Mean Field Behavior of Collaborative Multiagent Foragers

Daniel Jarne Ornia, Pedro J. Zufiria, Manuel Mazo

Research output: Contribution to journalArticleScientificpeer-review


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.

Original languageEnglish
JournalIEEE Transactions on Robotics
Publication statusAccepted/In press - 7 Mar 2022


  • Agent-based systems
  • Collaboration
  • Convergence
  • learning and adaptive systems
  • mean field models
  • Random variables
  • Robot kinematics
  • Stochastic processes
  • swarms
  • Task analysis
  • Trajectory


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