Mean Field Behavior of Collaborative Multiagent Foragers

Journal article (2022)

Authors

D. Jarne Ornia Team Manuel Mazo Jr - Mechanical, Maritime and Materials Engineering
Pedro J. Zufiria Universidad Politécnica de Madrid
M. Mazo Team Manuel Mazo Jr - Mechanical, Maritime and Materials Engineering
Research Group
Team Manuel Mazo Jr (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2022 D. Jarne Ornia, Pedro J. Zufiria, M. Mazo
DOI
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https://doi.org/10.1109/TRO.2022.3152691
Collaboration Convergence Stochastic processes Trajectory Task analysis Robot kinematics Learning and adaptive systems Agent-based systems Mean field models Random variables Swarms
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Published Date

2022

Language

English

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Delft Center for Systems and Control

Research Group

Team Manuel Mazo Jr

Abstract

Collaborative multiagent robotic systems, where agents coordinate by modifying a shared environment often result in undesired dynamical couplings that complicate the analysis and experiments when solving a specific problem or task. Simultaneously, biologically inspired robotics rely on simplifying agents and increasing their number to obtain more efficient solutions to such problems, drawing similarities with natural processes. In this work, we focus on the problem of a biologically inspired multiagent system solving collaborative foraging. We show how mean field techniques can be used to re-formulate such a stochastic multiagent problem into a deterministic autonomous system. This de-couples agent dynamics, enabling the computation of limit behaviors and the analysis of optimality guarantees. Furthermore, we analyse how having finite number of agents affects the performance when compared to the mean field limit and we discuss the implications of such limit approximations in this multiagent system, which have impact on more general collaborative stochastic problems.