite imagery as well as drop-sondes deployed from repeated aircraft flights above or even within the hurricane itself, are costly, dangerous and limited in duration or resolution. We demonstrate how a swarm of inexpensive, buoyancy-controlled, sensor-laden balloons can be deployed from altitude or from sea-level within a hurricane flow field, and coordinated autonomously in an energetically-efficient fashion to persistently and continuously monitor relevant properties (pressure, humidity, temperature, windspeed) of a hurricane for days at a time. Rather than fighting the gale-force winds in the storm, the strong, predictable stratification of these winds is leveraged to disperse the balloons into a favorable, time-evolving distribution and to follow the hurricane track as it moves. Certain target orbits of interest in the hurricane can be continuously sampled by some balloons, while other balloons make continuous sweeps between the eye and the spiral rain bands. We expect the acquired data to complement current measurement methods and to be instrumental in improving the numerical models' forecast skills.

Probabilistic Swarm Guidance using Optimal Transport

DTIC Science & Technology

2014-10-10

controlled to collectively exhibit useful emergent behavior [2]–[5]. Similarly, swarms of hundreds to thousands of femtosatellites (100-gram-class...algorithm using inhomo- geneous Markov chains (PSG– IMC ), each agent chooses the tuning parameter (ξjk) based on the Hellinger distance (HD) between the...PGA and PSG– IMC in the next section. B. Simulation Results We now present the setup of this simulation example. The swarm containing m = 5000 agents is

Improved Performance of Loop-Mediated Isothermal Amplification Assays via Swarm Priming.

PubMed

Martineau, Rhett L; Murray, Sarah A; Ci, Shufang; Gao, Weimin; Chao, Shih-Hui; Meldrum, Deirdre R

2017-01-03

This work describes an enhancement to the loop-mediated isothermal amplification (LAMP) reaction which results in improved performance. Enhancement is achieved by adding a new set of primers to conventional LAMP reactions. These primers are termed "swarm primers" based on their relatively high concentration and their ability to create new amplicons despite the theoretical lack of single-stranded annealing sites. The primers target a region upstream of the FIP/BIP primer recognition sequences on opposite strands, substantially overlapping F1/B1 sites. Thus, despite the addition of a new primer set to an already complex assay, no significant increase in assay complexity is incurred. Swarm priming is presented for three DNA templates: Lambda phage, Synechocystis sp. PCC 6803 rbcL gene, and human HFE. The results of adding swarm primers to conventional LAMP reactions include increased amplification speed, increased indicator contrast, and increased reaction products. For at least one template, minor improvements in assay repeatability are also shown. In addition, swarm priming is shown to be effective at increasing the reaction speed for RNA amplification via RT-LAMP. Collectively, these results suggest that the addition of swarm primers will likely benefit most if not all existing LAMP assays based on state-of-the-art, six-primer reactions.

Cooperative Search and Rescue with Artificial Fishes Based on Fish-Swarm Algorithm for Underwater Wireless Sensor Networks

PubMed Central

Zhao, Wei; Tang, Zhenmin; Yang, Yuwang; Wang, Lei; Lan, Shaohua

2014-01-01

This paper presents a searching control approach for cooperating mobile sensor networks. We use a density function to represent the frequency of distress signals issued by victims. The mobile nodes' moving in mission space is similar to the behaviors of fish-swarm in water. So, we take the mobile node as artificial fish node and define its operations by a probabilistic model over a limited range. A fish-swarm based algorithm is designed requiring local information at each fish node and maximizing the joint detection probabilities of distress signals. Optimization of formation is also considered for the searching control approach and is optimized by fish-swarm algorithm. Simulation results include two schemes: preset route and random walks, and it is showed that the control scheme has adaptive and effective properties. PMID:24741341

Cooperative search and rescue with artificial fishes based on fish-swarm algorithm for underwater wireless sensor networks.

PubMed

Zhao, Wei; Tang, Zhenmin; Yang, Yuwang; Wang, Lei; Lan, Shaohua

2014-01-01

This paper presents a searching control approach for cooperating mobile sensor networks. We use a density function to represent the frequency of distress signals issued by victims. The mobile nodes' moving in mission space is similar to the behaviors of fish-swarm in water. So, we take the mobile node as artificial fish node and define its operations by a probabilistic model over a limited range. A fish-swarm based algorithm is designed requiring local information at each fish node and maximizing the joint detection probabilities of distress signals. Optimization of formation is also considered for the searching control approach and is optimized by fish-swarm algorithm. Simulation results include two schemes: preset route and random walks, and it is showed that the control scheme has adaptive and effective properties.

Composite Particle Swarm Optimizer With Historical Memory for Function Optimization.

PubMed

Li, Jie; Zhang, JunQi; Jiang, ChangJun; Zhou, MengChu

2015-10-01

Particle swarm optimization (PSO) algorithm is a population-based stochastic optimization technique. It is characterized by the collaborative search in which each particle is attracted toward the global best position (gbest) in the swarm and its own best position (pbest). However, all of particles' historical promising pbests in PSO are lost except their current pbests. In order to solve this problem, this paper proposes a novel composite PSO algorithm, called historical memory-based PSO (HMPSO), which uses an estimation of distribution algorithm to estimate and preserve the distribution information of particles' historical promising pbests. Each particle has three candidate positions, which are generated from the historical memory, particles' current pbests, and the swarm's gbest. Then the best candidate position is adopted. Experiments on 28 CEC2013 benchmark functions demonstrate the superiority of HMPSO over other algorithms.

Determination Gradients of the Earth's Magnetic Field from the Measurements of the Satellites and Inversion of the Kursk Magnetic Anomaly

NASA Technical Reports Server (NTRS)

Karoly, Kis; Taylor, Patrick T.; Geza, Wittmann

2014-01-01

We computed magnetic field gradients at satellite altitude, over Europe with emphasis on the Kursk Magnetic Anomaly (KMA). They were calculated using the CHAMP satellite total magnetic anomalies. Our computations were done to determine how the magnetic anomaly data from the new ESA/Swarm satellites could be utilized to determine the structure of the magnetization of the Earths crust, especially in the region of the KMA. Since the ten years of 2 CHAMP data could be used to simulate the Swarm data. An initial East magnetic anomaly gradient map of Europe was computed and subsequently the North, East and Vertical magnetic gradients for the KMA region were calculated. The vertical gradient of the KMA was determined using Hilbert transforms. Inversion of the total KMA was derived using Simplex and Simulated Annealing algorithms. Our resulting inversion depth model is a horizontal quadrangle with upper 300-329 km and lower 331-339 km boundaries.

Autonomous power expert system

NASA Technical Reports Server (NTRS)

Ringer, Mark J.; Quinn, Todd M.

1990-01-01

The goal of the Autonomous Power System (APS) program is to develop and apply intelligent problem solving and control technologies to the Space Station Freedom Electrical Power Systems (SSF/EPS). The objectives of the program are to establish artificial intelligence/expert system technology paths, to create knowledge based tools with advanced human-operator interfaces, and to integrate and interface knowledge-based and conventional control schemes. This program is being developed at the NASA-Lewis. The APS Brassboard represents a subset of a 20 KHz Space Station Power Management And Distribution (PMAD) testbed. A distributed control scheme is used to manage multiple levels of computers and switchgear. The brassboard is comprised of a set of intelligent switchgear used to effectively switch power from the sources to the loads. The Autonomous Power Expert System (APEX) portion of the APS program integrates a knowledge based fault diagnostic system, a power resource scheduler, and an interface to the APS Brassboard. The system includes knowledge bases for system diagnostics, fault detection and isolation, and recommended actions. The scheduler autonomously assigns start times to the attached loads based on temporal and power constraints. The scheduler is able to work in a near real time environment for both scheduling and dynamic replanning.

Autonomous power expert system

NASA Technical Reports Server (NTRS)

Ringer, Mark J.; Quinn, Todd M.

1990-01-01

The goal of the Autonomous Power System (APS) program is to develop and apply intelligent problem solving and control technologies to the Space Station Freedom Electrical Power Systems (SSF/EPS). The objectives of the program are to establish artificial intelligence/expert system technology paths, to create knowledge based tools with advanced human-operator interfaces, and to integrate and interface knowledge-based and conventional control schemes. This program is being developed at the NASA-Lewis. The APS Brassboard represents a subset of a 20 KHz Space Station Power Management And Distribution (PMAD) testbed. A distributed control scheme is used to manage multiple levels of computers and switchgear. The brassboard is comprised of a set of intelligent switchgear used to effectively switch power from the sources to the loads. The Autonomous Power Expert System (APEX) portion of the APS program integrates a knowledge based fault diagnostic system, a power resource scheduler, and an interface to the APS Brassboard. The system includes knowledge bases for system diagnostics, fault detection and isolation, and recommended actions. The scheduler autonomously assigns start times to the attached loads based on temporal and power constraints. The scheduler is able to work in a near real time environment for both scheduling an dynamic replanning.

Biologically inspired collision avoidance system for unmanned vehicles

NASA Astrophysics Data System (ADS)

Ortiz, Fernando E.; Graham, Brett; Spagnoli, Kyle; Kelmelis, Eric J.

2009-05-01

In this project, we collaborate with researchers in the neuroscience department at the University of Delaware to develop an Field Programmable Gate Array (FPGA)-based embedded computer, inspired by the brains of small vertebrates (fish). The mechanisms of object detection and avoidance in fish have been extensively studied by our Delaware collaborators. The midbrain optic tectum is a biological multimodal navigation controller capable of processing input from all senses that convey spatial information, including vision, audition, touch, and lateral-line (water current sensing in fish). Unfortunately, computational complexity makes these models too slow for use in real-time applications. These simulations are run offline on state-of-the-art desktop computers, presenting a gap between the application and the target platform: a low-power embedded device. EM Photonics has expertise in developing of high-performance computers based on commodity platforms such as graphic cards (GPUs) and FPGAs. FPGAs offer (1) high computational power, low power consumption and small footprint (in line with typical autonomous vehicle constraints), and (2) the ability to implement massively-parallel computational architectures, which can be leveraged to closely emulate biological systems. Combining UD's brain modeling algorithms and the power of FPGAs, this computer enables autonomous navigation in complex environments, and further types of onboard neural processing in future applications.

A New Soft Computing Method for K-Harmonic Means Clustering.

PubMed

Yeh, Wei-Chang; Jiang, Yunzhi; Chen, Yee-Fen; Chen, Zhe

2016-01-01

The K-harmonic means clustering algorithm (KHM) is a new clustering method used to group data such that the sum of the harmonic averages of the distances between each entity and all cluster centroids is minimized. Because it is less sensitive to initialization than K-means (KM), many researchers have recently been attracted to studying KHM. In this study, the proposed iSSO-KHM is based on an improved simplified swarm optimization (iSSO) and integrates a variable neighborhood search (VNS) for KHM clustering. As evidence of the utility of the proposed iSSO-KHM, we present extensive computational results on eight benchmark problems. From the computational results, the comparison appears to support the superiority of the proposed iSSO-KHM over previously developed algorithms for all experiments in the literature.

Fuzzy logic control of an AGV

NASA Astrophysics Data System (ADS)

Kelkar, Nikhal; Samu, Tayib; Hall, Ernest L.

1997-09-01

Automated guided vehicles (AGVs) have many potential applications in manufacturing, medicine, space and defense. The purpose of this paper is to describe exploratory research on the design of a modular autonomous mobile robot controller. The controller incorporates a fuzzy logic approach for steering and speed control, a neuro-fuzzy approach for ultrasound sensing (not discussed in this paper) and an overall expert system. The advantages of a modular system are related to portability and transportability, i.e. any vehicle can become autonomous with minimal modifications. A mobile robot test-bed has been constructed using a golf cart base. This cart has full speed control with guidance provided by a vision system and obstacle avoidance using ultrasonic sensors. The speed and steering fuzzy logic controller is supervised by a 486 computer through a multi-axis motion controller. The obstacle avoidance system is based on a micro-controller interfaced with six ultrasonic transducers. This micro- controller independently handles all timing and distance calculations and sends a steering angle correction back to the computer via the serial line. This design yields a portable independent system in which high speed computer communication is not necessary. Vision guidance is accomplished with a CCD camera with a zoom lens. The data is collected by a vision tracking device that transmits the X, Y coordinates of the lane marker to the control computer. Simulation and testing of these systems yielded promising results. This design, in its modularity, creates a portable autonomous fuzzy logic controller applicable to any mobile vehicle with only minor adaptations.

Development of a mobile robot for the 1995 AUVS competition

NASA Astrophysics Data System (ADS)

Matthews, Bradley O.; Ruthemeyer, Michael A.; Perdue, David; Hall, Ernest L.

1995-12-01

Automated guided vehicles (AGVs) have many potential applications in manufacturing, medicine, space and defense. The purpose of this paper is to describe exploratory research on the design of a modular autonomous mobile robot controller. The advantages of a modular system are related to portability and the fact that any vehicle can become autonomous with minimal modifications. A mobile robot test-bed has been constructed using a golf cart base. This cart has full speed control with guidance provided by a vision system and obstacle avoidance using ultrasonic sensors systems. The speed and steering control are supervised by a 486 computer through a 3-axis motion controller. The obstacle avoidance system is based on a micro-controller interfaced with six ultrasonic transducers. The is micro-controller independently handles all timing and distance calculations and sends a steering angle correction back to the computer via the serial line. This design yields a portable independent system, where even computer communication is not necessary. Vision guidance is accomplished with a CCD camera with a zoom lens. The data is collected through a commercial tracking device, communicating with the computer the X,Y coordinates of the lane marker. Testing of these systems yielded positive results by showing that at five mph the vehicle can follow a line and at the same time avoid obstacles. This design, in its modularity, creates a portable autonomous controller applicable for any mobile vehicle with only minor adaptations.

An External Archive-Guided Multiobjective Particle Swarm Optimization Algorithm.

PubMed

Zhu, Qingling; Lin, Qiuzhen; Chen, Weineng; Wong, Ka-Chun; Coello Coello, Carlos A; Li, Jianqiang; Chen, Jianyong; Zhang, Jun

2017-09-01

The selection of swarm leaders (i.e., the personal best and global best), is important in the design of a multiobjective particle swarm optimization (MOPSO) algorithm. Such leaders are expected to effectively guide the swarm to approach the true Pareto optimal front. In this paper, we present a novel external archive-guided MOPSO algorithm (AgMOPSO), where the leaders for velocity update are all selected from the external archive. In our algorithm, multiobjective optimization problems (MOPs) are transformed into a set of subproblems using a decomposition approach, and then each particle is assigned accordingly to optimize each subproblem. A novel archive-guided velocity update method is designed to guide the swarm for exploration, and the external archive is also evolved using an immune-based evolutionary strategy. These proposed approaches speed up the convergence of AgMOPSO. The experimental results fully demonstrate the superiority of our proposed AgMOPSO in solving most of the test problems adopted, in terms of two commonly used performance measures. Moreover, the effectiveness of our proposed archive-guided velocity update method and immune-based evolutionary strategy is also experimentally validated on more than 30 test MOPs.

Fluid-faulting interactions: Fracture-mesh and fault-valve behavior in the February 2014 Mammoth Mountain, California, earthquake swarm

USGS Publications Warehouse

Shelly, David R.; Taira, Taka’aki; Prejean, Stephanie; Hill, David P.; Dreger, Douglas S.

2015-01-01

Faulting and fluid transport in the subsurface are highly coupled processes, which may manifest seismically as earthquake swarms. A swarm in February 2014 beneath densely monitored Mammoth Mountain, California, provides an opportunity to witness these interactions in high resolution. Toward this goal, we employ massive waveform-correlation-based event detection and relative relocation, which quadruples the swarm catalog to more than 6000 earthquakes and produces high-precision locations even for very small events. The swarm's main seismic zone forms a distributed fracture mesh, with individual faults activated in short earthquake bursts. The largest event of the sequence, M 3.1, apparently acted as a fault valve and was followed by a distinct wave of earthquakes propagating ~1 km westward from the updip edge of rupture, 1–2 h later. Late in the swarm, multiple small, shallower subsidiary faults activated with pronounced hypocenter migration, suggesting that a broader fluid pressure pulse propagated through the subsurface.

ANTS/PAM: Future Exploration of the Asteroid Belt

NASA Astrophysics Data System (ADS)

Clark, P. E.; Curtis, S. A.; Rilee, M. L.; Cheung, C. Y.

2004-05-01

The Autonomous Nano-Technology Swarm (ANTS) is applied to the Prospecting Asteroid Mission (PAM) concept, as part of a NASA RASC study. The ANTS architecture is inspired by success of social insect colonies, based on the division of labor within the colonies: 1) within their specialties, individual specialists generally outperform general-ists, and 2) with sufficiently efficient social interaction and coordination, the group of specialists generally outper-forms the group of generalists. ANTS as applied to PAM involves a thousand individual specialist `sciencecraft', one subswarm per target, in an environment where detection and tracking of irregular, infrequent targets is a major chal-lenge. Workers, carry and operate eight to nine different scientific instruments, including spectrometers, ranging and radio science devices, imagers. The remaining specialists, Messenger/Rulers, provide communication and coordina-tion. The non-expendable propulsion system is based on autonomously deployable and configurable solar sails, a system suitable to a low gravity environment. The design of the neural basis function requires a minimum of 4 or 5 specialists for collective decision making. Allowing for ten instrument specialist teams and compensating for antici-pated high attrition, we calculate an initial minimum of 100 per subswarm should allow characterization of hundreds of asteroids. The difficulty in observing irregular, rapidly moving, poorly illuminated objects is largely overcome by the ANT sciencecraft capability to optimize conditions for each instrument. Components are composed of carbon nanotubules reversibly deployable from NEMS nodes, allowing 100 times decrease in packaging volume. 1000 smart 10 centimeter, 1 kg cubic boxes create a 1000 kg 1 meter cube.

A Satellite-Based Lagrangian View on Phytoplankton Dynamics

NASA Astrophysics Data System (ADS)

Lehahn, Yoav; d'Ovidio, Francesco; Koren, Ilan

2018-01-01

The well-lit upper layer of the open ocean is a dynamical environment that hosts approximately half of global primary production. In the remote parts of this environment, distant from the coast and from the seabed, there is no obvious spatially fixed reference frame for describing the dynamics of the microscopic drifting organisms responsible for this immense production of organic matter—the phytoplankton. Thus, a natural perspective for studying phytoplankton dynamics is to follow the trajectories of water parcels in which the organisms are embedded. With the advent of satellite oceanography, this Lagrangian perspective has provided valuable information on different aspects of phytoplankton dynamics, including bloom initiation and termination, spatial distribution patterns, biodiversity, export of carbon to the deep ocean, and, more recently, bottom-up mechanisms that affect the distribution and behavior of higher-trophic-level organisms. Upcoming submesoscale-resolving satellite observations and swarms of autonomous platforms open the way to the integration of vertical dynamics into the Lagrangian view of phytoplankton dynamics.

A Satellite-Based Lagrangian View on Phytoplankton Dynamics.

PubMed

Lehahn, Yoav; d'Ovidio, Francesco; Koren, Ilan

2018-01-03

The well-lit upper layer of the open ocean is a dynamical environment that hosts approximately half of global primary production. In the remote parts of this environment, distant from the coast and from the seabed, there is no obvious spatially fixed reference frame for describing the dynamics of the microscopic drifting organisms responsible for this immense production of organic matter-the phytoplankton. Thus, a natural perspective for studying phytoplankton dynamics is to follow the trajectories of water parcels in which the organisms are embedded. With the advent of satellite oceanography, this Lagrangian perspective has provided valuable information on different aspects of phytoplankton dynamics, including bloom initiation and termination, spatial distribution patterns, biodiversity, export of carbon to the deep ocean, and, more recently, bottom-up mechanisms that affect the distribution and behavior of higher-trophic-level organisms. Upcoming submesoscale-resolving satellite observations and swarms of autonomous platforms open the way to the integration of vertical dynamics into the Lagrangian view of phytoplankton dynamics.

Dynamic Triggering of Seismic Events and Their Relation to Slow Slip in Interior Alaska

NASA Astrophysics Data System (ADS)

Sims, N. E.; Holtkamp, S. G.

2017-12-01

We conduct a search for dynamically triggered events in the Minto Flats Fault Zone (MFFZ), a left-lateral strike-slip zone expressed as multiple, partially overlapping faults, in central Alaska. We focus on the MFFZ because we have observed slow slip processes (earthquake swarms and Very Low Frequency Earthquakes) and interaction between earthquake swarms and larger main-shock (MS) events in this area before. We utilize the Alaska Earthquake Center catalog to identify potential earthquake swarms and dynamically triggered foreshock and mainshock events along the fault zone. We find 30 swarms occurring in the last two decades, five of which we classify as foreshock (FS) swarms due to their close proximity in both time and space to MS events. Many of the earthquake swarms cluster around 15-20 km depth, which is near the seismic-aseismic transition along this fault zone. Additionally, we observe instances of large teleseismic events such as the M8.6 2012 Sumatra earthquake and M7.4 2012 Guatemala earthquake triggering seismic events within the MFFZ, with the Sumatra earthquake triggering a mainshock event that was preceded by an ongoing earthquake swarm and the Guatemala event triggering earthquake swarms that subsequently transition into a larger mainshock event. In both cases an earthquake swarm transitioned into a mainshock-aftershock event and activity continued for several days after the teleseismic waves had passed, lending some evidence to delayed dynamic triggering of seismic events. We hypothesize that large dynamic transient strain associated with the passage of teleseismic surface waves is triggering slow slip processes near the base of the seismogenic zone. These triggered aseismic transient events result in earthquake swarms, which sometimes lead to the nucleation of larger earthquakes. We utilize network matched filtering to build more robust catalogs of swarm earthquake families in this region to search for additional swarm-like or triggered activity in response to teleseismic surface waves, and to test dynamic triggering hypotheses.

Stereo Correspondence Using Moment Invariants

NASA Astrophysics Data System (ADS)

Premaratne, Prashan; Safaei, Farzad

Autonomous navigation is seen as a vital tool in harnessing the enormous potential of Unmanned Aerial Vehicles (UAV) and small robotic vehicles for both military and civilian use. Even though, laser based scanning solutions for Simultaneous Location And Mapping (SLAM) is considered as the most reliable for depth estimation, they are not feasible for use in UAV and land-based small vehicles due to their physical size and weight. Stereovision is considered as the best approach for any autonomous navigation solution as stereo rigs are considered to be lightweight and inexpensive. However, stereoscopy which estimates the depth information through pairs of stereo images can still be computationally expensive and unreliable. This is mainly due to some of the algorithms used in successful stereovision solutions require high computational requirements that cannot be met by small robotic vehicles. In our research, we implement a feature-based stereovision solution using moment invariants as a metric to find corresponding regions in image pairs that will reduce the computational complexity and improve the accuracy of the disparity measures that will be significant for the use in UAVs and in small robotic vehicles.

Model-based Executive Control through Reactive Planning for Autonomous Rovers

NASA Technical Reports Server (NTRS)

Finzi, Alberto; Ingrand, Felix; Muscettola, Nicola

2004-01-01

This paper reports on the design and implementation of a real-time executive for a mobile rover that uses a model-based, declarative approach. The control system is based on the Intelligent Distributed Execution Architecture (IDEA), an approach to planning and execution that provides a unified representational and computational framework for an autonomous agent. The basic hypothesis of IDEA is that a large control system can be structured as a collection of interacting agents, each with the same fundamental structure. We show that planning and real-time response are compatible if the executive minimizes the size of the planning problem. We detail the implementation of this approach on an exploration rover (Gromit an RWI ATRV Junior at NASA Ames) presenting different IDEA controllers of the same domain and comparing them with more classical approaches. We demonstrate that the approach is scalable to complex coordination of functional modules needed for autonomous navigation and exploration.

Prime factorization using quantum annealing and computational algebraic geometry

NASA Astrophysics Data System (ADS)

Dridi, Raouf; Alghassi, Hedayat

2017-02-01

We investigate prime factorization from two perspectives: quantum annealing and computational algebraic geometry, specifically Gröbner bases. We present a novel autonomous algorithm which combines the two approaches and leads to the factorization of all bi-primes up to just over 200000, the largest number factored to date using a quantum processor. We also explain how Gröbner bases can be used to reduce the degree of Hamiltonians.

Autonomous Electrothermal Facility for Oil Recovery Intensification Fed by Wind Driven Power Unit

NASA Astrophysics Data System (ADS)

Belsky, Aleksey A.; Dobush, Vasiliy S.

2017-10-01

This paper describes the structure of autonomous facility fed by wind driven power unit for intensification of viscous and heavy crude oil recovery by means of heat impact on productive strata. Computer based service simulation of this facility was performed. Operational energy characteristics were obtained for various operational modes of facility. The optimal resistance of heating element of the downhole heater was determined for maximum operating efficiency of wind power unit.

Collective navigation of cargo-carrying swarms

PubMed Central

Shklarsh, Adi; Finkelshtein, Alin; Ariel, Gil; Kalisman, Oren; Ingham, Colin; Ben-Jacob, Eshel

2012-01-01

Much effort has been devoted to the study of swarming and collective navigation of micro-organisms, insects, fish, birds and other organisms, as well as multi-agent simulations and to the study of real robots. It is well known that insect swarms can carry cargo. The studies here are motivated by a less well-known phenomenon: cargo transport by bacteria swarms. We begin with a concise review of how bacteria swarms carry natural, micrometre-scale objects larger than the bacteria (e.g. fungal spores) as well as man-made beads and capsules (for drug delivery). A comparison of the trajectories of virtual beads in simulations (using different putative coupling between the virtual beads and the bacteria) with the observed trajectories of transported fungal spores implies the existence of adaptable coupling. Motivated by these observations, we devised new, multi-agent-based studies of cargo transport by agent swarms. As a first step, we extended previous modelling of collective navigation of simple bacteria-inspired agents in complex terrain, using three putative models of agent–cargo coupling. We found that cargo-carrying swarms can navigate efficiently in a complex landscape. We further investigated how the stability, elasticity and other features of agent–cargo bonds influence the collective motion and the transport of the cargo, and found sharp phase shifts and dual successful strategies for cargo delivery. Further understanding of such mechanisms may provide valuable clues to understand cargo-transport by smart swarms of other organisms as well as by man-made swarming robots. PMID:24312731

Seismological mechanism analysis of 2015 Luanxian swarm, Hebei province,China

NASA Astrophysics Data System (ADS)

Tan, Yipei; Liao, Xu; Ma, Hongsheng; Zhou, Longquan; Wang, Xingzhou

2017-04-01

The seismological mechanism of an earthquake swarm, a kind of seismic burst activity, means the physical and dynamic process in earthquakes triggering in the swarm. Here we focus on the seismological mechanism of 2015 Luanxian swarm in Hebei province, China. The process of digital seismic waveform data processing is divided into four steps. (1) Choose the three components waveform of earthquakes in the catalog as templates, and detect missing earthquakes by scanning the continues waveforms with matched filter technique. (2) Recalibrate P and S-wave phase arrival time using waveform cross-correlation phase detection technique to eliminate the artificial error in phase picking in the observation report made by Hebei seismic network, and then we obtain a more complete catalog and a more precise seismic phase report. (3) Relocate the earthquakes in the swarm using hypoDD based on phase arrival time we recalibrated, and analyze the characteristics of swarm epicenter migration based on the earthquake relocation result. (4) Detect whether there are repeating earthquakes activity using both waveform cross-correlation standard and whether rupture areas can overlapped. We finally detect 106 missing earthquakes in the swarm, 66 of them have the magnitude greater than ML0.0, include 2 greater than ML1.0. Relocation result shows that the epicenters of earthquakes in the swarm have a strip distribution in NE-SW direction, which indicates the seismogenic structure may be a NE-SW trending fault. The spatial-temporal distribution variation of epicenters in the swarm shows a kind of two stages linear migration characteristics, in which the first stage has appeared with a higher migration velocity as 1.2 km per day, and the velocity of the second step is 0.0024 km per day. According to the three basic models to explain the seismological mechanism of earthquake swarms: cascade model, slow slip model and fluid diffusion model, repeating earthquakes activity is difficult to explain by previous earthquakes stress triggering, however, it can be explained by continuing stress loading at the same asperity from fault slow slip. The phenomena of linear migration is more fitting slow slip model than the migration characteristics of fluid diffusion which satisfied diffusion equation. Comparing the phenomena we observed and the seismological mechanism models, we find that the Luanxian earthquake swarm may be associated with fault slow slip. Fault slow slip may play a role in Luanxian earthquake swarm triggering and sustained activity.

System and method of self-properties for an autonomous and automatic computer environment

NASA Technical Reports Server (NTRS)

Sterritt, Roy (Inventor); Hinchey, Michael G. (Inventor)

2010-01-01

Systems, methods and apparatus are provided through which in some embodiments self health/urgency data and environment health/urgency data may be transmitted externally from an autonomic element. Other embodiments may include transmitting the self health/urgency data and environment health/urgency data together on a regular basis similar to the lub-dub of a heartbeat. Yet other embodiments may include a method for managing a system based on the functioning state and operating status of the system, wherein the method may include processing received signals from the system indicative of the functioning state and the operating status to obtain an analysis of the condition of the system, generating one or more stay alive signals based on the functioning status and the operating state of the system, transmitting the stay-alive signal, transmitting self health/urgency data, and transmitting environment health/urgency data. Still other embodiments may include an autonomic element that includes a self monitor, a self adjuster, an environment monitor, and an autonomic manager.

Advanced Autonomous Systems for Space Operations

NASA Astrophysics Data System (ADS)

Gross, A. R.; Smith, B. D.; Muscettola, N.; Barrett, A.; Mjolssness, E.; Clancy, D. J.

2002-01-01

New missions of exploration and space operations will require unprecedented levels of autonomy to successfully accomplish their objectives. Inherently high levels of complexity, cost, and communication distances will preclude the degree of human involvement common to current and previous space flight missions. With exponentially increasing capabilities of computer hardware and software, including networks and communication systems, a new balance of work is being developed between humans and machines. This new balance holds the promise of not only meeting the greatly increased space exploration requirements, but simultaneously dramatically reducing the design, development, test, and operating costs. New information technologies, which take advantage of knowledge-based software, model-based reasoning, and high performance computer systems, will enable the development of a new generation of design and development tools, schedulers, and vehicle and system health management capabilities. Such tools will provide a degree of machine intelligence and associated autonomy that has previously been unavailable. These capabilities are critical to the future of advanced space operations, since the science and operational requirements specified by such missions, as well as the budgetary constraints will limit the current practice of monitoring and controlling missions by a standing army of ground-based controllers. System autonomy capabilities have made great strides in recent years, for both ground and space flight applications. Autonomous systems have flown on advanced spacecraft, providing new levels of spacecraft capability and mission safety. Such on-board systems operate by utilizing model-based reasoning that provides the capability to work from high-level mission goals, while deriving the detailed system commands internally, rather than having to have such commands transmitted from Earth. This enables missions of such complexity and communication` distances as are not otherwise possible, as well as many more efficient and low cost applications. In addition, utilizing component and system modeling and reasoning capabilities, autonomous systems will play an increasing role in ground operations for space missions, where they will both reduce the human workload as well as provide greater levels of monitoring and system safety. This paper will focus specifically on new and innovative software for remote, autonomous, space systems flight operations. Topics to be presented will include a brief description of key autonomous control concepts, the Remote Agent program that commanded the Deep Space 1 spacecraft to new levels of system autonomy, recent advances in distributed autonomous system capabilities, and concepts for autonomous vehicle health management systems. A brief description of teaming spacecraft and rovers for complex exploration missions will also be provided. New on-board software for autonomous science data acquisition for planetary exploration will be described, as well as advanced systems for safe planetary landings. A new multi-agent architecture that addresses some of the challenges of autonomous systems will be presented. Autonomous operation of ground systems will also be considered, including software for autonomous in-situ propellant production and management, and closed- loop ecological life support systems (CELSS). Finally, plans and directions for the future will be discussed.

Computing architecture for autonomous microgrids

DOEpatents

Goldsmith, Steven Y.

2015-09-29

A computing architecture that facilitates autonomously controlling operations of a microgrid is described herein. A microgrid network includes numerous computing devices that execute intelligent agents, each of which is assigned to a particular entity (load, source, storage device, or switch) in the microgrid. The intelligent agents can execute in accordance with predefined protocols to collectively perform computations that facilitate uninterrupted control of the .

Optimal Computing Budget Allocation for Particle Swarm Optimization in Stochastic Optimization.

PubMed

Zhang, Si; Xu, Jie; Lee, Loo Hay; Chew, Ek Peng; Wong, Wai Peng; Chen, Chun-Hung

2017-04-01

Particle Swarm Optimization (PSO) is a popular metaheuristic for deterministic optimization. Originated in the interpretations of the movement of individuals in a bird flock or fish school, PSO introduces the concept of personal best and global best to simulate the pattern of searching for food by flocking and successfully translate the natural phenomena to the optimization of complex functions. Many real-life applications of PSO cope with stochastic problems. To solve a stochastic problem using PSO, a straightforward approach is to equally allocate computational effort among all particles and obtain the same number of samples of fitness values. This is not an efficient use of computational budget and leaves considerable room for improvement. This paper proposes a seamless integration of the concept of optimal computing budget allocation (OCBA) into PSO to improve the computational efficiency of PSO for stochastic optimization problems. We derive an asymptotically optimal allocation rule to intelligently determine the number of samples for all particles such that the PSO algorithm can efficiently select the personal best and global best when there is stochastic estimation noise in fitness values. We also propose an easy-to-implement sequential procedure. Numerical tests show that our new approach can obtain much better results using the same amount of computational effort.

Optimal Computing Budget Allocation for Particle Swarm Optimization in Stochastic Optimization

PubMed Central

Zhang, Si; Xu, Jie; Lee, Loo Hay; Chew, Ek Peng; Chen, Chun-Hung

2017-01-01

Particle Swarm Optimization (PSO) is a popular metaheuristic for deterministic optimization. Originated in the interpretations of the movement of individuals in a bird flock or fish school, PSO introduces the concept of personal best and global best to simulate the pattern of searching for food by flocking and successfully translate the natural phenomena to the optimization of complex functions. Many real-life applications of PSO cope with stochastic problems. To solve a stochastic problem using PSO, a straightforward approach is to equally allocate computational effort among all particles and obtain the same number of samples of fitness values. This is not an efficient use of computational budget and leaves considerable room for improvement. This paper proposes a seamless integration of the concept of optimal computing budget allocation (OCBA) into PSO to improve the computational efficiency of PSO for stochastic optimization problems. We derive an asymptotically optimal allocation rule to intelligently determine the number of samples for all particles such that the PSO algorithm can efficiently select the personal best and global best when there is stochastic estimation noise in fitness values. We also propose an easy-to-implement sequential procedure. Numerical tests show that our new approach can obtain much better results using the same amount of computational effort. PMID:29170617

Particle Swarm Learning Algorithm Based on Adjustment of Parameter and its Applications Assessment of Agricultural Projects

NASA Astrophysics Data System (ADS)

Yang, Shanlin; Zhu, Weidong; Chen, Li

The particle swarm, which optimizes neural networks, has overcome its disadvantage of slow convergent speed and shortcoming of local optimum. The parameter that the particle swarm optimization relates to is not much. But it has strongly sensitivity to the parameter. In this paper, we applied PSO-BP to evaluate the environmental effect of an agricultural project, and researched application and Particle Swarm learning algorithm based on adjustment of parameter. This paper, we use MATLAB language .The particle number is 5, 30, 50, 90, and the inertia weight is 0.4, 0.6, and 0.8 separately. Calculate 10 times under each same parameter, and analyze the influence under the same parameter. Result is indicated that the number of particles is in 25 ~ 30 and the inertia weight is in 0.6 ~ 0.7, and the result of optimization is satisfied.

VDLLA: A virtual daddy-long legs optimization

NASA Astrophysics Data System (ADS)

Yaakub, Abdul Razak; Ghathwan, Khalil I.

2016-08-01

Swarm intelligence is a strong optimization algorithm based on a biological behavior of insects or animals. The success of any optimization algorithm is depending on the balance between exploration and exploitation. In this paper, we present a new swarm intelligence algorithm, which is based on daddy long legs spider (VDLLA) as a new optimization algorithm with virtual behavior. In VDLLA, each agent (spider) has nine positions which represent the legs of spider and each position represent one solution. The proposed VDLLA is tested on four standard functions using average fitness, Medium fitness and standard deviation. The results of proposed VDLLA have been compared against Particle Swarm Optimization (PSO), Differential Evolution (DE) and Bat Inspired Algorithm (BA). Additionally, the T-Test has been conducted to show the significant deference between our proposed and other algorithms. VDLLA showed very promising results on benchmark test functions for unconstrained optimization problems and also significantly improved the original swarm algorithms.

Novel probabilistic and distributed algorithms for guidance, control, and nonlinear estimation of large-scale multi-agent systems

NASA Astrophysics Data System (ADS)

Bandyopadhyay, Saptarshi

Multi-agent systems are widely used for constructing a desired formation shape, exploring an area, surveillance, coverage, and other cooperative tasks. This dissertation introduces novel algorithms in the three main areas of shape formation, distributed estimation, and attitude control of large-scale multi-agent systems. In the first part of this dissertation, we address the problem of shape formation for thousands to millions of agents. Here, we present two novel algorithms for guiding a large-scale swarm of robotic systems into a desired formation shape in a distributed and scalable manner. These probabilistic swarm guidance algorithms adopt an Eulerian framework, where the physical space is partitioned into bins and the swarm's density distribution over each bin is controlled using tunable Markov chains. In the first algorithm - Probabilistic Swarm Guidance using Inhomogeneous Markov Chains (PSG-IMC) - each agent determines its bin transition probabilities using a time-inhomogeneous Markov chain that is constructed in real-time using feedback from the current swarm distribution. This PSG-IMC algorithm minimizes the expected cost of the transitions required to achieve and maintain the desired formation shape, even when agents are added to or removed from the swarm. The algorithm scales well with a large number of agents and complex formation shapes, and can also be adapted for area exploration applications. In the second algorithm - Probabilistic Swarm Guidance using Optimal Transport (PSG-OT) - each agent determines its bin transition probabilities by solving an optimal transport problem, which is recast as a linear program. In the presence of perfect feedback of the current swarm distribution, this algorithm minimizes the given cost function, guarantees faster convergence, reduces the number of transitions for achieving the desired formation, and is robust to disturbances or damages to the formation. We demonstrate the effectiveness of these two proposed swarm guidance algorithms using results from numerical simulations and closed-loop hardware experiments on multiple quadrotors. In the second part of this dissertation, we present two novel discrete-time algorithms for distributed estimation, which track a single target using a network of heterogeneous sensing agents. The Distributed Bayesian Filtering (DBF) algorithm, the sensing agents combine their normalized likelihood functions using the logarithmic opinion pool and the discrete-time dynamic average consensus algorithm. Each agent's estimated likelihood function converges to an error ball centered on the joint likelihood function of the centralized multi-sensor Bayesian filtering algorithm. Using a new proof technique, the convergence, stability, and robustness properties of the DBF algorithm are rigorously characterized. The explicit bounds on the time step of the robust DBF algorithm are shown to depend on the time-scale of the target dynamics. Furthermore, the DBF algorithm for linear-Gaussian models can be cast into a modified form of the Kalman information filter. In the Bayesian Consensus Filtering (BCF) algorithm, the agents combine their estimated posterior pdfs multiple times within each time step using the logarithmic opinion pool scheme. Thus, each agent's consensual pdf minimizes the sum of Kullback-Leibler divergences with the local posterior pdfs. The performance and robust properties of these algorithms are validated using numerical simulations. In the third part of this dissertation, we present an attitude control strategy and a new nonlinear tracking controller for a spacecraft carrying a large object, such as an asteroid or a boulder. If the captured object is larger or comparable in size to the spacecraft and has significant modeling uncertainties, conventional nonlinear control laws that use exact feed-forward cancellation are not suitable because they exhibit a large resultant disturbance torque. The proposed nonlinear tracking control law guarantees global exponential convergence of tracking errors with finite-gain Lp stability in the presence of modeling uncertainties and disturbances, and reduces the resultant disturbance torque. Further, this control law permits the use of any attitude representation and its integral control formulation eliminates any constant disturbance. Under small uncertainties, the best strategy for stabilizing the combined system is to track a fuel-optimal reference trajectory using this nonlinear control law, because it consumes the least amount of fuel. In the presence of large uncertainties, the most effective strategy is to track the derivative plus proportional-derivative based reference trajectory, because it reduces the resultant disturbance torque. The effectiveness of the proposed attitude control law is demonstrated by using results of numerical simulation based on an Asteroid Redirect Mission concept. The new algorithms proposed in this dissertation will facilitate the development of versatile autonomous multi-agent systems that are capable of performing a variety of complex tasks in a robust and scalable manner.

Computer Vision and Machine Learning for Autonomous Characterization of AM Powder Feedstocks

NASA Astrophysics Data System (ADS)

DeCost, Brian L.; Jain, Harshvardhan; Rollett, Anthony D.; Holm, Elizabeth A.

2017-03-01

By applying computer vision and machine learning methods, we develop a system to characterize powder feedstock materials for metal additive manufacturing (AM). Feature detection and description algorithms are applied to create a microstructural scale image representation that can be used to cluster, compare, and analyze powder micrographs. When applied to eight commercial feedstock powders, the system classifies powder images into the correct material systems with greater than 95% accuracy. The system also identifies both representative and atypical powder images. These results suggest the possibility of measuring variations in powders as a function of processing history, relating microstructural features of powders to properties relevant to their performance in AM processes, and defining objective material standards based on visual images. A significant advantage of the computer vision approach is that it is autonomous, objective, and repeatable.

A Computationally Inexpensive Optimal Guidance via Radial-Basis-Function Neural Network for Autonomous Soft Landing on Asteroids

PubMed Central

Zhang, Peng; Liu, Keping; Zhao, Bo; Li, Yuanchun

2015-01-01

Optimal guidance is essential for the soft landing task. However, due to its high computational complexities, it is hardly applied to the autonomous guidance. In this paper, a computationally inexpensive optimal guidance algorithm based on the radial basis function neural network (RBFNN) is proposed. The optimization problem of the trajectory for soft landing on asteroids is formulated and transformed into a two-point boundary value problem (TPBVP). Combining the database of initial states with the relative initial co-states, an RBFNN is trained offline. The optimal trajectory of the soft landing is determined rapidly by applying the trained network in the online guidance. The Monte Carlo simulations of soft landing on the Eros433 are performed to demonstrate the effectiveness of the proposed guidance algorithm. PMID:26367382

Determination of equilibrium electron temperature and times using an electron swarm model with BOLSIG+ calculated collision frequencies and rate coefficients

DOE PAGES

Pusateri, Elise N.; Morris, Heidi E.; Nelson, Eric M.; ...

2015-08-04

Electromagnetic pulse (EMP) events produce low-energy conduction electrons from Compton electron or photoelectron ionizations with air. It is important to understand how conduction electrons interact with air in order to accurately predict EMP evolution and propagation. An electron swarm model can be used to monitor the time evolution of conduction electrons in an environment characterized by electric field and pressure. Here a swarm model is developed that is based on the coupled ordinary differential equations (ODEs) described by Higgins et al. (1973), hereinafter HLO. The ODEs characterize the swarm electric field, electron temperature, electron number density, and drift velocity. Importantmore » swarm parameters, the momentum transfer collision frequency, energy transfer collision frequency, and ionization rate, are calculated and compared to the previously reported fitted functions given in HLO. These swarm parameters are found using BOLSIG+, a two term Boltzmann solver developed by Hagelaar and Pitchford (2005), which utilizes updated cross sections from the LXcat website created by Pancheshnyi et al. (2012). We validate the swarm model by comparing to experimental effective ionization coefficient data in Dutton (1975) and drift velocity data in Ruiz-Vargas et al. (2010). In addition, we report on electron equilibrium temperatures and times for a uniform electric field of 1 StatV/cm for atmospheric heights from 0 to 40 km. We show that the equilibrium temperature and time are sensitive to the modifications in the collision frequencies and ionization rate based on the updated electron interaction cross sections.« less

Transport of Particle Swarms Through Fractures

NASA Astrophysics Data System (ADS)

Boomsma, E.; Pyrak-Nolte, L. J.

2011-12-01

The transport of engineered micro- and nano-scale particles through fractured rock is often assumed to occur as dispersions or emulsions. Another potential transport mechanism is the release of particle swarms from natural or industrial processes where small liquid drops, containing thousands to millions of colloidal-size particles, are released over time from seepage or leaks. Swarms have higher velocities than any individual colloid because the interactions among the particles maintain the cohesiveness of the swarm as it falls under gravity. Thus particle swarms give rise to the possibility that engineered particles may be transported farther and faster in fractures than predicted by traditional dispersion models. In this study, the effect of fractures on colloidal swarm cohesiveness and evolution was studied as a swarm falls under gravity and interacts with fracture walls. Transparent acrylic was used to fabricate synthetic fracture samples with either (1) a uniform aperture or (2) a converging aperture followed by a uniform aperture (funnel-shaped). The samples consisted of two blocks that measured 100 x 100 x 50 mm. The separation between these blocks determined the aperture (0.5 mm to 50 mm). During experiments, a fracture was fully submerged in water and swarms were released into it. The swarms consisted of dilute suspensions of either 25 micron soda-lime glass beads (2% by mass) or 3 micron polystyrene fluorescent beads (1% by mass) with an initial volume of 5μL. The swarms were illuminated with a green (525 nm) LED array and imaged optically with a CCD camera. In the uniform aperture fracture, the speed of the swarm prior to bifurcation increased with aperture up to a maximum at a fracture width of approximately 10 mm. For apertures greater than ~15 mm, the velocity was essentially constant with fracture width (but less than at 10 mm). This peak suggests that two competing mechanisms affect swarm velocity in fractures. The wall provides both drag, which slows the swarm, and a cohesive force that prevents swarm expansion and the corresponding decrease in particle density. For apertures >15mm, though the drag force is small, the loss of swarm cohesion dominates. In small apertures (<5mm), the drag from the wall dominates causing a loss in speed even though there is strong confinement. From a force-based particle interaction approach, the initial simulation did not capture the observed experimental behavior, i.e., the distinct peak in swarm velocities was not observed. For the funnel shaped aperture, the swarm was observed to bifurcate immediately upon reaching the intersection between the converging aperture and the uniform aperture portions of the fracture. Furthermore, converging apertures resulted in the deceleration of a swarm. Thus, the rate of transport of particle swarms is strongly affected by fracture aperture. Acknowledgment: The authors wish to acknowledge support of this work by the Geosciences Research Program, Office of Basic Energy Sciences US Department of Energy (DE-FG02-09ER16022).

A comprehensive overview of the applications of artificial life.

PubMed

Kim, Kyung-Joong; Cho, Sung-Bae

2006-01-01

We review the applications of artificial life (ALife), the creation of synthetic life on computers to study, simulate, and understand living systems. The definition and features of ALife are shown by application studies. ALife application fields treated include robot control, robot manufacturing, practical robots, computer graphics, natural phenomenon modeling, entertainment, games, music, economics, Internet, information processing, industrial design, simulation software, electronics, security, data mining, and telecommunications. In order to show the status of ALife application research, this review primarily features a survey of about 180 ALife application articles rather than a selected representation of a few articles. Evolutionary computation is the most popular method for designing such applications, but recently swarm intelligence, artificial immune network, and agent-based modeling have also produced results. Applications were initially restricted to the robotics and computer graphics, but presently, many different applications in engineering areas are of interest.

A shifted hyperbolic augmented Lagrangian-based artificial fish two-swarm algorithm with guaranteed convergence for constrained global optimization

NASA Astrophysics Data System (ADS)

Rocha, Ana Maria A. C.; Costa, M. Fernanda P.; Fernandes, Edite M. G. P.

2016-12-01

This article presents a shifted hyperbolic penalty function and proposes an augmented Lagrangian-based algorithm for non-convex constrained global optimization problems. Convergence to an ?-global minimizer is proved. At each iteration k, the algorithm requires the ?-global minimization of a bound constrained optimization subproblem, where ?. The subproblems are solved by a stochastic population-based metaheuristic that relies on the artificial fish swarm paradigm and a two-swarm strategy. To enhance the speed of convergence, the algorithm invokes the Nelder-Mead local search with a dynamically defined probability. Numerical experiments with benchmark functions and engineering design problems are presented. The results show that the proposed shifted hyperbolic augmented Lagrangian compares favorably with other deterministic and stochastic penalty-based methods.

Optimization of multi-objective micro-grid based on improved particle swarm optimization algorithm

NASA Astrophysics Data System (ADS)

Zhang, Jian; Gan, Yang

2018-04-01

The paper presents a multi-objective optimal configuration model for independent micro-grid with the aim of economy and environmental protection. The Pareto solution set can be obtained by solving the multi-objective optimization configuration model of micro-grid with the improved particle swarm algorithm. The feasibility of the improved particle swarm optimization algorithm for multi-objective model is verified, which provides an important reference for multi-objective optimization of independent micro-grid.

Immune systems are not just for making you feel better: they are for controlling autonomous robots

NASA Astrophysics Data System (ADS)

Rosenblum, Mark

2005-05-01

The typical algorithm for robot autonomous navigation in off-road complex environments involves building a 3D map of the robot's surrounding environment using a 3D sensing modality such as stereo vision or active laser scanning, and generating an instantaneous plan to navigate around hazards. Although there has been steady progress using these methods, these systems suffer from several limitations that cannot be overcome with 3D sensing and planning alone. Geometric sensing alone has no ability to distinguish between compressible and non-compressible materials. As a result, these systems have difficulty in heavily vegetated environments and require sensitivity adjustments across different terrain types. On the planning side, these systems have no ability to learn from their mistakes and avoid problematic environmental situations on subsequent encounters. We have implemented an adaptive terrain classification system based on the Artificial Immune System (AIS) computational model, which is loosely based on the biological immune system, that combines various forms of imaging sensor inputs to produce a "feature labeled" image of the scene categorizing areas as benign or detrimental for autonomous robot navigation. Because of the qualities of the AIS computation model, the resulting system will be able to learn and adapt on its own through interaction with the environment by modifying its interpretation of the sensor data. The feature labeled results from the AIS analysis are inserted into a map and can then be used by a planner to generate a safe route to a goal point. The coupling of diverse visual cues with the malleable AIS computational model will lead to autonomous robotic ground vehicles that require less human intervention for deployment in novel environments and more robust operation as a result of the system's ability to improve its performance through interaction with the environment.

Inverse Trilateration - A Positional Goal Seeking Algorithm for a Swarm of Autonomous Drones

DTIC Science & Technology

2014-06-12

uny ouc or the n clrmus, \\Vt: ca11 •lcvc lop a system of equations for that ptnticular drmu .. ’- thc onn which will IJc att.<~mpting t.o fi11d u...a111l thc at’Tial vchich:. One sc:euerio for f’lctcnuiuing clistaocc to an nbject of unkuown location is to co1is11lcr a trauSJnith’r of lowvm powcr1...U1ing On6owU V"ual ond foertiol SvuinJ. In rroc. or the \\\\Orbhop Oh Visual Control of Mobilr Robols (VlC4’>.MoR) at thc - JEEE/RJS lntrmaiion"I Confl

Electrocardiographic signals and swarm-based support vector machine for hypoglycemia detection.

PubMed

Nuryani, Nuryani; Ling, Steve S H; Nguyen, H T

2012-04-01

Cardiac arrhythmia relating to hypoglycemia is suggested as a cause of death in diabetic patients. This article introduces electrocardiographic (ECG) parameters for artificially induced hypoglycemia detection. In addition, a hybrid technique of swarm-based support vector machine (SVM) is introduced for hypoglycemia detection using the ECG parameters as inputs. In this technique, a particle swarm optimization (PSO) is proposed to optimize the SVM to detect hypoglycemia. In an experiment using medical data of patients with Type 1 diabetes, the introduced ECG parameters show significant contributions to the performance of the hypoglycemia detection and the proposed detection technique performs well in terms of sensitivity and specificity.

Particle Swarms in Fractures: Open Versus Partially Closed Systems

NASA Astrophysics Data System (ADS)

Boomsma, E.; Pyrak-Nolte, L. J.

2014-12-01

In the field, fractures may be isolated or connected to fluid reservoirs anywhere along the perimeter of a fracture. These boundaries affect fluid circulation, flow paths and communication with external reservoirs. The transport of drop like collections of colloidal-sized particles (particle swarms) in open and partially closed systems was studied. A uniform aperture synthetic fracture was constructed using two blocks (100 x 100 x 50 mm) of transparent acrylic placed parallel to each other. The fracture was fully submerged a tank filled with 100cSt silicone oil. Fracture apertures were varied from 5-80 mm. Partially closed systems were created by sealing the sides of the fracture with plastic film. The four boundary conditions study were: (Case 1) open, (Case 2) closed on the sides, (Case 3) closed on the bottom, and (Case 4) closed on both the sides and bottom of the fracture. A 15 μL dilute suspension of soda-lime glass particles in oil (2% by mass) were released into the fracture. Particle swarms were illuminated using a green (525 nm) LED array and imaged with a CCD camera. The presence of the additional boundaries modified the speed of the particle swarms (see figure). In Case 1, enhanced swarm transport was observed for a range of apertures, traveling faster than either very small or very large apertures. In Case 2, swarm velocities were enhanced over a larger range of fracture apertures than in any of the other cases. Case 3 shifted the enhanced transport regime to lower apertures and also reduced swarm speed when compared to Case 2. Finally, Case 4 eliminated the enhanced transport regime entirely. Communication between the fluid in the fracture and an external fluid reservoir resulted in enhanced swarm transport in Cases 1-3. The non-rigid nature of a swarm enables drag from the fracture walls to modify the swarm geometry. The particles composing a swarm reorganize in response to the fracture, elongating the swarm and maintaining its density. Unlike a drop or solid sphere, fracture boundaries do not exclusively decelerate swarm motion but instead produce enhanced swarm transport. Acknowledgments: This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Geosciences Research Program under Award Number (DE-FG02-09ER16022).

Particle Swarm-Based Translation Control for Immersed Tunnel Element in the Hong Kong-Zhuhai-Macao Bridge Project

NASA Astrophysics Data System (ADS)

Li, Jun-jun; Yang, Xiao-jun; Xiao, Ying-jie; Xu, Bo-wei; Wu, Hua-feng

2018-03-01

Immersed tunnel is an important part of the Hong Kong-Zhuhai-Macao Bridge (HZMB) project. In immersed tunnel floating, translation which includes straight and transverse movements is the main working mode. To decide the magnitude and direction of the towing force for each tug, a particle swarm-based translation control method is presented for non-power immersed tunnel element. A sort of linear weighted logarithmic function is exploited to avoid weak subgoals. In simulation, the particle swarm-based control method is evaluated and compared with traditional empirical method in the case of the HZMB project. Simulation results show that the presented method delivers performance improvement in terms of the enhanced surplus towing force.

LinkMind: link optimization in swarming mobile sensor networks.

PubMed

Ngo, Trung Dung

2011-01-01

A swarming mobile sensor network is comprised of a swarm of wirelessly connected mobile robots equipped with various sensors. Such a network can be applied in an uncertain environment for services such as cooperative navigation and exploration, object identification and information gathering. One of the most advantageous properties of the swarming wireless sensor network is that mobile nodes can work cooperatively to organize an ad-hoc network and optimize the network link capacity to maximize the transmission of gathered data from a source to a target. This paper describes a new method of link optimization of swarming mobile sensor networks. The new method is based on combination of the artificial potential force guaranteeing connectivities of the mobile sensor nodes and the max-flow min-cut theorem of graph theory ensuring optimization of the network link capacity. The developed algorithm is demonstrated and evaluated in simulation.

LinkMind: Link Optimization in Swarming Mobile Sensor Networks

PubMed Central

Ngo, Trung Dung

2011-01-01

A swarming mobile sensor network is comprised of a swarm of wirelessly connected mobile robots equipped with various sensors. Such a network can be applied in an uncertain environment for services such as cooperative navigation and exploration, object identification and information gathering. One of the most advantageous properties of the swarming wireless sensor network is that mobile nodes can work cooperatively to organize an ad-hoc network and optimize the network link capacity to maximize the transmission of gathered data from a source to a target. This paper describes a new method of link optimization of swarming mobile sensor networks. The new method is based on combination of the artificial potential force guaranteeing connectivities of the mobile sensor nodes and the max-flow min-cut theorem of graph theory ensuring optimization of the network link capacity. The developed algorithm is demonstrated and evaluated in simulation. PMID:22164070

Prime factorization using quantum annealing and computational algebraic geometry

PubMed Central

Dridi, Raouf; Alghassi, Hedayat

2017-01-01

We investigate prime factorization from two perspectives: quantum annealing and computational algebraic geometry, specifically Gröbner bases. We present a novel autonomous algorithm which combines the two approaches and leads to the factorization of all bi-primes up to just over 200000, the largest number factored to date using a quantum processor. We also explain how Gröbner bases can be used to reduce the degree of Hamiltonians. PMID:28220854

Flexible Wing Base Micro Aerial Vehicles: Towards Flight Autonomy: Vision-Based Horizon Detection for Micro Air Vehicles

NASA Technical Reports Server (NTRS)

Nechyba, Michael C.; Ettinger, Scott M.; Ifju, Peter G.; Wazak, Martin

2002-01-01

Recently substantial progress has been made towards design building and testifying remotely piloted Micro Air Vehicles (MAVs). This progress in overcoming the aerodynamic obstacles to flight at very small scales has, unfortunately, not been matched by similar progress in autonomous MAV flight. Thus, we propose a robust, vision-based horizon detection algorithm as the first step towards autonomous MAVs. In this paper, we first motivate the use of computer vision for the horizon detection task by examining the flight of birds (biological MAVs) and considering other practical factors. We then describe our vision-based horizon detection algorithm, which has been demonstrated at 30 Hz with over 99.9% correct horizon identification, over terrain that includes roads, buildings large and small, meadows, wooded areas, and a lake. We conclude with some sample horizon detection results and preview a companion paper, where the work discussed here forms the core of a complete autonomous flight stability system.

SWARMs Ontology: A Common Information Model for the Cooperation of Underwater Robots.

PubMed

Li, Xin; Bilbao, Sonia; Martín-Wanton, Tamara; Bastos, Joaquim; Rodriguez, Jonathan

2017-03-11

In order to facilitate cooperation between underwater robots, it is a must for robots to exchange information with unambiguous meaning. However, heterogeneity, existing in information pertaining to different robots, is a major obstruction. Therefore, this paper presents a networked ontology, named the Smart and Networking Underwater Robots in Cooperation Meshes (SWARMs) ontology, to address information heterogeneity and enable robots to have the same understanding of exchanged information. The SWARMs ontology uses a core ontology to interrelate a set of domain-specific ontologies, including the mission and planning, the robotic vehicle, the communication and networking, and the environment recognition and sensing ontology. In addition, the SWARMs ontology utilizes ontology constructs defined in the PR-OWL ontology to annotate context uncertainty based on the Multi-Entity Bayesian Network (MEBN) theory. Thus, the SWARMs ontology can provide both a formal specification for information that is necessarily exchanged between robots and a command and control entity, and also support for uncertainty reasoning. A scenario on chemical pollution monitoring is described and used to showcase how the SWARMs ontology can be instantiated, be extended, represent context uncertainty, and support uncertainty reasoning.

Particle Swarm Optimization Toolbox

NASA Technical Reports Server (NTRS)

Grant, Michael J.

2010-01-01

The Particle Swarm Optimization Toolbox is a library of evolutionary optimization tools developed in the MATLAB environment. The algorithms contained in the library include a genetic algorithm (GA), a single-objective particle swarm optimizer (SOPSO), and a multi-objective particle swarm optimizer (MOPSO). Development focused on both the SOPSO and MOPSO. A GA was included mainly for comparison purposes, and the particle swarm optimizers appeared to perform better for a wide variety of optimization problems. All algorithms are capable of performing unconstrained and constrained optimization. The particle swarm optimizers are capable of performing single and multi-objective optimization. The SOPSO and MOPSO algorithms are based on swarming theory and bird-flocking patterns to search the trade space for the optimal solution or optimal trade in competing objectives. The MOPSO generates Pareto fronts for objectives that are in competition. A GA, based on Darwin evolutionary theory, is also included in the library. The GA consists of individuals that form a population in the design space. The population mates to form offspring at new locations in the design space. These offspring contain traits from both of the parents. The algorithm is based on this combination of traits from parents to hopefully provide an improved solution than either of the original parents. As the algorithm progresses, individuals that hold these optimal traits will emerge as the optimal solutions. Due to the generic design of all optimization algorithms, each algorithm interfaces with a user-supplied objective function. This function serves as a "black-box" to the optimizers in which the only purpose of this function is to evaluate solutions provided by the optimizers. Hence, the user-supplied function can be numerical simulations, analytical functions, etc., since the specific detail of this function is of no concern to the optimizer. These algorithms were originally developed to support entry trajectory and guidance design for the Mars Science Laboratory mission but may be applied to any optimization problem.

Hysteresis compensation of the Prandtl-Ishlinskii model for piezoelectric actuators using modified particle swarm optimization with chaotic map.

PubMed

Long, Zhili; Wang, Rui; Fang, Jiwen; Dai, Xufei; Li, Zuohua

2017-07-01

Piezoelectric actuators invariably exhibit hysteresis nonlinearities that tend to become significant under the open-loop condition and could cause oscillations and errors in nanometer-positioning tasks. Chaotic map modified particle swarm optimization (MPSO) is proposed and implemented to identify the Prandtl-Ishlinskii model for piezoelectric actuators. Hysteresis compensation is attained through application of an inverse Prandtl-Ishlinskii model, in which the parameters are formulated based on the original model with chaotic map MPSO. To strengthen the diversity and improve the searching ergodicity of the swarm, an initial method of adaptive inertia weight based on a chaotic map is proposed. To compare and prove that the swarm's convergence occurs before stochastic initialization and to attain an optimal particle swarm optimization algorithm, the parameters of a proportional-integral-derivative controller are searched using self-tuning, and the simulated results are used to verify the search effectiveness of chaotic map MPSO. The results show that chaotic map MPSO is superior to its competitors for identifying the Prandtl-Ishlinskii model and that the inverse Prandtl-Ishlinskii model can provide hysteresis compensation under different conditions in a simple and effective manner.

Reinforcement Learning with Autonomous Small Unmanned Aerial Vehicles in Cluttered Environments

NASA Technical Reports Server (NTRS)

Tran, Loc; Cross, Charles; Montague, Gilbert; Motter, Mark; Neilan, James; Qualls, Garry; Rothhaar, Paul; Trujillo, Anna; Allen, B. Danette

2015-01-01

We present ongoing work in the Autonomy Incubator at NASA Langley Research Center (LaRC) exploring the efficacy of a data set aggregation approach to reinforcement learning for small unmanned aerial vehicle (sUAV) flight in dense and cluttered environments with reactive obstacle avoidance. The goal is to learn an autonomous flight model using training experiences from a human piloting a sUAV around static obstacles. The training approach uses video data from a forward-facing camera that records the human pilot's flight. Various computer vision based features are extracted from the video relating to edge and gradient information. The recorded human-controlled inputs are used to train an autonomous control model that correlates the extracted feature vector to a yaw command. As part of the reinforcement learning approach, the autonomous control model is iteratively updated with feedback from a human agent who corrects undesired model output. This data driven approach to autonomous obstacle avoidance is explored for simulated forest environments furthering autonomous flight under the tree canopy research. This enables flight in previously inaccessible environments which are of interest to NASA researchers in Earth and Atmospheric sciences.

An Improved Swarm Optimization for Parameter Estimation and Biological Model Selection

PubMed Central

Abdullah, Afnizanfaizal; Deris, Safaai; Mohamad, Mohd Saberi; Anwar, Sohail

2013-01-01

One of the key aspects of computational systems biology is the investigation on the dynamic biological processes within cells. Computational models are often required to elucidate the mechanisms and principles driving the processes because of the nonlinearity and complexity. The models usually incorporate a set of parameters that signify the physical properties of the actual biological systems. In most cases, these parameters are estimated by fitting the model outputs with the corresponding experimental data. However, this is a challenging task because the available experimental data are frequently noisy and incomplete. In this paper, a new hybrid optimization method is proposed to estimate these parameters from the noisy and incomplete experimental data. The proposed method, called Swarm-based Chemical Reaction Optimization, integrates the evolutionary searching strategy employed by the Chemical Reaction Optimization, into the neighbouring searching strategy of the Firefly Algorithm method. The effectiveness of the method was evaluated using a simulated nonlinear model and two biological models: synthetic transcriptional oscillators, and extracellular protease production models. The results showed that the accuracy and computational speed of the proposed method were better than the existing Differential Evolution, Firefly Algorithm and Chemical Reaction Optimization methods. The reliability of the estimated parameters was statistically validated, which suggests that the model outputs produced by these parameters were valid even when noisy and incomplete experimental data were used. Additionally, Akaike Information Criterion was employed to evaluate the model selection, which highlighted the capability of the proposed method in choosing a plausible model based on the experimental data. In conclusion, this paper presents the effectiveness of the proposed method for parameter estimation and model selection problems using noisy and incomplete experimental data. This study is hoped to provide a new insight in developing more accurate and reliable biological models based on limited and low quality experimental data. PMID:23593445

On Distributed Strategies in Defense of a High Value Unit (HVU) Against a Swarm Attack

DTIC Science & Technology

2012-09-01

function [ lam ,U] = tqr(a,b,U) % [ lam u] = tqr(a,b) or [ lam U] = tqr(a,b,U... lam u] = tqr(a,b): % % The column lam contains the eigenvalues of the Hermitian tridiagonal % matrix T = mxt(a,b) computed by one version of the...computed. The computed eigenvalues are real and are sorted to be % nonincreasing. % % [ lam U] = tqr(a,b,U): % % This replaces the input U

HBC-Evo: predicting human breast cancer by exploiting amino acid sequence-based feature spaces and evolutionary ensemble system.

PubMed

Majid, Abdul; Ali, Safdar

2015-01-01

We developed genetic programming (GP)-based evolutionary ensemble system for the early diagnosis, prognosis and prediction of human breast cancer. This system has effectively exploited the diversity in feature and decision spaces. First, individual learners are trained in different feature spaces using physicochemical properties of protein amino acids. Their predictions are then stacked to develop the best solution during GP evolution process. Finally, results for HBC-Evo system are obtained with optimal threshold, which is computed using particle swarm optimization. Our novel approach has demonstrated promising results compared to state of the art approaches.

Overview of computational control research at UT Austin

NASA Technical Reports Server (NTRS)

Bong, Wie

1989-01-01

An overview of current research activities at UT Austin is presented to discuss certain technical issues in the following areas: (1) Computer-Aided Nonlinear Control Design: In this project, the describing function method is employed for the nonlinear control analysis and design of a flexible spacecraft equipped with pulse modulated reaction jets. INCA program has been enhanced to allow the numerical calculation of describing functions as well as the nonlinear limit cycle analysis capability in the frequency domain; (2) Robust Linear Quadratic Gaussian (LQG) Compensator Synthesis: Robust control design techniques and software tools are developed for flexible space structures with parameter uncertainty. In particular, an interactive, robust multivariable control design capability is being developed for INCA program; and (3) LQR-Based Autonomous Control System for the Space Station: In this project, real time implementation of LQR-based autonomous control system is investigated for the space station with time-varying inertias and with significant multibody dynamic interactions.

Fuzzy Decision-Making Fuser (FDMF) for Integrating Human-Machine Autonomous (HMA) Systems with Adaptive Evidence Sources.

PubMed

Liu, Yu-Ting; Pal, Nikhil R; Marathe, Amar R; Wang, Yu-Kai; Lin, Chin-Teng

2017-01-01

A brain-computer interface (BCI) creates a direct communication pathway between the human brain and an external device or system. In contrast to patient-oriented BCIs, which are intended to restore inoperative or malfunctioning aspects of the nervous system, a growing number of BCI studies focus on designing auxiliary systems that are intended for everyday use. The goal of building these BCIs is to provide capabilities that augment existing intact physical and mental capabilities. However, a key challenge to BCI research is human variability; factors such as fatigue, inattention, and stress vary both across different individuals and for the same individual over time. If these issues are addressed, autonomous systems may provide additional benefits that enhance system performance and prevent problems introduced by individual human variability. This study proposes a human-machine autonomous (HMA) system that simultaneously aggregates human and machine knowledge to recognize targets in a rapid serial visual presentation (RSVP) task. The HMA focuses on integrating an RSVP BCI with computer vision techniques in an image-labeling domain. A fuzzy decision-making fuser (FDMF) is then applied in the HMA system to provide a natural adaptive framework for evidence-based inference by incorporating an integrated summary of the available evidence (i.e., human and machine decisions) and associated uncertainty. Consequently, the HMA system dynamically aggregates decisions involving uncertainties from both human and autonomous agents. The collaborative decisions made by an HMA system can achieve and maintain superior performance more efficiently than either the human or autonomous agents can achieve independently. The experimental results shown in this study suggest that the proposed HMA system with the FDMF can effectively fuse decisions from human brain activities and the computer vision techniques to improve overall performance on the RSVP recognition task. This conclusion demonstrates the potential benefits of integrating autonomous systems with BCI systems.

Fuzzy Decision-Making Fuser (FDMF) for Integrating Human-Machine Autonomous (HMA) Systems with Adaptive Evidence Sources

PubMed Central

Liu, Yu-Ting; Pal, Nikhil R.; Marathe, Amar R.; Wang, Yu-Kai; Lin, Chin-Teng

2017-01-01

A brain-computer interface (BCI) creates a direct communication pathway between the human brain and an external device or system. In contrast to patient-oriented BCIs, which are intended to restore inoperative or malfunctioning aspects of the nervous system, a growing number of BCI studies focus on designing auxiliary systems that are intended for everyday use. The goal of building these BCIs is to provide capabilities that augment existing intact physical and mental capabilities. However, a key challenge to BCI research is human variability; factors such as fatigue, inattention, and stress vary both across different individuals and for the same individual over time. If these issues are addressed, autonomous systems may provide additional benefits that enhance system performance and prevent problems introduced by individual human variability. This study proposes a human-machine autonomous (HMA) system that simultaneously aggregates human and machine knowledge to recognize targets in a rapid serial visual presentation (RSVP) task. The HMA focuses on integrating an RSVP BCI with computer vision techniques in an image-labeling domain. A fuzzy decision-making fuser (FDMF) is then applied in the HMA system to provide a natural adaptive framework for evidence-based inference by incorporating an integrated summary of the available evidence (i.e., human and machine decisions) and associated uncertainty. Consequently, the HMA system dynamically aggregates decisions involving uncertainties from both human and autonomous agents. The collaborative decisions made by an HMA system can achieve and maintain superior performance more efficiently than either the human or autonomous agents can achieve independently. The experimental results shown in this study suggest that the proposed HMA system with the FDMF can effectively fuse decisions from human brain activities and the computer vision techniques to improve overall performance on the RSVP recognition task. This conclusion demonstrates the potential benefits of integrating autonomous systems with BCI systems. PMID:28676734

A multipopulation PSO based memetic algorithm for permutation flow shop scheduling.

PubMed

Liu, Ruochen; Ma, Chenlin; Ma, Wenping; Li, Yangyang

2013-01-01

The permutation flow shop scheduling problem (PFSSP) is part of production scheduling, which belongs to the hardest combinatorial optimization problem. In this paper, a multipopulation particle swarm optimization (PSO) based memetic algorithm (MPSOMA) is proposed in this paper. In the proposed algorithm, the whole particle swarm population is divided into three subpopulations in which each particle evolves itself by the standard PSO and then updates each subpopulation by using different local search schemes such as variable neighborhood search (VNS) and individual improvement scheme (IIS). Then, the best particle of each subpopulation is selected to construct a probabilistic model by using estimation of distribution algorithm (EDA) and three particles are sampled from the probabilistic model to update the worst individual in each subpopulation. The best particle in the entire particle swarm is used to update the global optimal solution. The proposed MPSOMA is compared with two recently proposed algorithms, namely, PSO based memetic algorithm (PSOMA) and hybrid particle swarm optimization with estimation of distribution algorithm (PSOEDA), on 29 well-known PFFSPs taken from OR-library, and the experimental results show that it is an effective approach for the PFFSP.

DoD High Performance Computing Modernization Program Users Group Conference (HPCMP UGC 2011) Held in Portland, Oregon on June 20-23, 2011

DTIC Science & Technology

2011-06-01

4. Conclusion The Web -based AGeS system described in this paper is a computationally-efficient and scalable system for high- throughput genome...method for protecting web services involves making them more resilient to attack using autonomic computing techniques. This paper presents our initial...20–23, 2011 2011 DoD High Performance Computing Modernzation Program Users Group Conference HPCMP UGC 2011 The papers in this book comprise the

Magma intrusion near Volcan Tancitaro: Evidence from seismic analysis

DOE PAGES

Pinzon, Juan I.; Nunez-Cornu, Francisco J.; Rowe, Charlotte Anne

2016-11-17

Between May and June 2006, an earthquake swarm occurred near Volcan Tancítaro in Mexico, which was recorded by a temporary seismic deployment known as the MARS network. We located ~1000 events from this seismic swarm. Previous earthquake swarms in the area were reported in the years 1997, 1999 and 2000. We relocate and analyze the evolution and properties of the 2006 earthquake swarm, employing a waveform cross-correlation-based phase repicking technique. Hypocenters from 911 events were located and divided into eighteen families having a correlation coefficient at or above 0.75. 90% of the earthquakes provide at least sixteen phase picks. Wemore » used the single-event location code Hypo71 and the P-wave velocity model used by the Jalisco Seismic and Accelerometer Network to improve hypocenters based on the correlation-adjusted phase arrival times. We relocated 121 earthquakes, which show clearly two clusters, between 9–10 km and 3–4 km depth respectively. The average location error estimates are <1 km epicentrally, and <2 km in depth, for the largest event in each cluster. Depths of seismicity migrate upward from 16 to 3.5 km and exhibit a NE-SW trend. The swarm first migrated toward Paricutin Volcano but by mid-June began propagating back toward Volcán Tancítaro. In addition to its persistence, noteworthy aspects of this swarm include a quasi-exponential increase in the rate of activity within the first 15 days; a b-value of 1.47; a jug-shaped hypocenter distribution; a shoaling rate of ~5 km/month within the deeper cluster, and a composite focal mechanism solution indicating largely reverse faulting. As a result, these features of the swarm suggest a magmatic source elevating the crustal strain beneath Volcan Tancítaro.« less

Magma intrusion near Volcan Tancitaro: Evidence from seismic analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pinzon, Juan I.; Nunez-Cornu, Francisco J.; Rowe, Charlotte Anne

Between May and June 2006, an earthquake swarm occurred near Volcan Tancítaro in Mexico, which was recorded by a temporary seismic deployment known as the MARS network. We located ~1000 events from this seismic swarm. Previous earthquake swarms in the area were reported in the years 1997, 1999 and 2000. We relocate and analyze the evolution and properties of the 2006 earthquake swarm, employing a waveform cross-correlation-based phase repicking technique. Hypocenters from 911 events were located and divided into eighteen families having a correlation coefficient at or above 0.75. 90% of the earthquakes provide at least sixteen phase picks. Wemore » used the single-event location code Hypo71 and the P-wave velocity model used by the Jalisco Seismic and Accelerometer Network to improve hypocenters based on the correlation-adjusted phase arrival times. We relocated 121 earthquakes, which show clearly two clusters, between 9–10 km and 3–4 km depth respectively. The average location error estimates are <1 km epicentrally, and <2 km in depth, for the largest event in each cluster. Depths of seismicity migrate upward from 16 to 3.5 km and exhibit a NE-SW trend. The swarm first migrated toward Paricutin Volcano but by mid-June began propagating back toward Volcán Tancítaro. In addition to its persistence, noteworthy aspects of this swarm include a quasi-exponential increase in the rate of activity within the first 15 days; a b-value of 1.47; a jug-shaped hypocenter distribution; a shoaling rate of ~5 km/month within the deeper cluster, and a composite focal mechanism solution indicating largely reverse faulting. As a result, these features of the swarm suggest a magmatic source elevating the crustal strain beneath Volcan Tancítaro.« less

Magma intrusion near Volcan Tancítaro: Evidence from seismic analysis

NASA Astrophysics Data System (ADS)

Pinzón, Juan I.; Núñez-Cornú, Francisco J.; Rowe, Charlotte A.

2017-01-01

Between May and June 2006, an earthquake swarm occurred near Volcan Tancítaro in Mexico, which was recorded by a temporary seismic deployment known as the MARS network. We located ∼1000 events from this seismic swarm. Previous earthquake swarms in the area were reported in the years 1997, 1999 and 2000. We relocate and analyze the evolution and properties of the 2006 earthquake swarm, employing a waveform cross-correlation-based phase repicking technique. Hypocenters from 911 events were located and divided into eighteen families having a correlation coefficient at or above 0.75. 90% of the earthquakes provide at least sixteen phase picks. We used the single-event location code Hypo71 and the P-wave velocity model used by the Jalisco Seismic and Accelerometer Network to improve hypocenters based on the correlation-adjusted phase arrival times. We relocated 121 earthquakes, which show clearly two clusters, between 9-10 km and 3-4 km depth respectively. The average location error estimates are <1 km epicentrally, and <2 km in depth, for the largest event in each cluster. Depths of seismicity migrate upward from 16 to 3.5 km and exhibit a NE-SW trend. The swarm first migrated toward Paricutin Volcano but by mid-June began propagating back toward Volcán Tancítaro. In addition to its persistence, noteworthy aspects of this swarm include a quasi-exponential increase in the rate of activity within the first 15 days; a b-value of 1.47; a jug-shaped hypocenter distribution; a shoaling rate of ∼5 km/month within the deeper cluster, and a composite focal mechanism solution indicating largely reverse faulting. These features of the swarm suggest a magmatic source elevating the crustal strain beneath Volcan Tancítaro.

Integrated Swarming Operations for Air Base Defense: Applications in Irregular Warfare

DTIC Science & Technology

2006-06-01

Giordano THIS PAGE INTENTIONALLY LEFT BLANK i REPORT DOCUMENTATION PAGE Form Approved OMB No. 0704-0188 Public...Gray Approved by: John Arquilla Thesis Advisor Frank Giordano Second Reader Gordon McCormick Chairman, Department of Defense...Frank Giordano for all their help in guiding me down the path of swarming, counterinsurgency and applications for air base defense in irregular

Hybrid swarm intelligence optimization approach for optimal data storage position identification in wireless sensor networks.

PubMed

Mohanasundaram, Ranganathan; Periasamy, Pappampalayam Sanmugam

2015-01-01

The current high profile debate with regard to data storage and its growth have become strategic task in the world of networking. It mainly depends on the sensor nodes called producers, base stations, and also the consumers (users and sensor nodes) to retrieve and use the data. The main concern dealt here is to find an optimal data storage position in wireless sensor networks. The works that have been carried out earlier did not utilize swarm intelligence based optimization approaches to find the optimal data storage positions. To achieve this goal, an efficient swam intelligence approach is used to choose suitable positions for a storage node. Thus, hybrid particle swarm optimization algorithm has been used to find the suitable positions for storage nodes while the total energy cost of data transmission is minimized. Clustering-based distributed data storage is utilized to solve clustering problem using fuzzy-C-means algorithm. This research work also considers the data rates and locations of multiple producers and consumers to find optimal data storage positions. The algorithm is implemented in a network simulator and the experimental results show that the proposed clustering and swarm intelligence based ODS strategy is more effective than the earlier approaches.

Multi Dimensional Honey Bee Foraging Algorithm Based on Optimal Energy Consumption

NASA Astrophysics Data System (ADS)

Saritha, R.; Vinod Chandra, S. S.

2017-10-01

In this paper a new nature inspired algorithm is proposed based on natural foraging behavior of multi-dimensional honey bee colonies. This method handles issues that arise when food is shared from multiple sources by multiple swarms at multiple destinations. The self organizing nature of natural honey bee swarms in multiple colonies is based on the principle of energy consumption. Swarms of multiple colonies select a food source to optimally fulfill the requirements of its colonies. This is based on the energy requirement for transporting food between a source and destination. Minimum use of energy leads to maximizing profit in each colony. The mathematical model proposed here is based on this principle. This has been successfully evaluated by applying it on multi-objective transportation problem for optimizing cost and time. The algorithm optimizes the needs at each destination in linear time.

Spacecube: A Family of Reconfigurable Hybrid On-Board Science Data Processors

NASA Technical Reports Server (NTRS)

Flatley, Thomas P.

2015-01-01

SpaceCube is a family of Field Programmable Gate Array (FPGA) based on-board science data processing systems developed at the NASA Goddard Space Flight Center (GSFC). The goal of the SpaceCube program is to provide 10x to 100x improvements in on-board computing power while lowering relative power consumption and cost. SpaceCube is based on the Xilinx Virtex family of FPGAs, which include processor, FPGA logic and digital signal processing (DSP) resources. These processing elements are leveraged to produce a hybrid science data processing platform that accelerates the execution of algorithms by distributing computational functions to the most suitable elements. This approach enables the implementation of complex on-board functions that were previously limited to ground based systems, such as on-board product generation, data reduction, calibration, classification, eventfeature detection, data mining and real-time autonomous operations. The system is fully reconfigurable in flight, including data parameters, software and FPGA logic, through either ground commanding or autonomously in response to detected eventsfeatures in the instrument data stream.

Episodic dike swarms inferred from near-bottom magnetic anomaly maps at the southern East Pacific Rise

NASA Astrophysics Data System (ADS)

Shah, Anjana K.; Cormier, Marie-Helene; Ryan, William B. F.; Jin, Wen; Sinton, John; Bergmanis, Eric; Carlut, Julie; Bradley, Al; Yoerger, Dana

2003-02-01

Near-bottom, high-resolution magnetic field data gathered at the southern East Pacific Rise near 17°28'S, 18°14'S, and 18°37'S, using the autonomous underwater vehicle Autonomous Benthic Explorer (ABE) echo various geologic structures, including void space within lobate caverns, recent pillow mounds, and hydrothermal vent activity. This study is focused on a magnetic field low extending several kilometers along axis, coincident with a trough created by the draining of a lava lake during a highly effusive fissure eruption at 17°28'S. Similar lows are observed at three other drained lava lake troughs, including one which is at least 1800 years old, residing 400 m away from the present-day axis. We attribute these lows to the presence of shallow dike swarms. The degree to which other geologic features may contribute to the lows is constrained using geologic, geophysical, and geochemical observations and forward modeling. Compositional analyses of Alvin samples at 17°28'S do not support Fe or Ti variations as a primary source. Hypotheses requiring hydrothermal alteration and porosity variations are both inconsistent with geologic observations and near-bottom gravity data analysis from similar areas. Previous mappings between paleointensity variations and the observed magnetic field over distances of several kilometers from the axis suggest that such variations do not create the field low. The dominant source of the magnetization low is most likely the presence of a 100-200 m wide region of shallow dikes which are poorly magnetized relative to extrusives, or a region heated above magnetic blocking or Curie temperatures by intrusions during the most recent eruption (though the latter interpretation cannot explain the low at the fossil trough). In the first case, this extrusive thinning implies a change in eruptive behavior over the last 750-1500 years given the local spreading rate. For the latter case, thermal models suggest the anomaly had to have been created by a dike swarms totaling at least 45 m width during the most recent eruption(s), corresponding to ˜300 years of plate spreading. Models indicate that the source of the low is centered slightly east of the axial trough. This offset suggests that the axis has been progressively migrating westward over the past millennium, consistent with other studies covering greater length and timescales. Westward migration provides an explanation for the preferential emplacement of recent lavas flows west of the axis, evident in ABE bathymetry and submersible observations.

A Localization Method for Underwater Wireless Sensor Networks Based on Mobility Prediction and Particle Swarm Optimization Algorithms

PubMed Central

Zhang, Ying; Liang, Jixing; Jiang, Shengming; Chen, Wei

2016-01-01

Due to their special environment, Underwater Wireless Sensor Networks (UWSNs) are usually deployed over a large sea area and the nodes are usually floating. This results in a lower beacon node distribution density, a longer time for localization, and more energy consumption. Currently most of the localization algorithms in this field do not pay enough consideration on the mobility of the nodes. In this paper, by analyzing the mobility patterns of water near the seashore, a localization method for UWSNs based on a Mobility Prediction and a Particle Swarm Optimization algorithm (MP-PSO) is proposed. In this method, the range-based PSO algorithm is used to locate the beacon nodes, and their velocities can be calculated. The velocity of an unknown node is calculated by using the spatial correlation of underwater object’s mobility, and then their locations can be predicted. The range-based PSO algorithm may cause considerable energy consumption and its computation complexity is a little bit high, nevertheless the number of beacon nodes is relatively smaller, so the calculation for the large number of unknown nodes is succinct, and this method can obviously decrease the energy consumption and time cost of localizing these mobile nodes. The simulation results indicate that this method has higher localization accuracy and better localization coverage rate compared with some other widely used localization methods in this field. PMID:26861348

[Application of optimized parameters SVM based on photoacoustic spectroscopy method in fault diagnosis of power transformer].

PubMed

Zhang, Yu-xin; Cheng, Zhi-feng; Xu, Zheng-ping; Bai, Jing

2015-01-01

In order to solve the problems such as complex operation, consumption for the carrier gas and long test period in traditional power transformer fault diagnosis approach based on dissolved gas analysis (DGA), this paper proposes a new method which is detecting 5 types of characteristic gas content in transformer oil such as CH4, C2H2, C2H4, C2H6 and H2 based on photoacoustic Spectroscopy and C2H2/C2H4, CH4/H2, C2H4/C2H6 three-ratios data are calculated. The support vector machine model was constructed using cross validation method under five support vector machine functions and four kernel functions, heuristic algorithms were used in parameter optimization for penalty factor c and g, which to establish the best SVM model for the highest fault diagnosis accuracy and the fast computing speed. Particles swarm optimization and genetic algorithm two types of heuristic algorithms were comparative studied in this paper for accuracy and speed in optimization. The simulation result shows that SVM model composed of C-SVC, RBF kernel functions and genetic algorithm obtain 97. 5% accuracy in test sample set and 98. 333 3% accuracy in train sample set, and genetic algorithm was about two times faster than particles swarm optimization in computing speed. The methods described in this paper has many advantages such as simple operation, non-contact measurement, no consumption for the carrier gas, long test period, high stability and sensitivity, the result shows that the methods described in this paper can instead of the traditional transformer fault diagnosis by gas chromatography and meets the actual project needs in transformer fault diagnosis.

Prognostics Methodology for Complex Systems

NASA Technical Reports Server (NTRS)

Gulati, Sandeep; Mackey, Ryan

2003-01-01

An automatic method to schedule maintenance and repair of complex systems is produced based on a computational structure called the Informed Maintenance Grid (IMG). This method provides solutions to the two fundamental problems in autonomic logistics: (1) unambiguous detection of deterioration or impending loss of function and (2) determination of the time remaining to perform maintenance or other corrective action based upon information from the system. The IMG provides a health determination over the medium-to-longterm operation of the system, from one or more days to years of study. The IMG is especially applicable to spacecraft and both piloted and autonomous aircraft, or industrial control processes.

Authentication and Encryption Using Modified Elliptic Curve Cryptography with Particle Swarm Optimization and Cuckoo Search Algorithm

NASA Astrophysics Data System (ADS)

Kota, Sujatha; Padmanabhuni, Venkata Nageswara Rao; Budda, Kishor; K, Sruthi

2018-05-01

Elliptic Curve Cryptography (ECC) uses two keys private key and public key and is considered as a public key cryptographic algorithm that is used for both authentication of a person and confidentiality of data. Either one of the keys is used in encryption and other in decryption depending on usage. Private key is used in encryption by the user and public key is used to identify user in the case of authentication. Similarly, the sender encrypts with the private key and the public key is used to decrypt the message in case of confidentiality. Choosing the private key is always an issue in all public key Cryptographic Algorithms such as RSA, ECC. If tiny values are chosen in random the security of the complete algorithm becomes an issue. Since the Public key is computed based on the Private Key, if they are not chosen optimally they generate infinity values. The proposed Modified Elliptic Curve Cryptography uses selection in either of the choices; the first option is by using Particle Swarm Optimization and the second option is by using Cuckoo Search Algorithm for randomly choosing the values. The proposed algorithms are developed and tested using sample database and both are found to be secured and reliable. The test results prove that the private key is chosen optimally not repetitive or tiny and the computations in public key will not reach infinity.

Seismicity-based estimation of the driving fluid pressure in the case of swarm activity in Western Bohemia

NASA Astrophysics Data System (ADS)

Hainzl, S.; Fischer, T.; Dahm, T.

2012-10-01

Two recent major swarms in Western Bohemia occurred in the years 2000 and 2008 within almost the same portion of a fault close to Novy Kostel. Previous analysis of the year 2000 earthquake swarm revealed that fluid intrusion seemed to initiate the activity whereas stress redistribution by the individual swarm earthquakes played a major role in the further swarm evolution. Here we analyse the new swarm, which occurred in the year 2008, with regard to its correlation to the previous swarm as well its spatiotemporal migration patterns. We find that (i) the main part of the year 2008 activity ruptured fault patches adjacent to the main activity of the swarm 2000, but that also (ii) a significant overlap exists where earthquakes occurred in patches in which stress had been already released by precursory events; (iii) the activity shows a clear migration which can be described by a 1-D (in up-dip direction) diffusion process; (iv) the migration pattern can be equally well explained by a hydrofracture growth, which additionally explains the faster migration in up-dip compared to the down-dip direction as well as the maximum up-dip extension of the activity. We use these observations to estimate the underlying fluid pressure change in two different ways: First, we calculate the stress changes induced by precursory events at the location of each swarm earthquake assuming that observed stress deficits had to be compensated by pore pressure increases; and secondly, we estimate the fluid overpressure by fitting a hydrofracture model to the asymmetric seismicity patterns. Both independent methods indicate that the fluid pressure increase was initially up to 30 MPa.

Autonomous mission planning and scheduling: Innovative, integrated, responsive

NASA Technical Reports Server (NTRS)

Sary, Charisse; Liu, Simon; Hull, Larry; Davis, Randy

1994-01-01

Autonomous mission scheduling, a new concept for NASA ground data systems, is a decentralized and distributed approach to scientific spacecraft planning, scheduling, and command management. Systems and services are provided that enable investigators to operate their own instruments. In autonomous mission scheduling, separate nodes exist for each instrument and one or more operations nodes exist for the spacecraft. Each node is responsible for its own operations which include planning, scheduling, and commanding; and for resolving conflicts with other nodes. One or more database servers accessible to all nodes enable each to share mission and science planning, scheduling, and commanding information. The architecture for autonomous mission scheduling is based upon a realistic mix of state-of-the-art and emerging technology and services, e.g., high performance individual workstations, high speed communications, client-server computing, and relational databases. The concept is particularly suited to the smaller, less complex missions of the future.

Autonomous Flight Safety System Road Test

NASA Technical Reports Server (NTRS)

Simpson, James C.; Zoemer, Roger D.; Forney, Chris S.

2005-01-01

On February 3, 2005, Kennedy Space Center (KSC) conducted the first Autonomous Flight Safety System (AFSS) test on a moving vehicle -- a van driven around the KSC industrial area. A subset of the Phase III design was used consisting of a single computer, GPS receiver, and UPS antenna. The description and results of this road test are described in this report.AFSS is a joint KSC and Wallops Flight Facility project that is in its third phase of development. AFSS is an independent subsystem intended for use with Expendable Launch Vehicles that uses tracking data from redundant onboard sensors to autonomously make flight termination decisions using software-based rules implemented on redundant flight processors. The goals of this project are to increase capabilities by allowing launches from locations that do not have or cannot afford extensive ground-based range safety assets, to decrease range costs, and to decrease reaction time for special situations.

Main field and secular variation candidate models for the 12th IGRF generation after 10 months of Swarm measurements

NASA Astrophysics Data System (ADS)

Saturnino, Diana; Langlais, Benoit; Civet, François; Thébault, Erwan; Mandea, Mioara

2015-06-01

We describe the main field and secular variation candidate models for the 12th generation of the International Geomagnetic Reference Field model. These two models are derived from the same parent model, in which the main field is extrapolated to epoch 2015.0 using its associated secular variation. The parent model is exclusively based on measurements acquired by the European Space Agency Swarm mission between its launch on 11/22/2013 and 09/18/2014. It is computed up to spherical harmonic degree and order 25 for the main field, 13 for the secular variation, and 2 for the external field. A selection on local time rather than on true illumination of the spacecraft was chosen in order to keep more measurements. Data selection based on geomagnetic indices was used to minimize the external field contributions. Measurements were screened and outliers were carefully removed. The model uses magnetic field intensity measurements at all latitudes and magnetic field vector measurements equatorward of 50° absolute quasi-dipole magnetic latitude. A second model using only the vertical component of the measured magnetic field and the total intensity was computed. This companion model offers a slightly better fit to the measurements. These two models are compared and discussed.We discuss in particular the quality of the model which does not use the full vector measurements and underline that this approach may be used when only partial directional information is known. The candidate models and their associated companion models are retrospectively compared to the adopted IGRF which allows us to criticize our own choices.

Autonomic Management of Application Workflows on Hybrid Computing Infrastructure

DOE PAGES

Kim, Hyunjoo; el-Khamra, Yaakoub; Rodero, Ivan; ...

2011-01-01

In this paper, we present a programming and runtime framework that enables the autonomic management of complex application workflows on hybrid computing infrastructures. The framework is designed to address system and application heterogeneity and dynamics to ensure that application objectives and constraints are satisfied. The need for such autonomic system and application management is becoming critical as computing infrastructures become increasingly heterogeneous, integrating different classes of resources from high-end HPC systems to commodity clusters and clouds. For example, the framework presented in this paper can be used to provision the appropriate mix of resources based on application requirements and constraints.more » The framework also monitors the system/application state and adapts the application and/or resources to respond to changing requirements or environment. To demonstrate the operation of the framework and to evaluate its ability, we employ a workflow used to characterize an oil reservoir executing on a hybrid infrastructure composed of TeraGrid nodes and Amazon EC2 instances of various types. Specifically, we show how different applications objectives such as acceleration, conservation and resilience can be effectively achieved while satisfying deadline and budget constraints, using an appropriate mix of dynamically provisioned resources. Our evaluations also demonstrate that public clouds can be used to complement and reinforce the scheduling and usage of traditional high performance computing infrastructure.« less

Distributed Pheromone-Based Swarming Control of Unmanned Air and Ground Vehicles for RSTA

DTIC Science & Technology

2008-03-20

Forthcoming in Proceedings of SPIE Defense & Security Conference, March 2008, Orlando, FL Distributed Pheromone -Based Swarming Control of Unmanned...describes recent advances in a fully distributed digital pheromone algorithm that has demonstrated its effectiveness in managing the complexity of...onboard digital pheromone responding to the needs of the automatic target recognition algorithms. UAVs and UGVs controlled by the same pheromone algorithm

SWARMs Ontology: A Common Information Model for the Cooperation of Underwater Robots

PubMed Central

Li, Xin; Bilbao, Sonia; Martín-Wanton, Tamara; Bastos, Joaquim; Rodriguez, Jonathan

2017-01-01

In order to facilitate cooperation between underwater robots, it is a must for robots to exchange information with unambiguous meaning. However, heterogeneity, existing in information pertaining to different robots, is a major obstruction. Therefore, this paper presents a networked ontology, named the Smart and Networking Underwater Robots in Cooperation Meshes (SWARMs) ontology, to address information heterogeneity and enable robots to have the same understanding of exchanged information. The SWARMs ontology uses a core ontology to interrelate a set of domain-specific ontologies, including the mission and planning, the robotic vehicle, the communication and networking, and the environment recognition and sensing ontology. In addition, the SWARMs ontology utilizes ontology constructs defined in the PR-OWL ontology to annotate context uncertainty based on the Multi-Entity Bayesian Network (MEBN) theory. Thus, the SWARMs ontology can provide both a formal specification for information that is necessarily exchanged between robots and a command and control entity, and also support for uncertainty reasoning. A scenario on chemical pollution monitoring is described and used to showcase how the SWARMs ontology can be instantiated, be extended, represent context uncertainty, and support uncertainty reasoning. PMID:28287468

Resilience and Controllability of Dynamic Collective Behaviors

PubMed Central

Komareji, Mohammad; Bouffanais, Roland

2013-01-01

The network paradigm is used to gain insight into the structural root causes of the resilience of consensus in dynamic collective behaviors, and to analyze the controllability of the swarm dynamics. Here we devise the dynamic signaling network which is the information transfer channel underpinning the swarm dynamics of the directed interagent connectivity based on a topological neighborhood of interactions. The study of the connectedness of the swarm signaling network reveals the profound relationship between group size and number of interacting neighbors, which is found to be in good agreement with field observations on flock of starlings [Ballerini et al. (2008) Proc. Natl. Acad. Sci. USA, 105: 1232]. Using a dynamical model, we generate dynamic collective behaviors enabling us to uncover that the swarm signaling network is a homogeneous clustered small-world network, thus facilitating emergent outcomes if connectedness is maintained. Resilience of the emergent consensus is tested by introducing exogenous environmental noise, which ultimately stresses how deeply intertwined are the swarm dynamics in the physical and network spaces. The availability of the signaling network allows us to analytically establish for the first time the number of driver agents necessary to fully control the swarm dynamics. PMID:24358209

Optimization of shared autonomy vehicle control architectures for swarm operations.

PubMed

Sengstacken, Aaron J; DeLaurentis, Daniel A; Akbarzadeh-T, Mohammad R

2010-08-01

The need for greater capacity in automotive transportation (in the midst of constrained resources) and the convergence of key technologies from multiple domains may eventually produce the emergence of a "swarm" concept of operations. The swarm, which is a collection of vehicles traveling at high speeds and in close proximity, will require technology and management techniques to ensure safe, efficient, and reliable vehicle interactions. We propose a shared autonomy control approach, in which the strengths of both human drivers and machines are employed in concert for this management. Building from a fuzzy logic control implementation, optimal architectures for shared autonomy addressing differing classes of drivers (represented by the driver's response time) are developed through a genetic-algorithm-based search for preferred fuzzy rules. Additionally, a form of "phase transition" from a safe to an unsafe swarm architecture as the amount of sensor capability is varied uncovers key insights on the required technology to enable successful shared autonomy for swarm operations.

A Swarm Optimization Genetic Algorithm Based on Quantum-Behaved Particle Swarm Optimization.

PubMed

Sun, Tao; Xu, Ming-Hai

2017-01-01

Quantum-behaved particle swarm optimization (QPSO) algorithm is a variant of the traditional particle swarm optimization (PSO). The QPSO that was originally developed for continuous search spaces outperforms the traditional PSO in search ability. This paper analyzes the main factors that impact the search ability of QPSO and converts the particle movement formula to the mutation condition by introducing the rejection region, thus proposing a new binary algorithm, named swarm optimization genetic algorithm (SOGA), because it is more like genetic algorithm (GA) than PSO in form. SOGA has crossover and mutation operator as GA but does not need to set the crossover and mutation probability, so it has fewer parameters to control. The proposed algorithm was tested with several nonlinear high-dimension functions in the binary search space, and the results were compared with those from BPSO, BQPSO, and GA. The experimental results show that SOGA is distinctly superior to the other three algorithms in terms of solution accuracy and convergence.

A novel artificial immune clonal selection classification and rule mining with swarm learning model

NASA Astrophysics Data System (ADS)

Al-Sheshtawi, Khaled A.; Abdul-Kader, Hatem M.; Elsisi, Ashraf B.

2013-06-01

Metaheuristic optimisation algorithms have become popular choice for solving complex problems. By integrating Artificial Immune clonal selection algorithm (CSA) and particle swarm optimisation (PSO) algorithm, a novel hybrid Clonal Selection Classification and Rule Mining with Swarm Learning Algorithm (CS2) is proposed. The main goal of the approach is to exploit and explore the parallel computation merit of Clonal Selection and the speed and self-organisation merits of Particle Swarm by sharing information between clonal selection population and particle swarm. Hence, we employed the advantages of PSO to improve the mutation mechanism of the artificial immune CSA and to mine classification rules within datasets. Consequently, our proposed algorithm required less training time and memory cells in comparison to other AIS algorithms. In this paper, classification rule mining has been modelled as a miltiobjective optimisation problem with predictive accuracy. The multiobjective approach is intended to allow the PSO algorithm to return an approximation to the accuracy and comprehensibility border, containing solutions that are spread across the border. We compared our proposed algorithm classification accuracy CS2 with five commonly used CSAs, namely: AIRS1, AIRS2, AIRS-Parallel, CLONALG, and CSCA using eight benchmark datasets. We also compared our proposed algorithm classification accuracy CS2 with other five methods, namely: Naïve Bayes, SVM, MLP, CART, and RFB. The results show that the proposed algorithm is comparable to the 10 studied algorithms. As a result, the hybridisation, built of CSA and PSO, can develop respective merit, compensate opponent defect, and make search-optimal effect and speed better.

Alexithymia and empathy predict changes in autonomic arousal during affective stimulation.

PubMed

Bogdanov, Volodymyr B; Bogdanova, Olena V; Gorlov, Dmytro S; Gorgo, Yuriy P; Dirckx, Joris J J; Makarchuk, Mykola Y; Schoenen, Jean; Critchley, Hugo

2013-09-01

Alexithymia, the inability to describe one's own emotions, is linked to deficits in empathy, manifesting as a diminished capacity to recognize or understand the emotions and mental states of others. Several brain centers of autonomic control and interoception that are activated in empathy are thought to misfunction in alexithymia. We hypothesized that individual differences in autonomic changes under affective stimulation might be associated with differences in alexithymia and empathy. We studied 21 healthy volunteers, comparing their alexithymia and empathy scores with changes in their sympathetic autonomic arousal, indexed by the palmar skin potential level, during 3 tasks: playing a computer game, performing mental arithmetic, and watching a negative emotional valence video. Both autonomic and subjective sense of arousal increased at the beginning of each task and then gradually subsided over the course of the task. Higher autonomic arousal at the onset of the computer game was associated with higher empathy scores, and at the onset of the negative video with higher scores for both empathy and alexithymia. Alexithymia delayed the habituation of autonomic arousal during the computer game, while the empathy score was related to a faster decline in arousal during the negative video task. High alexithymia and high empathy scores were linked to increased autonomic arousal at the onset of emotional stimulation, but were distinguishable in the rates of habituation of the evoked arousal. Our data provide insight into the relationships among interacting psychological traits, physiologic regulation, and the arousal dimension of emotional experience.

Autonomous Aerial Refueling Ground Test Demonstration—A Sensor-in-the-Loop, Non-Tracking Method

PubMed Central

Chen, Chao-I; Koseluk, Robert; Buchanan, Chase; Duerner, Andrew; Jeppesen, Brian; Laux, Hunter

2015-01-01

An essential capability for an unmanned aerial vehicle (UAV) to extend its airborne duration without increasing the size of the aircraft is called the autonomous aerial refueling (AAR). This paper proposes a sensor-in-the-loop, non-tracking method for probe-and-drogue style autonomous aerial refueling tasks by combining sensitivity adjustments of a 3D Flash LIDAR camera with computer vision based image-processing techniques. The method overcomes the inherit ambiguity issues when reconstructing 3D information from traditional 2D images by taking advantage of ready to use 3D point cloud data from the camera, followed by well-established computer vision techniques. These techniques include curve fitting algorithms and outlier removal with the random sample consensus (RANSAC) algorithm to reliably estimate the drogue center in 3D space, as well as to establish the relative position between the probe and the drogue. To demonstrate the feasibility of the proposed method on a real system, a ground navigation robot was designed and fabricated. Results presented in the paper show that using images acquired from a 3D Flash LIDAR camera as real time visual feedback, the ground robot is able to track a moving simulated drogue and continuously narrow the gap between the robot and the target autonomously. PMID:25970254

An initial ULF wave index derived from 2 years of Swarm observations

NASA Astrophysics Data System (ADS)

Papadimitriou, Constantinos; Balasis, Georgios; Daglis, Ioannis A.; Giannakis, Omiros

2018-03-01

The ongoing Swarm satellite mission provides an opportunity for better knowledge of the near-Earth electromagnetic environment. Herein, we use a new methodological approach for the detection and classification of ultra low-frequency (ULF) wave events observed by Swarm based on an existing time-frequency analysis (TFA) tool and utilizing a state-of-the-art high-resolution magnetic field model and Swarm Level 2 products (i.e., field-aligned currents - FACs - and the Ionospheric Bubble Index - IBI). We present maps of the dependence of ULF wave power with magnetic latitude and magnetic local time (MLT) as well as geographic latitude and longitude from the three satellites at their different locations in low-Earth orbit (LEO) for a period spanning 2 years after the constellation's final configuration. We show that the inclusion of the Swarm single-spacecraft FAC product in our analysis eliminates all the wave activity at high altitudes, which is physically unrealistic. Moreover, we derive a Swarm orbit-by-orbit Pc3 wave (20-100 MHz) index for the topside ionosphere and compare its values with the corresponding variations of solar wind variables and geomagnetic activity indices. This is the first attempt, to our knowledge, to derive a ULF wave index from LEO satellite data. The technique can be potentially used to define a new Level 2 product from the mission, the Swarm ULF wave index, which would be suitable for space weather applications.

Firefly as a novel swarm intelligence variable selection method in spectroscopy.

PubMed

Goodarzi, Mohammad; dos Santos Coelho, Leandro

2014-12-10

A critical step in multivariate calibration is wavelength selection, which is used to build models with better prediction performance when applied to spectral data. Up to now, many feature selection techniques have been developed. Among all different types of feature selection techniques, those based on swarm intelligence optimization methodologies are more interesting since they are usually simulated based on animal and insect life behavior to, e.g., find the shortest path between a food source and their nests. This decision is made by a crowd, leading to a more robust model with less falling in local minima during the optimization cycle. This paper represents a novel feature selection approach to the selection of spectroscopic data, leading to more robust calibration models. The performance of the firefly algorithm, a swarm intelligence paradigm, was evaluated and compared with genetic algorithm and particle swarm optimization. All three techniques were coupled with partial least squares (PLS) and applied to three spectroscopic data sets. They demonstrate improved prediction results in comparison to when only a PLS model was built using all wavelengths. Results show that firefly algorithm as a novel swarm paradigm leads to a lower number of selected wavelengths while the prediction performance of built PLS stays the same. Copyright © 2014. Published by Elsevier B.V.

Competitive Swarm Optimizer Based Gateway Deployment Algorithm in Cyber-Physical Systems.

PubMed

Huang, Shuqiang; Tao, Ming

2017-01-22

Wireless sensor network topology optimization is a highly important issue, and topology control through node selection can improve the efficiency of data forwarding, while saving energy and prolonging lifetime of the network. To address the problem of connecting a wireless sensor network to the Internet in cyber-physical systems, here we propose a geometric gateway deployment based on a competitive swarm optimizer algorithm. The particle swarm optimization (PSO) algorithm has a continuous search feature in the solution space, which makes it suitable for finding the geometric center of gateway deployment; however, its search mechanism is limited to the individual optimum (pbest) and the population optimum (gbest); thus, it easily falls into local optima. In order to improve the particle search mechanism and enhance the search efficiency of the algorithm, we introduce a new competitive swarm optimizer (CSO) algorithm. The CSO search algorithm is based on an inter-particle competition mechanism and can effectively avoid trapping of the population falling into a local optimum. With the improvement of an adaptive opposition-based search and its ability to dynamically parameter adjustments, this algorithm can maintain the diversity of the entire swarm to solve geometric K -center gateway deployment problems. The simulation results show that this CSO algorithm has a good global explorative ability as well as convergence speed and can improve the network quality of service (QoS) level of cyber-physical systems by obtaining a minimum network coverage radius. We also find that the CSO algorithm is more stable, robust and effective in solving the problem of geometric gateway deployment as compared to the PSO or Kmedoids algorithms.

Driving Processes of Earthquake Swarms: Evidence from High Resolution Seismicity

NASA Astrophysics Data System (ADS)

Ellsworth, W. L.; Shelly, D. R.; Hill, D. P.; Hardebeck, J.; Hsieh, P. A.

2017-12-01

Earthquake swarms are transient increases in seismicity deviating from a typical mainshock-aftershock pattern. Swarms are most prevalent in volcanic and hydrothermal areas, yet also occur in other environments, such as extensional fault stepovers. Swarms provide a valuable opportunity to investigate source zone physics, including the causes of their swarm-like behavior. To gain insight into this behavior, we have used waveform-based methods to greatly enhance standard seismic catalogs. Depending on the application, we detect and precisely relocate 2-10x as many events as included in the initial catalog. Recently, we have added characterization of focal mechanisms (applied to a 2014 swarm in Long Valley Caldera, California), addressing a common shortcoming in microseismicity analyses (Shelly et al., JGR, 2016). In analysis of multiple swarms (both within and outside volcanic areas), several features stand out, including: (1) dramatic expansion of the active source region with time, (2) tendency for events to occur on the immediate fringe of prior activity, (3) overall upward migration, and (4) complex faulting structure. Some swarms also show an apparent mismatch between seismicity orientations (as defined by patterns in hypocentral locations) and slip orientations (as inferred from focal mechanisms). These features are largely distinct from those observed in mainshock-aftershock sequences. In combination, these swarm behaviors point to an important role for fluid pressure diffusion. Swarms may in fact be generated by a cascade of fluid pressure diffusion and stress transfer: in cases where faults are critically stressed, an increase in fluid pressure will trigger faulting. Faulting will in turn dramatically increase permeability in the faulted area, allowing rapid equilibration of fluid pressure to the fringe of the rupture zone. This process may perpetuate until fluid pressure perturbations drop and/or stresses become further from failure, such that any perturbation (fluid + stress transfer) is insufficient to generate further faulting. Numerical modeling supports this hypothesis - for example, the main features of the 2014 Long Valley swarm can be reproduced by a relatively simple model incorporating both stress transfer and rupture-aided fluid pressure diffusion (Hsieh et al., AGU FM, 2016).

Time-variable gravity fields and ocean mass change from 37 months of kinematic Swarm orbits

NASA Astrophysics Data System (ADS)

Lück, Christina; Kusche, Jürgen; Rietbroek, Roelof; Löcher, Anno

2018-03-01

Measuring the spatiotemporal variation of ocean mass allows for partitioning of volumetric sea level change, sampled by radar altimeters, into mass-driven and steric parts. The latter is related to ocean heat change and the current Earth's energy imbalance. Since 2002, the Gravity Recovery and Climate Experiment (GRACE) mission has provided monthly snapshots of the Earth's time-variable gravity field, from which one can derive ocean mass variability. However, GRACE has reached the end of its lifetime with data degradation and several gaps occurred during the last years, and there will be a prolonged gap until the launch of the follow-on mission GRACE-FO. Therefore, efforts focus on generating a long and consistent ocean mass time series by analyzing kinematic orbits from other low-flying satellites, i.e. extending the GRACE time series. Here we utilize data from the European Space Agency's (ESA) Swarm Earth Explorer satellites to derive and investigate ocean mass variations. For this aim, we use the integral equation approach with short arcs (Mayer-Gürr, 2006) to compute more than 500 time-variable gravity fields with different parameterizations from kinematic orbits. We investigate the potential to bridge the gap between the GRACE and the GRACE-FO mission and to substitute missing monthly solutions with Swarm results of significantly lower resolution. Our monthly Swarm solutions have a root mean square error (RMSE) of 4.0 mm with respect to GRACE, whereas directly estimating constant, trend, annual, and semiannual (CTAS) signal terms leads to an RMSE of only 1.7 mm. Concerning monthly gaps, our CTAS Swarm solution appears better than interpolating existing GRACE data in 13.5 % of all cases, when artificially removing one solution. In the case of an 18-month artificial gap, 80.0 % of all CTAS Swarm solutions were found closer to the observed GRACE data compared to interpolated GRACE data. Furthermore, we show that precise modeling of non-gravitational forces acting on the Swarm satellites is the key for reaching these accuracies. Our results have implications for sea level budget studies, but they may also guide further research in gravity field analysis schemes, including satellites not dedicated to gravity field studies.

Mamdani Fuzzy System for Indoor Autonomous Mobile Robot

NASA Astrophysics Data System (ADS)

Khan, M. K. A. Ahamed; Rashid, Razif; Elamvazuthi, I.

2011-06-01

Several control algorithms for autonomous mobile robot navigation have been proposed in the literature. Recently, the employment of non-analytical methods of computing such as fuzzy logic, evolutionary computation, and neural networks has demonstrated the utility and potential of these paradigms for intelligent control of mobile robot navigation. In this paper, Mamdani fuzzy system for an autonomous mobile robot is developed. The paper begins with the discussion on the conventional controller and then followed by the description of fuzzy logic controller in detail.

Computational modeling of the cell-autonomous mammalian circadian oscillator.

PubMed

Podkolodnaya, Olga A; Tverdokhleb, Natalya N; Podkolodnyy, Nikolay L

2017-02-24

This review summarizes various mathematical models of cell-autonomous mammalian circadian clock. We present the basics necessary for understanding of the cell-autonomous mammalian circadian oscillator, modern experimental data essential for its reconstruction and some special problems related to the validation of mathematical circadian oscillator models. This work compares existing mathematical models of circadian oscillator and the results of the computational studies of the oscillating systems. Finally, we discuss applications of the mathematical models of mammalian circadian oscillator for solving specific problems in circadian rhythm biology.

A Probabilistic and Highly Efficient Topology Control Algorithm for Underwater Cooperating AUV Networks

PubMed Central

Li, Ning; Cürüklü, Baran; Bastos, Joaquim; Sucasas, Victor; Fernandez, Jose Antonio Sanchez; Rodriguez, Jonathan

2017-01-01

The aim of the Smart and Networking Underwater Robots in Cooperation Meshes (SWARMs) project is to make autonomous underwater vehicles (AUVs), remote operated vehicles (ROVs) and unmanned surface vehicles (USVs) more accessible and useful. To achieve cooperation and communication between different AUVs, these must be able to exchange messages, so an efficient and reliable communication network is necessary for SWARMs. In order to provide an efficient and reliable communication network for mission execution, one of the important and necessary issues is the topology control of the network of AUVs that are cooperating underwater. However, due to the specific properties of an underwater AUV cooperation network, such as the high mobility of AUVs, large transmission delays, low bandwidth, etc., the traditional topology control algorithms primarily designed for terrestrial wireless sensor networks cannot be used directly in the underwater environment. Moreover, these algorithms, in which the nodes adjust their transmission power once the current transmission power does not equal an optimal one, are costly in an underwater cooperating AUV network. Considering these facts, in this paper, we propose a Probabilistic Topology Control (PTC) algorithm for an underwater cooperating AUV network. In PTC, when the transmission power of an AUV is not equal to the optimal transmission power, then whether the transmission power needs to be adjusted or not will be determined based on the AUV’s parameters. Each AUV determines their own transmission power adjustment probability based on the parameter deviations. The larger the deviation, the higher the transmission power adjustment probability is, and vice versa. For evaluating the performance of PTC, we combine the PTC algorithm with the Fuzzy logic Topology Control (FTC) algorithm and compare the performance of these two algorithms. The simulation results have demonstrated that the PTC is efficient at reducing the transmission power adjustment ratio while improving the network performance. PMID:28471387

A Probabilistic and Highly Efficient Topology Control Algorithm for Underwater Cooperating AUV Networks.

PubMed

Li, Ning; Cürüklü, Baran; Bastos, Joaquim; Sucasas, Victor; Fernandez, Jose Antonio Sanchez; Rodriguez, Jonathan

2017-05-04

The aim of the Smart and Networking Underwater Robots in Cooperation Meshes (SWARMs) project is to make autonomous underwater vehicles (AUVs), remote operated vehicles (ROVs) and unmanned surface vehicles (USVs) more accessible and useful. To achieve cooperation and communication between different AUVs, these must be able to exchange messages, so an efficient and reliable communication network is necessary for SWARMs. In order to provide an efficient and reliable communication network for mission execution, one of the important and necessary issues is the topology control of the network of AUVs that are cooperating underwater. However, due to the specific properties of an underwater AUV cooperation network, such as the high mobility of AUVs, large transmission delays, low bandwidth, etc., the traditional topology control algorithms primarily designed for terrestrial wireless sensor networks cannot be used directly in the underwater environment. Moreover, these algorithms, in which the nodes adjust their transmission power once the current transmission power does not equal an optimal one, are costly in an underwater cooperating AUV network. Considering these facts, in this paper, we propose a Probabilistic Topology Control (PTC) algorithm for an underwater cooperating AUV network. In PTC, when the transmission power of an AUV is not equal to the optimal transmission power, then whether the transmission power needs to be adjusted or not will be determined based on the AUV's parameters. Each AUV determines their own transmission power adjustment probability based on the parameter deviations. The larger the deviation, the higher the transmission power adjustment probability is, and vice versa. For evaluating the performance of PTC, we combine the PTC algorithm with the Fuzzy logic Topology Control (FTC) algorithm and compare the performance of these two algorithms. The simulation results have demonstrated that the PTC is efficient at reducing the transmission power adjustment ratio while improving the network performance.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hasan, Iftekhar; Husain, Tausif; Sozer, Yilmaz

This paper proposes an analytical machine design tool using magnetic equivalent circuit (MEC)-based particle swarm optimization (PSO) for a double-sided, flux-concentrating transverse flux machine (TFM). The magnetic equivalent circuit method is applied to analytically establish the relationship between the design objective and the input variables of prospective TFM designs. This is computationally less intensive and more time efficient than finite element solvers. A PSO algorithm is then used to design a machine with the highest torque density within the specified power range along with some geometric design constraints. The stator pole length, magnet length, and rotor thickness are the variablesmore » that define the optimization search space. Finite element analysis (FEA) was carried out to verify the performance of the MEC-PSO optimized machine. The proposed analytical design tool helps save computation time by at least 50% when compared to commercial FEA-based optimization programs, with results found to be in agreement with less than 5% error.« less

Semi-autonomous parking for enhanced safety and efficiency.

DOT National Transportation Integrated Search

2017-06-01

This project focuses on the use of tools from a combination of computer vision and localization based navigation schemes to aid the process of efficient and safe parking of vehicles in high density parking spaces. The principles of collision avoidanc...

A fluid-driven earthquake swarm on the margin of the Yellowstone caldera

USGS Publications Warehouse

Shelly, David R.; Hill, David P.; Massin, Frederick; Farrell, Jamie; Smith, Robert B.; Taira, Taka'aki

2013-01-01

Over the past several decades, the Yellowstone caldera has experienced frequent earthquake swarms and repeated cycles of uplift and subsidence, reflecting dynamic volcanic and tectonic processes. Here, we examine the detailed spatial-temporal evolution of the 2010 Madison Plateau swarm, which occurred near the northwest boundary of the Yellowstone caldera. To fully explore the evolution of the swarm, we integrated procedures for seismic waveform-based earthquake detection with precise double-difference relative relocation. Using cross-correlation of continuous seismic data and waveform templates constructed from cataloged events, we detected and precisely located 8710 earthquakes during the three-week swarm, nearly four times the number of events included in the standard catalog. This high-resolution analysis reveals distinct migration of earthquake activity over the course of the swarm. The swarm initiated abruptly on January 17, 2010 at about 10 km depth and expanded dramatically outward (both shallower and deeper) over time, primarily along a NNW-striking, ~55º ENE-dipping structure. To explain these characteristics, we hypothesize that the swarm was triggered by the rupture of a zone of confined high-pressure aqueous fluids into a pre-existing crustal fault system, prompting release of accumulated stress. The high-pressure fluid injection may have been accommodated by hybrid shear and dilatational failure, as is commonly observed in exhumed hydrothermally affected fault zones. This process has likely occurred repeatedly in Yellowstone as aqueous fluids exsolved from magma migrate into the brittle crust, and it may be a key element in the observed cycles of caldera uplift and subsidence.

The VIPER project (Visualization Integration Platform for Exploration Research): a biologically inspired autonomous reconfigurable robotic platform for diverse unstructured environments

NASA Astrophysics Data System (ADS)

Schubert, Oliver J.; Tolle, Charles R.

2004-09-01

Over the last decade the world has seen numerous autonomous vehicle programs. Wheels and track designs are the basis for many of these vehicles. This is primarily due to four main reasons: a vast preexisting knowledge base for these designs, energy efficiency of power sources, scalability of actuators, and the lack of control systems technologies for handling alternate highly complex distributed systems. Though large efforts seek to improve the mobility of these vehicles, many limitations still exist for these systems within unstructured environments, e.g. limited mobility within industrial and nuclear accident sites where existing plant configurations have been extensively changed. These unstructured operational environments include missions for exploration, reconnaissance, and emergency recovery of objects within reconfigured or collapsed structures, e.g. bombed buildings. More importantly, these environments present a clear and present danger for direct human interactions during the initial phases of recovery operations. Clearly, the current classes of autonomous vehicles are incapable of performing in these environments. Thus the next generation of designs must include highly reconfigurable and flexible autonomous robotic platforms. This new breed of autonomous vehicles will be both highly flexible and environmentally adaptable. Presented in this paper is one of the most successful designs from nature, the snake-eel-worm (SEW). This design implements shape memory alloy (SMA) actuators which allow for scaling of the robotic SEW designs from sub-micron scale to heavy industrial implementations without major conceptual redesigns as required in traditional hydraulic, pneumatic, or motor driven systems. Autonomous vehicles based on the SEW design posses the ability to easily move between air based environments and fluid based environments with limited or no reconfiguration. Under a SEW designed vehicle, one not only achieves vastly improved maneuverability within a highly unstructured environment, but also gains robotic manipulation abilities, normally relegated as secondary add-ons within existing vehicles, all within one small condensed package. The prototype design presented includes a Beowulf style computing system for advanced guidance calculations and visualization computations. All of the design and implementation pertaining to the SEW robot discussed in this paper is the product of a student team under the summer fellowship program at the DOEs INEEL.

Wiener-Hammerstein system identification - an evolutionary approach

NASA Astrophysics Data System (ADS)

Naitali, Abdessamad; Giri, Fouad

2016-01-01

The problem of identifying parametric Wiener-Hammerstein (WH) systems is addressed within the evolutionary optimisation context. Specifically, a hybrid culture identification method is developed that involves model structure adaptation using genetic recombination and model parameter learning using particle swarm optimisation. The method enjoys three interesting features: (1) the risk of premature convergence of model parameter estimates to local optima is significantly reduced, due to the constantly maintained diversity of model candidates; (2) no prior knowledge is needed except for upper bounds on the system structure indices; (3) the method is fully autonomous as no interaction is needed with the user during the optimum search process. The performances of the proposed method will be illustrated and compared to alternative methods using a well-established WH benchmark.

An interactive machine-learning approach for defect detection in computed tomogaraphy (CT) images of hardwood logs

Treesearch

Erol Sarigul; A. Lynn Abbott; Daniel L. Schmoldt; Philip A. Araman

2005-01-01

This paper describes recent progress in the analysis of computed tomography (CT) images of hardwood logs. The long-term goal of the work is to develop a system that is capable of autonomous (or semiautonomous) detection of internal defects, so that log breakdown decisions can be optimized based on defect locations. The problem is difficult because wood exhibits large...

Identifying Interacting Genetic Variations by Fish-Swarm Logic Regression

PubMed Central

Yang, Aiyuan; Yan, Chunxia; Zhu, Feng; Zhao, Zhongmeng; Cao, Zhi

2013-01-01

Understanding associations between genotypes and complex traits is a fundamental problem in human genetics. A major open problem in mapping phenotypes is that of identifying a set of interacting genetic variants, which might contribute to complex traits. Logic regression (LR) is a powerful multivariant association tool. Several LR-based approaches have been successfully applied to different datasets. However, these approaches are not adequate with regard to accuracy and efficiency. In this paper, we propose a new LR-based approach, called fish-swarm logic regression (FSLR), which improves the logic regression process by incorporating swarm optimization. In our approach, a school of fish agents are conducted in parallel. Each fish agent holds a regression model, while the school searches for better models through various preset behaviors. A swarm algorithm improves the accuracy and the efficiency by speeding up the convergence and preventing it from dropping into local optimums. We apply our approach on a real screening dataset and a series of simulation scenarios. Compared to three existing LR-based approaches, our approach outperforms them by having lower type I and type II error rates, being able to identify more preset causal sites, and performing at faster speeds. PMID:23984382

A Multipopulation PSO Based Memetic Algorithm for Permutation Flow Shop Scheduling

PubMed Central

Liu, Ruochen; Ma, Chenlin; Ma, Wenping; Li, Yangyang

2013-01-01

The permutation flow shop scheduling problem (PFSSP) is part of production scheduling, which belongs to the hardest combinatorial optimization problem. In this paper, a multipopulation particle swarm optimization (PSO) based memetic algorithm (MPSOMA) is proposed in this paper. In the proposed algorithm, the whole particle swarm population is divided into three subpopulations in which each particle evolves itself by the standard PSO and then updates each subpopulation by using different local search schemes such as variable neighborhood search (VNS) and individual improvement scheme (IIS). Then, the best particle of each subpopulation is selected to construct a probabilistic model by using estimation of distribution algorithm (EDA) and three particles are sampled from the probabilistic model to update the worst individual in each subpopulation. The best particle in the entire particle swarm is used to update the global optimal solution. The proposed MPSOMA is compared with two recently proposed algorithms, namely, PSO based memetic algorithm (PSOMA) and hybrid particle swarm optimization with estimation of distribution algorithm (PSOEDA), on 29 well-known PFFSPs taken from OR-library, and the experimental results show that it is an effective approach for the PFFSP. PMID:24453841

Hybrid Swarm Intelligence Optimization Approach for Optimal Data Storage Position Identification in Wireless Sensor Networks

PubMed Central

Mohanasundaram, Ranganathan; Periasamy, Pappampalayam Sanmugam

2015-01-01

The current high profile debate with regard to data storage and its growth have become strategic task in the world of networking. It mainly depends on the sensor nodes called producers, base stations, and also the consumers (users and sensor nodes) to retrieve and use the data. The main concern dealt here is to find an optimal data storage position in wireless sensor networks. The works that have been carried out earlier did not utilize swarm intelligence based optimization approaches to find the optimal data storage positions. To achieve this goal, an efficient swam intelligence approach is used to choose suitable positions for a storage node. Thus, hybrid particle swarm optimization algorithm has been used to find the suitable positions for storage nodes while the total energy cost of data transmission is minimized. Clustering-based distributed data storage is utilized to solve clustering problem using fuzzy-C-means algorithm. This research work also considers the data rates and locations of multiple producers and consumers to find optimal data storage positions. The algorithm is implemented in a network simulator and the experimental results show that the proposed clustering and swarm intelligence based ODS strategy is more effective than the earlier approaches. PMID:25734182

Performance Review of Harmony Search, Differential Evolution and Particle Swarm Optimization

NASA Astrophysics Data System (ADS)

Mohan Pandey, Hari

2017-08-01

Metaheuristic algorithms are effective in the design of an intelligent system. These algorithms are widely applied to solve complex optimization problems, including image processing, big data analytics, language processing, pattern recognition and others. This paper presents a performance comparison of three meta-heuristic algorithms, namely Harmony Search, Differential Evolution, and Particle Swarm Optimization. These algorithms are originated altogether from different fields of meta-heuristics yet share a common objective. The standard benchmark functions are used for the simulation. Statistical tests are conducted to derive a conclusion on the performance. The key motivation to conduct this research is to categorize the computational capabilities, which might be useful to the researchers.

Computing Nash equilibria through computational intelligence methods

NASA Astrophysics Data System (ADS)

Pavlidis, N. G.; Parsopoulos, K. E.; Vrahatis, M. N.

2005-03-01

Nash equilibrium constitutes a central solution concept in game theory. The task of detecting the Nash equilibria of a finite strategic game remains a challenging problem up-to-date. This paper investigates the effectiveness of three computational intelligence techniques, namely, covariance matrix adaptation evolution strategies, particle swarm optimization, as well as, differential evolution, to compute Nash equilibria of finite strategic games, as global minima of a real-valued, nonnegative function. An issue of particular interest is to detect more than one Nash equilibria of a game. The performance of the considered computational intelligence methods on this problem is investigated using multistart and deflection.

A Modified Artificial Bee Colony Algorithm for p-Center Problems

PubMed Central

Yurtkuran, Alkın

2014-01-01

The objective of the p-center problem is to locate p-centers on a network such that the maximum of the distances from each node to its nearest center is minimized. The artificial bee colony algorithm is a swarm-based meta-heuristic algorithm that mimics the foraging behavior of honey bee colonies. This study proposes a modified ABC algorithm that benefits from a variety of search strategies to balance exploration and exploitation. Moreover, random key-based coding schemes are used to solve the p-center problem effectively. The proposed algorithm is compared to state-of-the-art techniques using different benchmark problems, and computational results reveal that the proposed approach is very efficient. PMID:24616648

On edge-aware path-based color spatial sampling for Retinex: from Termite Retinex to Light Energy-driven Termite Retinex

NASA Astrophysics Data System (ADS)

Simone, Gabriele; Cordone, Roberto; Serapioni, Raul Paolo; Lecca, Michela

2017-05-01

Retinex theory estimates the human color sensation at any observed point by correcting its color based on the spatial arrangement of the colors in proximate regions. We revise two recent path-based, edge-aware Retinex implementations: Termite Retinex (TR) and Energy-driven Termite Retinex (ETR). As the original Retinex implementation, TR and ETR scan the neighborhood of any image pixel by paths and rescale their chromatic intensities by intensity levels computed by reworking the colors of the pixels on the paths. Our interest in TR and ETR is due to their unique, content-based scanning scheme, which uses the image edges to define the paths and exploits a swarm intelligence model for guiding the spatial exploration of the image. The exploration scheme of ETR has been showed to be particularly effective: its paths are local minima of an energy functional, designed to favor the sampling of image pixels highly relevant to color sensation. Nevertheless, since its computational complexity makes ETR poorly practicable, here we present a light version of it, named Light Energy-driven TR, and obtained from ETR by implementing a modified, optimized minimization procedure and by exploiting parallel computing.

Analytical Model-Based Design Optimization of a Transverse Flux Machine

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hasan, Iftekhar; Husain, Tausif; Sozer, Yilmaz

This paper proposes an analytical machine design tool using magnetic equivalent circuit (MEC)-based particle swarm optimization (PSO) for a double-sided, flux-concentrating transverse flux machine (TFM). The magnetic equivalent circuit method is applied to analytically establish the relationship between the design objective and the input variables of prospective TFM designs. This is computationally less intensive and more time efficient than finite element solvers. A PSO algorithm is then used to design a machine with the highest torque density within the specified power range along with some geometric design constraints. The stator pole length, magnet length, and rotor thickness are the variablesmore » that define the optimization search space. Finite element analysis (FEA) was carried out to verify the performance of the MEC-PSO optimized machine. The proposed analytical design tool helps save computation time by at least 50% when compared to commercial FEA-based optimization programs, with results found to be in agreement with less than 5% error.« less

Precise Orbit Solution for Swarm Using Space-Borne GPS Data and Optimized Pseudo-Stochastic Pulses.

PubMed

Zhang, Bingbing; Wang, Zhengtao; Zhou, Lv; Feng, Jiandi; Qiu, Yaodong; Li, Fupeng

2017-03-20

Swarm is a European Space Agency (ESA) project that was launched on 22 November 2013, which consists of three Swarm satellites. Swarm precise orbits are essential to the success of the above project. This study investigates how well Swarm zero-differenced (ZD) reduced-dynamic orbit solutions can be determined using space-borne GPS data and optimized pseudo-stochastic pulses under high ionospheric activity. We choose Swarm space-borne GPS data from 1-25 October 2014, and Swarm reduced-dynamic orbits are obtained. Orbit quality is assessed by GPS phase observation residuals and compared with Precise Science Orbits (PSOs) released by ESA. Results show that pseudo-stochastic pulses with a time interval of 6 min and a priori standard deviation (STD) of 10 -2 mm/s in radial (R), along-track (T) and cross-track (N) directions are optimized to Swarm ZD reduced-dynamic precise orbit determination (POD). During high ionospheric activity, the mean Root Mean Square (RMS) of Swarm GPS phase residuals is at 9-11 mm, Swarm orbit solutions are also compared with Swarm PSOs released by ESA and the accuracy of Swarm orbits can reach 2-4 cm in R, T and N directions. Independent Satellite Laser Ranging (SLR) validation indicates that Swarm reduced-dynamic orbits have an accuracy of 2-4 cm. Swarm-B orbit quality is better than those of Swarm-A and Swarm-C. The Swarm orbits can be applied to the geomagnetic, geoelectric and gravity field recovery.

Gravity inversion of a fault by Particle swarm optimization (PSO).

PubMed

Toushmalani, Reza

2013-01-01

Particle swarm optimization is a heuristic global optimization method and also an optimization algorithm, which is based on swarm intelligence. It comes from the research on the bird and fish flock movement behavior. In this paper we introduce and use this method in gravity inverse problem. We discuss the solution for the inverse problem of determining the shape of a fault whose gravity anomaly is known. Application of the proposed algorithm to this problem has proven its capability to deal with difficult optimization problems. The technique proved to work efficiently when tested to a number of models.

Application of particle swarm optimization in path planning of mobile robot

NASA Astrophysics Data System (ADS)

Wang, Yong; Cai, Feng; Wang, Ying

2017-08-01

In order to realize the optimal path planning of mobile robot in unknown environment, a particle swarm optimization algorithm based on path length as fitness function is proposed. The location of the global optimal particle is determined by the minimum fitness value, and the robot moves along the points of the optimal particles to the target position. The process of moving to the target point is done with MATLAB R2014a. Compared with the standard particle swarm optimization algorithm, the simulation results show that this method can effectively avoid all obstacles and get the optimal path.

Monitoring the West Bohemian earthquake swarm in 2008/2009 by a temporary small-aperture seismic array

NASA Astrophysics Data System (ADS)

Hiemer, Stefan; Roessler, Dirk; Scherbaum, Frank

2012-04-01

The most recent intense earthquake swarm in West Bohemia lasted from 6 October 2008 to January 2009. Starting 12 days after the onset, the University of Potsdam monitored the swarm by a temporary small-aperture seismic array at 10 km epicentral distance. The purpose of the installation was a complete monitoring of the swarm including micro-earthquakes ( M L < 0). We identify earthquakes using a conventional short-term average/long-term average trigger combined with sliding-window frequency-wavenumber and polarisation analyses. The resulting earthquake catalogue consists of 14,530 earthquakes between 19 October 2008 and 18 March 2009 with magnitudes in the range of - 1.2 ≤ M L ≤ 2.7. The small-aperture seismic array substantially lowers the detection threshold to about M c = - 0.4, when compared to the regional networks operating in West Bohemia ( M c > 0.0). In the course of this work, the main temporal features (frequency-magnitude distribution, propagation of back azimuth and horizontal slowness, occurrence rate of aftershock sequences and interevent-time distribution) of the recent 2008/2009 earthquake swarm are presented and discussed. Temporal changes of the coefficient of variation (based on interevent times) suggest that the swarm earthquake activity of the 2008/2009 swarm terminates by 12 January 2009. During the main phase in our studied swarm period after 19 October, the b value of the Gutenberg-Richter relation decreases from 1.2 to 0.8. This trend is also reflected in the power-law behavior of the seismic moment release. The corresponding total seismic moment release of 1.02×1017 Nm is equivalent to M L,max = 5.4.

Color Feature-Based Object Tracking through Particle Swarm Optimization with Improved Inertia Weight

PubMed Central

Guo, Siqiu; Zhang, Tao; Song, Yulong

2018-01-01

This paper presents a particle swarm tracking algorithm with improved inertia weight based on color features. The weighted color histogram is used as the target feature to reduce the contribution of target edge pixels in the target feature, which makes the algorithm insensitive to the target non-rigid deformation, scale variation, and rotation. Meanwhile, the influence of partial obstruction on the description of target features is reduced. The particle swarm optimization algorithm can complete the multi-peak search, which can cope well with the object occlusion tracking problem. This means that the target is located precisely where the similarity function appears multi-peak. When the particle swarm optimization algorithm is applied to the object tracking, the inertia weight adjustment mechanism has some limitations. This paper presents an improved method. The concept of particle maturity is introduced to improve the inertia weight adjustment mechanism, which could adjust the inertia weight in time according to the different states of each particle in each generation. Experimental results show that our algorithm achieves state-of-the-art performance in a wide range of scenarios. PMID:29690610

Color Feature-Based Object Tracking through Particle Swarm Optimization with Improved Inertia Weight.

PubMed

Guo, Siqiu; Zhang, Tao; Song, Yulong; Qian, Feng

2018-04-23

This paper presents a particle swarm tracking algorithm with improved inertia weight based on color features. The weighted color histogram is used as the target feature to reduce the contribution of target edge pixels in the target feature, which makes the algorithm insensitive to the target non-rigid deformation, scale variation, and rotation. Meanwhile, the influence of partial obstruction on the description of target features is reduced. The particle swarm optimization algorithm can complete the multi-peak search, which can cope well with the object occlusion tracking problem. This means that the target is located precisely where the similarity function appears multi-peak. When the particle swarm optimization algorithm is applied to the object tracking, the inertia weight adjustment mechanism has some limitations. This paper presents an improved method. The concept of particle maturity is introduced to improve the inertia weight adjustment mechanism, which could adjust the inertia weight in time according to the different states of each particle in each generation. Experimental results show that our algorithm achieves state-of-the-art performance in a wide range of scenarios.

Competitive Swarm Optimizer Based Gateway Deployment Algorithm in Cyber-Physical Systems

PubMed Central

Huang, Shuqiang; Tao, Ming

2017-01-01

Wireless sensor network topology optimization is a highly important issue, and topology control through node selection can improve the efficiency of data forwarding, while saving energy and prolonging lifetime of the network. To address the problem of connecting a wireless sensor network to the Internet in cyber-physical systems, here we propose a geometric gateway deployment based on a competitive swarm optimizer algorithm. The particle swarm optimization (PSO) algorithm has a continuous search feature in the solution space, which makes it suitable for finding the geometric center of gateway deployment; however, its search mechanism is limited to the individual optimum (pbest) and the population optimum (gbest); thus, it easily falls into local optima. In order to improve the particle search mechanism and enhance the search efficiency of the algorithm, we introduce a new competitive swarm optimizer (CSO) algorithm. The CSO search algorithm is based on an inter-particle competition mechanism and can effectively avoid trapping of the population falling into a local optimum. With the improvement of an adaptive opposition-based search and its ability to dynamically parameter adjustments, this algorithm can maintain the diversity of the entire swarm to solve geometric K-center gateway deployment problems. The simulation results show that this CSO algorithm has a good global explorative ability as well as convergence speed and can improve the network quality of service (QoS) level of cyber-physical systems by obtaining a minimum network coverage radius. We also find that the CSO algorithm is more stable, robust and effective in solving the problem of geometric gateway deployment as compared to the PSO or Kmedoids algorithms. PMID:28117735

Adjustable Autonomy Testbed

NASA Technical Reports Server (NTRS)

Malin, Jane T.; Schrenkenghost, Debra K.

2001-01-01

The Adjustable Autonomy Testbed (AAT) is a simulation-based testbed located in the Intelligent Systems Laboratory in the Automation, Robotics and Simulation Division at NASA Johnson Space Center. The purpose of the testbed is to support evaluation and validation of prototypes of adjustable autonomous agent software for control and fault management for complex systems. The AA T project has developed prototype adjustable autonomous agent software and human interfaces for cooperative fault management. This software builds on current autonomous agent technology by altering the architecture, components and interfaces for effective teamwork between autonomous systems and human experts. Autonomous agents include a planner, flexible executive, low level control and deductive model-based fault isolation. Adjustable autonomy is intended to increase the flexibility and effectiveness of fault management with an autonomous system. The test domain for this work is control of advanced life support systems for habitats for planetary exploration. The CONFIG hybrid discrete event simulation environment provides flexible and dynamically reconfigurable models of the behavior of components and fluids in the life support systems. Both discrete event and continuous (discrete time) simulation are supported, and flows and pressures are computed globally. This provides fast dynamic simulations of interacting hardware systems in closed loops that can be reconfigured during operations scenarios, producing complex cascading effects of operations and failures. Current object-oriented model libraries support modeling of fluid systems, and models have been developed of physico-chemical and biological subsystems for processing advanced life support gases. In FY01, water recovery system models will be developed.

On the spatial dynamics and oscillatory behavior of a predator-prey model based on cellular automata and local particle swarm optimization.

PubMed

Molina, Mario Martínez; Moreno-Armendáriz, Marco A; Carlos Seck Tuoh Mora, Juan

2013-11-07

A two-dimensional lattice model based on Cellular Automata theory and swarm intelligence is used to study the spatial and population dynamics of a theoretical ecosystem. It is found that the social interactions among predators provoke the formation of clusters, and that by increasing the mobility of predators the model enters into an oscillatory behavior. © 2013 Elsevier Ltd. All rights reserved.

An Autonomous Learning System of Bengali Characters Using Web-Based Intelligent Handwriting Recognition

ERIC Educational Resources Information Center

Khatun, Nazma; Miwa, Jouji

2016-01-01

This research project was aimed to develop an intelligent Bengali handwriting education system to improve the literacy level in Bangladesh. Due to the socio-economical limitation, all of the population does not have the chance to go to school. Here, we developed a prototype of web-based (iPhone/smartphone or computer browser) intelligent…

New tools for characterizing swarming systems: A comparison of minimal models

NASA Astrophysics Data System (ADS)

Huepe, Cristián; Aldana, Maximino

2008-05-01

We compare three simple models that reproduce qualitatively the emergent swarming behavior of bird flocks, fish schools, and other groups of self-propelled agents by using a new set of diagnosis tools related to the agents’ spatial distribution. Two of these correspond in fact to different implementations of the same model, which had been previously confused in the literature. All models appear to undergo a very similar order-to-disorder phase transition as the noise level is increased if we only compare the standard order parameter, which measures the degree of agent alignment. When considering our novel quantities, however, their properties are clearly distinguished, unveiling previously unreported qualitative characteristics that help determine which model best captures the main features of realistic swarms. Additionally, we analyze the agent clustering in space, finding that the distribution of cluster sizes is typically exponential at high noise, and approaches a power-law as the noise level is reduced. This trend is sometimes reversed at noise levels close to the phase transition, suggesting a non-trivial critical behavior that could be verified experimentally. Finally, we study a bi-stable regime that develops under certain conditions in large systems. By computing the probability distributions of our new quantities, we distinguish the properties of each of the coexisting metastable states. Our study suggests new experimental analyses that could be carried out to characterize real biological swarms.

Seismic swarms and diffuse fracturing within Triassic evaporites fed by deep degassing along the low-angle Alto Tiberina normal fault (central Apennines, Italy)

NASA Astrophysics Data System (ADS)

Piana Agostinetti, Nicola; Giacomuzzi, Genny; Chiarabba, Claudio

2017-01-01

We present high-resolution elastic models and relocated seismicity of a very active segment of the Apennines normal faulting system, computed via transdimensional local earthquake tomography (trans-D LET). Trans-D LET, a fully nonlinear approach to seismic tomography, robustly constrains high-velocity anomalies and inversions of P wave velocity, i.e., decreases of VP with depth, without introducing bias due to, e.g., a starting model, and giving the possibility to investigate the relation between fault structure, seismicity, and fluids. Changes in seismicity rate and recurring seismic swarms are frequent in the Apennines extensional belt. Deep fluids, upwelling from the delaminating continental lithosphere, are thought to be responsible for seismicity clustering in the upper crust and lubrication of normal faults during swarms and large earthquakes. We focus on the tectonic role played by the Alto Tiberina low-angle normal fault (ATF), finding displacements across the fault consistent with long-term accommodation of deformation. Our results show that recent seismic swarms affecting the area occur within a 3 km thick, high VP/VS, densely cracked, and overpressurized evaporitic layer, composed of dolostones and anhydrites. A persistent low VP, low VP/VS volume, present on top of and along the ATF low-angle detachment, traces the location of mantle-derived CO2, the upward flux of which contributes to cracking within the evaporitic layer.

Optimization of wireless sensor networks based on chicken swarm optimization algorithm

NASA Astrophysics Data System (ADS)

Wang, Qingxi; Zhu, Lihua

2017-05-01

In order to reduce the energy consumption of wireless sensor network and improve the survival time of network, the clustering routing protocol of wireless sensor networks based on chicken swarm optimization algorithm was proposed. On the basis of LEACH agreement, it was improved and perfected that the points on the cluster and the selection of cluster head using the chicken group optimization algorithm, and update the location of chicken which fall into the local optimum by Levy flight, enhance population diversity, ensure the global search capability of the algorithm. The new protocol avoided the die of partial node of intensive using by making balanced use of the network nodes, improved the survival time of wireless sensor network. The simulation experiments proved that the protocol is better than LEACH protocol on energy consumption, also is better than that of clustering routing protocol based on particle swarm optimization algorithm.

Fluid-Faulting Interactions Examined Though Massive Waveform-Based Analyses of Earthquake Swarms in Volcanic and Tectonic Settings: Mammoth Mountain, Long Valley, Lassen, and Fillmore, California Swarms, 2014-2015

NASA Astrophysics Data System (ADS)

Shelly, D. R.; Ellsworth, W. L.; Prejean, S. G.; Hill, D. P.; Hardebeck, J.; Hsieh, P. A.

2015-12-01

Earthquake swarms, sequences of sustained seismicity, convey active subsurface processes that sometimes precede larger tectonic or volcanic episodes. Their extended activity and spatiotemporal migration can often be attributed to fluid pressure transients as migrating crustal fluids (typically water and CO2) interact with subsurface structures. Although the swarms analyzed here are interpreted to be natural in origin, the mechanisms of seismic activation likely mirror those observed for earthquakes induced by industrial fluid injection. Here, we use massive-scale waveform correlation to detect and precisely locate 3-10 times as many earthquakes as included in routine catalogs for recent (2014-2015) swarms beneath Mammoth Mountain, Long Valley Caldera, Lassen Volcanic Center, and Fillmore areas of California, USA. These enhanced catalogs, with location precision as good as a few meters, reveal signatures of fluid-faulting interactions, such as systematic migration, fault-valve behavior, and fracture mesh structures, not resolved in routine catalogs. We extend this analysis to characterize source mechanism similarity even for very small newly detected events using relative P and S polarity estimates. This information complements precise locations to define fault complexities that would otherwise be invisible. In particular, although swarms often consist of groups of highly similar events, some swarms contain a population of outliers with different slip and/or fault orientations. These events highlight the complexity of fluid-faulting interactions. Despite their different settings, the four swarms analyzed here share many similarities, including pronounced hypocenter migration suggestive of a fluid pressure trigger. This includes the July 2015 Fillmore swarm, which, unlike the others, occurred outside of an obvious volcanic zone. Nevertheless, it exhibited systematic westward and downdip migration on a ~1x1.5 km low-angle, NW-dipping reverse fault at midcrustal depth.

Analysis of Earthquake Source Spectra in Salton Trough

NASA Astrophysics Data System (ADS)

Chen, X.; Shearer, P. M.

2009-12-01

Previous studies of the source spectra of small earthquakes in southern California show that average Brune-type stress drops vary among different regions, with particularly low stress drops observed in the Salton Trough (Shearer et al., 2006). The Salton Trough marks the southern end of the San Andreas Fault and is prone to earthquake swarms, some of which are driven by aseismic creep events (Lohman and McGuire, 2007). In order to learn the stress state and understand the physical mechanisms of swarms and slow slip events, we analyze the source spectra of earthquakes in this region. We obtain Southern California Seismic Network (SCSN) waveforms for earthquakes from 1977 to 2009 archived at the Southern California Earthquake Center (SCEC) data center, which includes over 17,000 events. After resampling the data to a uniform 100 Hz sample rate, we compute spectra for both signal and noise windows for each seismogram, and select traces with a P-wave signal-to-noise ratio greater than 5 between 5 Hz and 15 Hz. Using selected displacement spectra, we isolate the source spectra from station terms and path effects using an empirical Green’s function approach. From the corrected source spectra, we compute corner frequencies and estimate moments and stress drops. Finally we analyze spatial and temporal variations in stress drop in the Salton Trough and compare them with studies of swarms and creep events to assess the evolution of faulting and stress in the region. References: Lohman, R. B., and J. J. McGuire (2007), Earthquake swarms driven by aseismic creep in the Salton Trough, California, J. Geophys. Res., 112, B04405, doi:10.1029/2006JB004596 Shearer, P. M., G. A. Prieto, and E. Hauksson (2006), Comprehensive analysis of earthquake source spectra in southern California, J. Geophys. Res., 111, B06303, doi:10.1029/2005JB003979.

Obtaining Approximate Values of Exterior Orientation Elements of Multi-Intersection Images Using Particle Swarm Optimization

NASA Astrophysics Data System (ADS)

Li, X.; Li, S. W.

2012-07-01

In this paper, an efficient global optimization algorithm in the field of artificial intelligence, named Particle Swarm Optimization (PSO), is introduced into close range photogrammetric data processing. PSO can be applied to obtain the approximate values of exterior orientation elements under the condition that multi-intersection photography and a small portable plane control frame are used. PSO, put forward by an American social psychologist J. Kennedy and an electrical engineer R.C. Eberhart, is a stochastic global optimization method based on swarm intelligence, which was inspired by social behavior of bird flocking or fish schooling. The strategy of obtaining the approximate values of exterior orientation elements using PSO is as follows: in terms of image coordinate observed values and space coordinates of few control points, the equations of calculating the image coordinate residual errors can be given. The sum of absolute value of each image coordinate is minimized to be the objective function. The difference between image coordinate observed value and the image coordinate computed through collinear condition equation is defined as the image coordinate residual error. Firstly a gross area of exterior orientation elements is given, and then the adjustment of other parameters is made to get the particles fly in the gross area. After iterative computation for certain times, the satisfied approximate values of exterior orientation elements are obtained. By doing so, the procedures like positioning and measuring space control points in close range photogrammetry can be avoided. Obviously, this method can improve the surveying efficiency greatly and at the same time can decrease the surveying cost. And during such a process, only one small portable control frame with a couple of control points is employed, and there are no strict requirements for the space distribution of control points. In order to verify the effectiveness of this algorithm, two experiments are carried out. In the first experiment, images of a standard grid board are taken according to multi-intersection photography using digital camera. Three points or six points which are located on the left-down corner of the standard grid are regarded as control points respectively, and the exterior orientation elements of each image are computed through PSO, and compared with these elements computed through bundle adjustment. In the second experiment, the exterior orientation elements obtained from the first experiment are used as approximate values in bundle adjustment and then the space coordinates of other grid points on the board can be computed. The coordinate difference of grid points between these computed space coordinates and their known coordinates can be used to compute the accuracy. The point accuracy computed in above experiments are ±0.76mm and ±0.43mm respectively. The above experiments prove the effectiveness of PSO used in close range photogrammetry to compute approximate values of exterior orientation elements, and the algorithm can meet the requirement of higher accuracy. In short, PSO can get better results in a faster, cheaper way compared with other surveying methods in close range photogrammetry.

Cooperative Control of Multiple Unmanned Autonomous Vehicles

DTIC Science & Technology

2005-06-03

I I Final Report 4. TITLE AND SUBTITLE 5. FUNDING NUMBERS Cooperative Control of Multiple Unmanned Autonomous Vehicles F49620-01-1-0337 6. AUTHOR(S... Autonomous Vehicles Final Report Kendall E. Nygard Department of Computer Science and Operations Research North Dakota State University Fargo, ND 58105-5164

SWARMS Early Trials Management for The SWARMs ECSEL-H2020 Project

NASA Astrophysics Data System (ADS)

Alcaraz, Daniel; Morales, Tania; Castro, Ayoze; Barrera, Carlos; Hernández, Joaquín; Llinás, Octavio

2017-04-01

The work presented on this paper is aimed to explain how the Early Trials of the Project SWARMS were managed in order to complete the first field demonstrations on real environment. SWARMs aims to reduce the operational cost in the use of maritime robots and vehicles, in order to increase the safety of tasks and reduce profesional divers risks. This will be achieved enabling the AUVs/ROVs to work in a cooperative mesh. The challenge is to design and develop an integrated platform (a set of Software/Hardware components), incorporated into the current generation of underwater vehicles in order to improve autonomy, cooperation, robustness, cost-effectiveness, and reliability of the offshore operations. The first demonstration of the project has been performed at PLOCAN (The Oceanic Platform of the Canary Islands) where these technologies were validated on its first stage. The Early Trials have represented the first in situ deployment and test of the novel technologies developed during the initial 14 months of the Project. Going into the sea supposed a huge challenge also in terms of management. The 32 partners of SWARMS had very different requirements (logistics, technical needs, software/computation needs, etc.), and a limited time frame to test and prove their individual developments. In order to fullfill the project objectives, all these tests were divided in 7 missions that were aimed to cover this early demonstration requiements. From PLOCAN, a management protocol was designed in order to cover all the partners needs and make an efficient resource asignment from the begining. These results will be extended to other two demonstrations of the project that forseen to be held in Romania (2017) and Norway (2018).

Particle swarm optimization with recombination and dynamic linkage discovery.

PubMed

Chen, Ying-Ping; Peng, Wen-Chih; Jian, Ming-Chung

2007-12-01

In this paper, we try to improve the performance of the particle swarm optimizer by incorporating the linkage concept, which is an essential mechanism in genetic algorithms, and design a new linkage identification technique called dynamic linkage discovery to address the linkage problem in real-parameter optimization problems. Dynamic linkage discovery is a costless and effective linkage recognition technique that adapts the linkage configuration by employing only the selection operator without extra judging criteria irrelevant to the objective function. Moreover, a recombination operator that utilizes the discovered linkage configuration to promote the cooperation of particle swarm optimizer and dynamic linkage discovery is accordingly developed. By integrating the particle swarm optimizer, dynamic linkage discovery, and recombination operator, we propose a new hybridization of optimization methodologies called particle swarm optimization with recombination and dynamic linkage discovery (PSO-RDL). In order to study the capability of PSO-RDL, numerical experiments were conducted on a set of benchmark functions as well as on an important real-world application. The benchmark functions used in this paper were proposed in the 2005 Institute of Electrical and Electronics Engineers Congress on Evolutionary Computation. The experimental results on the benchmark functions indicate that PSO-RDL can provide a level of performance comparable to that given by other advanced optimization techniques. In addition to the benchmark, PSO-RDL was also used to solve the economic dispatch (ED) problem for power systems, which is a real-world problem and highly constrained. The results indicate that PSO-RDL can successfully solve the ED problem for the three-unit power system and obtain the currently known best solution for the 40-unit system.

Adaptive Control Parameters for Dispersal of Multi-Agent Mobile Ad Hoc Network (MANET) Swarms

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kurt Derr; Milos Manic

A mobile ad hoc network is a collection of independent nodes that communicate wirelessly with one another. This paper investigates nodes that are swarm robots with communications and sensing capabilities. Each robot in the swarm may operate in a distributed and decentralized manner to achieve some goal. This paper presents a novel approach to dynamically adapting control parameters to achieve mesh configuration stability. The presented approach to robot interaction is based on spring force laws (attraction and repulsion laws) to create near-optimal mesh like configurations. In prior work, we presented the extended virtual spring mesh (EVSM) algorithm for the dispersionmore » of robot swarms. This paper extends the EVSM framework by providing the first known study on the effects of adaptive versus static control parameters on robot swarm stability. The EVSM algorithm provides the following novelties: 1) improved performance with adaptive control parameters and 2) accelerated convergence with high formation effectiveness. Simulation results show that 120 robots reach convergence using adaptive control parameters more than twice as fast as with static control parameters in a multiple obstacle environment.« less

[Application of an Adaptive Inertia Weight Particle Swarm Algorithm in the Magnetic Resonance Bias Field Correction].

PubMed

Wang, Chang; Qin, Xin; Liu, Yan; Zhang, Wenchao

2016-06-01

An adaptive inertia weight particle swarm algorithm is proposed in this study to solve the local optimal problem with the method of traditional particle swarm optimization in the process of estimating magnetic resonance(MR)image bias field.An indicator measuring the degree of premature convergence was designed for the defect of traditional particle swarm optimization algorithm.The inertia weight was adjusted adaptively based on this indicator to ensure particle swarm to be optimized globally and to avoid it from falling into local optimum.The Legendre polynomial was used to fit bias field,the polynomial parameters were optimized globally,and finally the bias field was estimated and corrected.Compared to those with the improved entropy minimum algorithm,the entropy of corrected image was smaller and the estimated bias field was more accurate in this study.Then the corrected image was segmented and the segmentation accuracy obtained in this research was 10% higher than that with improved entropy minimum algorithm.This algorithm can be applied to the correction of MR image bias field.

Emergent Runaway into an Avoidance Area in a Swarm of Soldier Crabs

PubMed Central

Murakami, Hisashi; Tomaru, Takenori; Nishiyama, Yuta; Moriyama, Toru; Niizato, Takayuki; Gunji, Yukio-Pegio

2014-01-01

Emergent behavior that arises from a mass effect is one of the most striking aspects of collective animal groups. Investigating such behavior would be important in order to understand how individuals interact with their neighbors. Although there are many experiments that have used collective animals to investigate social learning or conflict between individuals and society such as that between a fish and a school, reports on mass effects are rare. In this study, we show that a swarm of soldier crabs could spontaneously enter a water pool, which are usually avoided, by forming densely populated part of a swarm at the edge of the water pool. Moreover, we show that the observed behavior can be explained by the model of collective behavior based on inherent noise that is individuals’ different velocities in a directed group. Our results suggest that inherent noise, which is widely seen in collective animals, can contribute to formation and/or maintenance of a swarm and that the dense swarm can enter the pool by means of enhanced inherent noise. PMID:24839970

Precise Orbit Solution for Swarm Using Space-Borne GPS Data and Optimized Pseudo-Stochastic Pulses

PubMed Central

Zhang, Bingbing; Wang, Zhengtao; Zhou, Lv; Feng, Jiandi; Qiu, Yaodong; Li, Fupeng

2017-01-01

Swarm is a European Space Agency (ESA) project that was launched on 22 November 2013, which consists of three Swarm satellites. Swarm precise orbits are essential to the success of the above project. This study investigates how well Swarm zero-differenced (ZD) reduced-dynamic orbit solutions can be determined using space-borne GPS data and optimized pseudo-stochastic pulses under high ionospheric activity. We choose Swarm space-borne GPS data from 1–25 October 2014, and Swarm reduced-dynamic orbits are obtained. Orbit quality is assessed by GPS phase observation residuals and compared with Precise Science Orbits (PSOs) released by ESA. Results show that pseudo-stochastic pulses with a time interval of 6 min and a priori standard deviation (STD) of 10−2 mm/s in radial (R), along-track (T) and cross-track (N) directions are optimized to Swarm ZD reduced-dynamic precise orbit determination (POD). During high ionospheric activity, the mean Root Mean Square (RMS) of Swarm GPS phase residuals is at 9–11 mm, Swarm orbit solutions are also compared with Swarm PSOs released by ESA and the accuracy of Swarm orbits can reach 2–4 cm in R, T and N directions. Independent Satellite Laser Ranging (SLR) validation indicates that Swarm reduced-dynamic orbits have an accuracy of 2–4 cm. Swarm-B orbit quality is better than those of Swarm-A and Swarm-C. The Swarm orbits can be applied to the geomagnetic, geoelectric and gravity field recovery. PMID:28335538

Improving Vector Evaluated Particle Swarm Optimisation by Incorporating Nondominated Solutions

PubMed Central

Lim, Kian Sheng; Ibrahim, Zuwairie; Buyamin, Salinda; Ahmad, Anita; Naim, Faradila; Ghazali, Kamarul Hawari; Mokhtar, Norrima

2013-01-01

The Vector Evaluated Particle Swarm Optimisation algorithm is widely used to solve multiobjective optimisation problems. This algorithm optimises one objective using a swarm of particles where their movements are guided by the best solution found by another swarm. However, the best solution of a swarm is only updated when a newly generated solution has better fitness than the best solution at the objective function optimised by that swarm, yielding poor solutions for the multiobjective optimisation problems. Thus, an improved Vector Evaluated Particle Swarm Optimisation algorithm is introduced by incorporating the nondominated solutions as the guidance for a swarm rather than using the best solution from another swarm. In this paper, the performance of improved Vector Evaluated Particle Swarm Optimisation algorithm is investigated using performance measures such as the number of nondominated solutions found, the generational distance, the spread, and the hypervolume. The results suggest that the improved Vector Evaluated Particle Swarm Optimisation algorithm has impressive performance compared with the conventional Vector Evaluated Particle Swarm Optimisation algorithm. PMID:23737718

Improving Vector Evaluated Particle Swarm Optimisation by incorporating nondominated solutions.

PubMed

Lim, Kian Sheng; Ibrahim, Zuwairie; Buyamin, Salinda; Ahmad, Anita; Naim, Faradila; Ghazali, Kamarul Hawari; Mokhtar, Norrima

2013-01-01

The Vector Evaluated Particle Swarm Optimisation algorithm is widely used to solve multiobjective optimisation problems. This algorithm optimises one objective using a swarm of particles where their movements are guided by the best solution found by another swarm. However, the best solution of a swarm is only updated when a newly generated solution has better fitness than the best solution at the objective function optimised by that swarm, yielding poor solutions for the multiobjective optimisation problems. Thus, an improved Vector Evaluated Particle Swarm Optimisation algorithm is introduced by incorporating the nondominated solutions as the guidance for a swarm rather than using the best solution from another swarm. In this paper, the performance of improved Vector Evaluated Particle Swarm Optimisation algorithm is investigated using performance measures such as the number of nondominated solutions found, the generational distance, the spread, and the hypervolume. The results suggest that the improved Vector Evaluated Particle Swarm Optimisation algorithm has impressive performance compared with the conventional Vector Evaluated Particle Swarm Optimisation algorithm.

Application of a swarm-based approach for phase unwrapping

NASA Astrophysics Data System (ADS)

da S. Maciel, Lucas; Albertazzi G., Armando, Jr.

2014-07-01

An algorithm for phase unwrapping based on swarm intelligence is proposed. The novel approach is based on the emergent behavior of swarms. This behavior is the result of the interactions between independent agents following a simple set of rules and is regarded as fast, flexible and robust. The rules here were designed with two purposes. Firstly, the collective behavior must result in a reliable map of the unwrapped phase. The unwrapping reliability was evaluated by each agent during run-time, based on the quality of the neighboring pixels. In addition, the rule set must result in a behavior that focuses on wrapped regions. Stigmergy and communication rules were implemented in order to enable each agent to seek less worked areas of the image. The agents were modeled as Finite-State Machines. Based on the availability of unwrappable pixels, each agent assumed a different state in order to better adapt itself to the surroundings. The implemented rule set was able to fulfill the requirements on reliability and focused unwrapping. The unwrapped phase map was comparable to those from established methods as the agents were able to reliably evaluate each pixel quality. Also, the unwrapping behavior, being observed in real time, was able to focus on workable areas as the agents communicated in order to find less traveled regions. The results were very positive for such a new approach to the phase unwrapping problem. Finally, the authors see great potential for future developments concerning the flexibility, robustness and processing times of the swarm-based algorithm.

Firefly algorithm for cardinality constrained mean-variance portfolio optimization problem with entropy diversity constraint.

PubMed

Bacanin, Nebojsa; Tuba, Milan

2014-01-01

Portfolio optimization (selection) problem is an important and hard optimization problem that, with the addition of necessary realistic constraints, becomes computationally intractable. Nature-inspired metaheuristics are appropriate for solving such problems; however, literature review shows that there are very few applications of nature-inspired metaheuristics to portfolio optimization problem. This is especially true for swarm intelligence algorithms which represent the newer branch of nature-inspired algorithms. No application of any swarm intelligence metaheuristics to cardinality constrained mean-variance (CCMV) portfolio problem with entropy constraint was found in the literature. This paper introduces modified firefly algorithm (FA) for the CCMV portfolio model with entropy constraint. Firefly algorithm is one of the latest, very successful swarm intelligence algorithm; however, it exhibits some deficiencies when applied to constrained problems. To overcome lack of exploration power during early iterations, we modified the algorithm and tested it on standard portfolio benchmark data sets used in the literature. Our proposed modified firefly algorithm proved to be better than other state-of-the-art algorithms, while introduction of entropy diversity constraint further improved results.

Firefly Algorithm for Cardinality Constrained Mean-Variance Portfolio Optimization Problem with Entropy Diversity Constraint

PubMed Central

2014-01-01

Portfolio optimization (selection) problem is an important and hard optimization problem that, with the addition of necessary realistic constraints, becomes computationally intractable. Nature-inspired metaheuristics are appropriate for solving such problems; however, literature review shows that there are very few applications of nature-inspired metaheuristics to portfolio optimization problem. This is especially true for swarm intelligence algorithms which represent the newer branch of nature-inspired algorithms. No application of any swarm intelligence metaheuristics to cardinality constrained mean-variance (CCMV) portfolio problem with entropy constraint was found in the literature. This paper introduces modified firefly algorithm (FA) for the CCMV portfolio model with entropy constraint. Firefly algorithm is one of the latest, very successful swarm intelligence algorithm; however, it exhibits some deficiencies when applied to constrained problems. To overcome lack of exploration power during early iterations, we modified the algorithm and tested it on standard portfolio benchmark data sets used in the literature. Our proposed modified firefly algorithm proved to be better than other state-of-the-art algorithms, while introduction of entropy diversity constraint further improved results. PMID:24991645

Emergence of a snake-like structure in mobile distributed agents: an exploratory agent-based modeling approach.

PubMed

Niazi, Muaz A

2014-01-01

The body structure of snakes is composed of numerous natural components thereby making it resilient, flexible, adaptive, and dynamic. In contrast, current computer animations as well as physical implementations of snake-like autonomous structures are typically designed to use either a single or a relatively smaller number of components. As a result, not only these artificial structures are constrained by the dimensions of the constituent components but often also require relatively more computationally intensive algorithms to model and animate. Still, these animations often lack life-like resilience and adaptation. This paper presents a solution to the problem of modeling snake-like structures by proposing an agent-based, self-organizing algorithm resulting in an emergent and surprisingly resilient dynamic structure involving a minimal of interagent communication. Extensive simulation experiments demonstrate the effectiveness as well as resilience of the proposed approach. The ideas originating from the proposed algorithm can not only be used for developing self-organizing animations but can also have practical applications such as in the form of complex, autonomous, evolvable robots with self-organizing, mobile components with minimal individual computational capabilities. The work also demonstrates the utility of exploratory agent-based modeling (EABM) in the engineering of artificial life-like complex adaptive systems.

Emergence of a Snake-Like Structure in Mobile Distributed Agents: An Exploratory Agent-Based Modeling Approach

PubMed Central

Niazi, Muaz A.

2014-01-01

The body structure of snakes is composed of numerous natural components thereby making it resilient, flexible, adaptive, and dynamic. In contrast, current computer animations as well as physical implementations of snake-like autonomous structures are typically designed to use either a single or a relatively smaller number of components. As a result, not only these artificial structures are constrained by the dimensions of the constituent components but often also require relatively more computationally intensive algorithms to model and animate. Still, these animations often lack life-like resilience and adaptation. This paper presents a solution to the problem of modeling snake-like structures by proposing an agent-based, self-organizing algorithm resulting in an emergent and surprisingly resilient dynamic structure involving a minimal of interagent communication. Extensive simulation experiments demonstrate the effectiveness as well as resilience of the proposed approach. The ideas originating from the proposed algorithm can not only be used for developing self-organizing animations but can also have practical applications such as in the form of complex, autonomous, evolvable robots with self-organizing, mobile components with minimal individual computational capabilities. The work also demonstrates the utility of exploratory agent-based modeling (EABM) in the engineering of artificial life-like complex adaptive systems. PMID:24701135

Moving without a purpose: an experimental study of swarm guidance in the Western honey bee, Apis mellifera.

PubMed

Makinson, James C; Beekman, Madeleine

2014-06-01

During reproductive swarming, honey bee scouts perform two very important functions. Firstly, they find new nesting locations and return to the swarm cluster to communicate their discoveries. Secondly, once the swarm is ready to depart, informed scout bees act as guides, leading the swarm to its final destination. We have previously hypothesised that the two processes, selecting a new nest site and swarm guidance, are tightly linked in honey bees. When swarms can be laissez faire about where they nest, reaching directional consensus prior to lift off seems unnecessary. If, in contrast, it is essential that the swarm reaches a precise location, either directional consensus must be near unanimous prior to swarm departure or only a select subgroup of the scouts guide the swarm. Here, we tested experimentally whether directional consensus is necessary for the successful guidance of swarms of the Western honey bee Apis mellifera by forcing swarms into the air prior to the completion of the decision-making process. Our results show that swarms were unable to guide themselves prior to the swarm reaching the pre-flight buzzing phase of the decision-making process, even when directional consensus was high. We therefore suggest that not all scouts involved in the decision-making process attempt to guide the swarm. © 2014. Published by The Company of Biologists Ltd.

Systems, methods and apparatus for generation and verification of policies in autonomic computing systems

NASA Technical Reports Server (NTRS)

Rouff, Christopher A. (Inventor); Sterritt, Roy (Inventor); Truszkowski, Walter F. (Inventor); Hinchey, Michael G. (Inventor); Gracanin, Denis (Inventor); Rash, James L. (Inventor)

2011-01-01

Described herein is a method that produces fully (mathematically) tractable development of policies for autonomic systems from requirements through to code generation. This method is illustrated through an example showing how user formulated policies can be translated into a formal mode which can then be converted to code. The requirements-based programming method described provides faster, higher quality development and maintenance of autonomic systems based on user formulation of policies.Further, the systems, methods and apparatus described herein provide a way of analyzing policies for autonomic systems and facilities the generation of provably correct implementations automatically, which in turn provides reduced development time, reduced testing requirements, guarantees of correctness of the implementation with respect to the policies specified at the outset, and provides a higher degree of confidence that the policies are both complete and reasonable. The ability to specify the policy for the management of a system and then automatically generate an equivalent implementation greatly improves the quality of software, the survivability of future missions, in particular when the system will operate untended in very remote environments, and greatly reduces development lead times and costs.