Herding stochastic autonomous agents via local control rules and online target selection strategies

Fabrizia Auletta, Davide Fiore, Michael J. Richardson, Mario di Bernardo*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)
40 Downloads (Pure)

Abstract

We propose a simple yet effective set of local control rules to make a small group of “herder agents” collect and contain in a desired region a large ensemble of non-cooperative, non-flocking stochastic “target agents” in the plane. We investigate the robustness of the proposed strategies to variations of the number of target agents and the strength of the repulsive force they feel when in proximity of the herders. The effectiveness of the proposed approach is confirmed in both simulations in ROS and experiments on real robots.

Original languageEnglish
Pages (from-to)469-481
Number of pages13
JournalAutonomous Robots
Volume46
Issue number3
DOIs
Publication statusPublished - Mar 2022

Bibliographical note

Copyright the Author(s) 2022. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

  • agent-based systems
  • biologically-inspired agents
  • autonomous agents
  • multi-robot systems

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