Multi-Robot Obstacle-Aware Shepherding of Non-Cohesive Target Agents
Cinzia Tomaselli, Stefano Covone, Andreagiovanni Reina, Mario di bernardo
AI summary
Problem
Existing shepherding methods assume targets flock cohesively, a condition that frequently fails in practice, and struggle to navigate cluttered environments without deadlocking. This gap leaves a critical need for control strategies that can individually steer independent targets while avoiding obstacles.
Approach
Herders use a decentralized hybrid control policy that blends normal repulsive forces with tangential sliding forces to circumnavigate obstacles while individually pushing non-cohesive targets toward a goal region.
Key results
- Hybrid normal-tangential force strategy prevents herder deadlock around obstacles
- Achieves 100% target confinement in simulations versus ~5% for cohesive baselines
- Validated in real-world experiments with TurtleBot4 herders and Osoyoo target robots
- Decentralized architecture maintains low computational overhead for large agent populations
Why it matters
Enables reliable multi-robot coordination in realistic, cluttered settings for applications like crowd management and search-and-rescue where targets do not flock.
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 nor- mal and tangential force components. Numerical simulations demonstrate superior performance compared to existing shep- herding methods, achieving higher target confinement rates in cluttered environments. Experimental validation using Turtle- Bot4 herders and Osoyoo target robots in an indoor arena confirms the practical effectiveness of the proposed approach.