Herding stochastic autonomous agents via local control rules and online target selection strategies
Fabrizia Auletta, Davide Fiore, Michael J. Richardson, Mario di Bernardo · Autonomous Robots · 2022
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.