障碍密集环境中分散机器人群体的规划辅助上下文敏感自主牧羊方法
Planning-Assisted Context-Sensitive Autonomous Shepherding of Dispersed Robotic Swarms in Obstacle-Cluttered Environments
AI总结：
针对障碍密集环境中分散机器人群体牧羊任务的现有方法不足，本文提出带避障能力的规划辅助上下文敏感自主牧羊框架，结合分层规划系统，经多场景实验验证了方法有效性。
摘要：
机器人牧羊是一种受生物启发的方法，可自主引导智能体集群到达目标位置。由于该方法能够通过数量较少的执行器（牧羊犬）控制集群（羊）中的大量智能体，近年来这一研究领域获得了越来越多的关注。然而，对于现有方法而言，在障碍密集环境中对高度分散的集群执行牧羊任务仍然存在挑战。为提升存在障碍物且羊群分散的复杂环境中的牧羊效能，本文提出了一种具备避障能力的规划辅助上下文敏感自主牧羊框架。所提方法将集群牧羊问题建模为单个旅行商问题（TSP），并设置了牧羊犬的两种工作模式：无交互模式与交互模式。框架中集成了自适应切换方法，用于引导实时路径规划，以规避静态障碍物与动态障碍物，其中动态障碍物即移动的羊群。随后本文提出了一套总体分层任务规划系统，该系统包含三个子系统：用于对羊式子集群进行分组与区分的聚类方法、作为TSP求解器以确定子集群最优驱赶顺序的蚁群优化（Ant Colony Optimisation, ACO）算法，以及用于计算牧羊犬与羊群最优路径的在线路径规划器。在有无障碍物的多种环境下开展的实验，客观验证了所提牧羊框架与规划方法的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
