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.

翻译：本文提出一种新颖的控制方法，旨在利用机器狗在杂乱未知环境中引导机器人羊群。狗型智能体持续扫描环境并计算安全轨迹，以将羊群引导至最终目的地。所提出的基于优化的控制器通过采用控制屏障函数，保证羊群与参考轨迹保持期望距离。同时采用额外的控制屏障函数约束以确保智能体间避碰及障碍物规避。通过仿真实验严格验证了所提方法的有效性，结果表明该方法能在复杂杂乱环境中成功实现机器人羊群的引导。