TY - GEN
T1 - Decentralized stochastic control of robotic swarm density
T2 - 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2017
AU - Li, Hanjun
AU - Feng, Chunhan
AU - Ehrhard, Henry
AU - Shen, Yijun
AU - Cobos, Bernardo
AU - Zhang, Fangbo
AU - Elamvazhuthi, Karthik
AU - Berman, Spring
AU - Haberland, Matt
AU - Bertozzi, Andrea L.
N1 - Funding Information:
*Corresponding author: haberland@ucla.edu 1UCLA Department of Mathematics, Los Angeles, CA 90095 2Nankai Univerity, School of Physics, Tianjin 300071, China 3Grinnell College, Mathematics and Statistics, Grinnell, IA 50112 4Arizona State University, School for Engineering of Matter, Transport, and Energy, Tempe, AZ, 85281 The authors gratefully acknowledge the support of the following grants: NSF DMS-1045536, NSF CMMI-1435709, NSF CMMI-1436960, and the Cross-Disciplinary Scholars in Science and Technology Program.
Publisher Copyright:
© 2017 IEEE.
PY - 2017/12/13
Y1 - 2017/12/13
N2 - This paper explores a stochastic approach for controlling swarms of independent robots toward a target distribution in a bounded domain. The robot swarm has no central controller, and individual robots lack both communication and localization capabilities. Robots can only measure a scalar field (e.g. concentration of a chemical) from the environment and from this deduce the desired local swarm density. Based on this value, each robot follows a simple control law that causes the swarm as a whole to diffuse toward the target distribution. Using a new holonomic drive robot, we present the first confirmation of this control law with physical experiment. Despite deviations from assumptions underpinning the theory, the swarm achieves the theorized convergence to the target distribution in both simulation and experiment. In fact, simulated and experimental performance agree with one another and with our hypothesis that the error from the target distribution is inversely proportional to the square root of the number of robots. This is evidence that the algorithm is both practical and easily scalable to large swarms.
AB - This paper explores a stochastic approach for controlling swarms of independent robots toward a target distribution in a bounded domain. The robot swarm has no central controller, and individual robots lack both communication and localization capabilities. Robots can only measure a scalar field (e.g. concentration of a chemical) from the environment and from this deduce the desired local swarm density. Based on this value, each robot follows a simple control law that causes the swarm as a whole to diffuse toward the target distribution. Using a new holonomic drive robot, we present the first confirmation of this control law with physical experiment. Despite deviations from assumptions underpinning the theory, the swarm achieves the theorized convergence to the target distribution in both simulation and experiment. In fact, simulated and experimental performance agree with one another and with our hypothesis that the error from the target distribution is inversely proportional to the square root of the number of robots. This is evidence that the algorithm is both practical and easily scalable to large swarms.
UR - http://www.scopus.com/inward/record.url?scp=85041954151&partnerID=8YFLogxK
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U2 - 10.1109/IROS.2017.8206299
DO - 10.1109/IROS.2017.8206299
M3 - Conference contribution
AN - SCOPUS:85041954151
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 4341
EP - 4347
BT - IROS 2017 - IEEE/RSJ International Conference on Intelligent Robots and Systems
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 24 September 2017 through 28 September 2017
ER -