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Review of Multi-Agent Algorithms for Collective Behavior: a Structural Taxonomy

Show simple item record Rossi, Federico Bandyopadhyay, Saptarshi Wolf, Michael Pavone, Marco 2020-05-11T22:19:09Z 2020-05-11T22:19:09Z 2018-06-13
dc.identifier.citation Networked & Autonomous Air & Space Systems - NAASS 2018, Santa Fe, New Mexico, June 13 - 15, 2018 en_US
dc.identifier.clearanceno 18-2603
dc.description.abstract In this paper, we review multi-agent collective behavior algorithms in the literature and classify them according to their underlying mathematical structure. For each mathematical technique, we identify the multi-agent coordination tasks it can be applied to, and we analyze its scalability, bandwidth use, and demonstrated maturity. We highlight how versatile techniques such as artificial potential functions can be used for applications ranging from low-level position control to high-level coordination and task allocation, we discuss possible reasons for the slow adoption of complex distributed coordination algorithms in the field, and we highlight areas for further research and development. en_US
dc.description.sponsorship NASA/JPL en_US
dc.language.iso en_US en_US
dc.publisher Pasadena, CA: Jet Propulsion Laboratory, National Aeronautics and Space Administration, 2018 en_US
dc.subject autonomous mobile robots en_US
dc.subject agents en_US
dc.subject distributed control en_US
dc.subject decentralized control en_US
dc.title Review of Multi-Agent Algorithms for Collective Behavior: a Structural Taxonomy en_US
dc.type Preprint en_US

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