Effective Robotic Swarm Shepherding in the Presence of Obstacles

Abstract

We present a modified planning-assisted swarm shepherding method to effectively control multi-robot (sheepdogs) when herding a swarm of reactive agents (sheep) towards a goal and in environments with obstacles. Given a highly-dispersed sheep swarm, a mission planner based on Ant Colony Optimisation and A * is designed and developed to support the shepherding task. To apply the swarm shepherding method to real robots, a multi-layer environmental modelling method is proposed to construct customised environment maps for sheep and sheepdogs according to their physical characteristics. Then, a lookahead - based sub-goal selection method is presented for herding the sheep swarm to follow the A * optimised reference path. Furthermore, a circle-based method for selecting feasible driving/collecting points while avoiding obstacles is designed. Experiments are conducted in numerical simulation environments to compare the proposed method with the state-of-the-art planning-assisted shepherding method, followed by testing in the robot simulation platform CoppeliaSim to demonstrate the effectiveness of the proposed method.