Planning-Assisted Context-Sensitive Autonomous Shepherding of Dispersed Robotic Swarms in Obstacle-Cluttered Environments
Jing Liu, Hemant Singh, Saber Elsayed, Robert Hunjet, Hussein Abbass
TL;DR
This paper addresses the challenge of autonomously shepherding a highly dispersed robotic swarm through obstacle-cluttered environments. It introduces a planning-assisted framework that treats multi-swarm shepherding as a single $TSP$, using cohesion-based grouping to form sub-swarms, offline $MMAS$ to determine an optimal visiting sequence, and online $A^*$-PP path planning with a two-mode, context-sensitive shepherding policy. Key contributions include a cohesive offline-online planning pipeline, a two-mode adaptive switching mechanism, and a demonstration that both single- and bi-sheepdog configurations significantly boost success rates while reducing travel time and energy use across 20 benchmark scenarios. The results highlight the framework’s potential for scalable, collision-avoiding swarm guidance in complex terrains, with practical implications for autonomous agriculture, crowd management, and aerial-robot coordination.
Abstract
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.