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Probabilistic swarm guidance using optimal transport

Bandyopadhyay, Saptarshi and Chung, Soon-Jo and Hadaegh, Fred Y. (2014) Probabilistic swarm guidance using optimal transport. In: 2014 IEEE Conference on Control Applications (CCA). IEEE , Piscataway, NJ, pp. 498-505. ISBN 978-1-4799-7409-2.

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Probabilistic swarm guidance enables autonomous agents to generate their individual trajectories independently so that the entire swarm converges to the desired distribution shape. In contrast with previous homogeneous or inhomogeneous Markov chain based approaches [1], this paper presents an optimal transport based approach which guarantees faster convergence, minimizes a given cost function, and reduces the number of transitions for achieving the desired formation. Each agent first estimates the current swarm distribution by communicating with neighboring agents and using a consensus algorithm and then solves the optimal transport problem, which is recast as a linear program, to determine its transition probabilities. We discuss methods for handling motion constraints and also demonstrate the superior performance of the proposed algorithm by numerically comparing it with existing Markov chain based strategies.

Item Type:Book Section
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Chung, Soon-Jo0000-0002-6657-3907
Additional Information:© 2014 IEEE. This research was supported in part by AFOSR grant FA95501210193 and the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration.
Funding AgencyGrant Number
Air Force Office of Scientific Research (AFOSR)FA95501210193
Record Number:CaltechAUTHORS:20170109-103702672
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Official Citation:S. Bandyopadhyay, S. J. Chung and F. Y. Hadaegh, "Probabilistic swarm guidance using optimal transport," 2014 IEEE Conference on Control Applications (CCA), Juan Les Antibes, 2014, pp. 498-505. doi: 10.1109/CCA.2014.6981395
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:73333
Deposited By: Ruth Sustaita
Deposited On:09 Jan 2017 20:57
Last Modified:03 Oct 2019 16:26

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