Multi-robot obstacle-aware shepherding of non-cohesive target agents

TL;DR

This paper introduces a new multi-robot shepherding control strategy for non-cohesive targets in obstacle-rich environments, combining goal-directed steering and obstacle avoidance.

Contribution

It presents a hybrid control policy enabling herders to guide non-cohesive targets around obstacles toward a goal, a novel approach compared to prior flocking-based methods.

Findings

Numerical simulations show higher target confinement rates in cluttered environments.

Experimental validation with TurtleBot4 confirms practical effectiveness.

The method outperforms existing shepherding techniques in complex scenarios.

Abstract

This paper presents a novel control strategy for multi-agent shepherding of non-cohesive targets in obstacle-rich environments. Unlike previous approaches that assume cohesive flocking behavior, our method handles targets that interact only with nearby herders through repulsive forces and exhibit no inter-target coordination. Each herder employs a hybrid control policy that combines direct goal-oriented steering with obstacle-tangent maneuvering, enabling targets to circumnavigate obstacles while being guided toward a goal region. The herder dynamics integrate three key behaviors: return-to-goal motion when idle, target steering with adaptive directional control, and obstacle avoidance using both normal and tangential force components. Numerical simulations demonstrate superior performance compared to existing shepherding methods, achieving higher target confinement rates in cluttered environments. Experimental validation using TurtleBot4 herders and Osoyoo target robots in an indoor arena confirms the practical effectiveness of the proposed approach.