Abstract
Robotic shepherding is a bio-inspired approach to autonomously guiding a swarm of agents towards a desired location and has earned increasing research interest recently. However, shepherding a highly dispersed swarm in an obstructive environment remains challenging for the existing methods. To improve the shepherding efficacy in complex environments with obstacles and dispersed sheep, this paper proposes a planning-assisted autonomous shepherding framework with collision avoidance. The proposed approach transforms the swarm shepherding problem into a single Travelling Salesman Problem (TSP), with the sheepdog moving mode classified into non-interaction and interaction mode. Additionally, an adaptive switching approach is integrated into the framework to guide real-time path planning for avoiding collisions with obstacles and sometimes with sheep swarm. Then the overarching hierarchical mission planning system is presented, which consists of a grouping approach to obtain sheep sub-swarms, a general TSP solver for determining the optimal push sequence of sub-swarms, and an online path planner for calculating optimal paths for both sheepdogs and sheep. The experiments on a range of environments, both with and without obstacles, quantitatively demonstrate the effectiveness of the proposed shepherding framework and planning approaches.
Abstract (translated)
机器人牧羊是一种基于生物启发式的自主引导一群Agent向目标地点前进的方法,最近引起了越来越多的研究兴趣。然而,在充满障碍的复杂环境中引导大量分散的羊群仍然是现有方法的挑战。为了提高在存在障碍物和分散羊群的复杂环境中的牧羊效果,本文提出了一种计划辅助的自主牧羊框架,并引入了避免碰撞的方法。该框架将羊群牧羊问题转化为单个旅行推销员问题(TSP),并将羊狗的运动模式分为非交互和交互模式。此外,该框架还集成了自适应切换方法,以指导实时路径规划,以避免与障碍物或有时与羊群碰撞。然后,提出了一个的总体任务规划系统,该系统包括一种分组方法来获取羊的子群体,一个通用TSP求解器以确定子群体的最佳推进序列,以及一个在线路径规划器,以计算羊狗和羊的最佳路径。在存在障碍物和无障碍物的多种环境中进行了实验,定量证明了所提出的牧羊框架和规划方法的有效性。
URL
https://arxiv.org/abs/2301.10363
