Robotic shepherding is a bio-inspired approach to autonomously guiding a swarm of agents towards a desired location. The research area has earned increasing research interest recently due to the efficacy of controlling a large number of agents in a swarm (sheep) using a smaller number of actuators (sheepdogs). However, shepherding a highly dispersed swarm in an obstacle-cluttered environment remains challenging for existing methods. To improve the efficacy of shepherding in complex environments with obstacles and dispersed sheep, this paper proposes a planning-assisted context-sensitive autonomous shepherding framework with collision avoidance abilities. The proposed approach models the swarm shepherding problem as a single Travelling Salesperson Problem (TSP), with two sheepdogs\textquoteright\ modes: no-interaction and interaction. An adaptive switching approach is integrated into the framework to guide real-time path planning for avoiding collisions with static and dynamic obstacles; the latter representing moving sheep swarms. We then propose an overarching hierarchical mission planning system, which is made of three sub-systems: a clustering approach to group and distinguish sheep sub-swarms, an Ant Colony Optimisation algorithm as a TSP solver for determining the optimal herding sequence of the sub-swarms, and an online path planner for calculating optimal paths for both sheepdogs and sheep. The experiments on various environments, both with and without obstacles, objectively demonstrate the effectiveness of the proposed shepherding framework and planning approaches.

翻译：机器人放牧是一种受生物启发的自主引导群体代理朝向目标位置的方法。由于利用少量执行器（牧羊犬）有效控制群体中大量代理（羊）的能力，该研究领域近年来受到越来越多的关注。然而，在充满障碍物的环境中引导高度分散的群体仍对现有方法构成挑战。为提高复杂障碍环境与分散羊群中的放牧效果，本文提出一种具有碰撞避免能力的规划辅助上下文敏感自主放牧框架。该方法将群体放牧问题建模为单个旅行商问题（TSP），并定义牧羊犬的两种模式：无交互模式与交互模式。框架中集成自适应切换方法，用于引导实时路径规划以避开静态与动态障碍物（后者代表移动的羊群）。随后提出一个分层任务规划系统，包含三个子系统：聚类方法用于分组并区分羊群子群；蚁群优化算法作为TSP求解器以确定子群的最优驱赶顺序；在线路径规划器用于计算牧羊犬与羊群的最优路径。在有无障碍物的多种环境中的实验客观证明了所提放牧框架与规划方法的有效性。