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Distributed Data-Driven Predictive Control for Multi-Agent Collaborative Legged Locomotion

Fawcett, Randall T. and Amanzadeh, Leila and Kim, Jeeseop and Ames, Aaron D. and Akbari Hamed, Kaveh (2022) Distributed Data-Driven Predictive Control for Multi-Agent Collaborative Legged Locomotion. . (Unpublished)

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The aim of this work is to define a planner that enables robust legged locomotion for complex multi-agent systems consisting of several holonomically constrained quadrupeds. To this end, we employ a methodology based on behavioral systems theory to model the sophisticated and high-dimensional structure induced by the holonomic constraints. The resulting model is then used in tandem with distributed control techniques such that the computational burden is shared across agents while the coupling between agents is preserved. Finally, this distributed model is framed in the context of a predictive controller, resulting in a robustly stable method for trajectory planning. This methodology is tested in simulation with up to five agents and is further experimentally validated on three A1 quadrupedal robots subject to various uncertainties, including payloads, rough terrain, and push disturbances.

Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription Paper
Fawcett, Randall T.0000-0002-7704-2706
Kim, Jeeseop0000-0003-4028-2359
Ames, Aaron D.0000-0003-0848-3177
Akbari Hamed, Kaveh0000-0001-9597-1691
Additional Information:Attribution 4.0 International (CC BY 4.0). The work of R. T. Fawcett is supported by the National Science Foundation (NSF) under Grant 2128948. The work of K. Akbari Hamed is supported by the NSF under Grants 1924617 and 2128948. The work of A. D. Ames is supported by the NSF under Grant 1924526.
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Record Number:CaltechAUTHORS:20221219-234108951
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:118470
Deposited By: George Porter
Deposited On:21 Dec 2022 19:19
Last Modified:21 Dec 2022 19:19

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