Robotic Shepherding in Cluttered and Unknown Environments using Control Barrier Functions

Abstract

This paper introduces a novel control methodology designed to guide a collective of robotic-sheep in a cluttered and unknown environment using robotic-dogs. The dog-agents continuously scan the environment and compute a safe trajectory to guide the sheep to their final destination. The proposed optimization-based controller guarantees that the sheep reside within a desired distance from the reference trajectory through the use of Control Barrier Functions (CBF). Additional CBF constraints are employed simultaneously to ensure inter-agent and obstacle collision avoidance. The efficacy of the proposed approach is rigorously tested in simulation, which demonstrates the successful herding of the robotic-sheep within complex and cluttered environments.