Frontier Shepherding: A Bio-inspired Multi-robot Framework for Large-Scale Exploration

TL;DR

This paper introduces FroShe, a bio-inspired multi-robot framework for large-scale exploration that models frontier exploration based on shepherding behavior, demonstrating improved efficiency and robustness in simulations and real-world tests.

Contribution

The paper presents a novel bio-inspired framework for multi-robot exploration, inspired by herding dogs, which is robust and requires minimal tuning across various environments.

Findings

Outperforms state-of-the-art strategies by 20% in simulations

Robust across different environment sizes and obstacle densities

Validated in real-world drone experiments

Abstract

Efficient exploration of large-scale environments remains a critical challenge in robotics, with applications ranging from environmental monitoring to search and rescue operations. This article proposes Frontier Shepherding (FroShe), a bio-inspired multi-robot framework for large-scale exploration. The framework heuristically models frontier exploration based on the shepherding behavior of herding dogs, where frontiers are treated as a swarm of sheep reacting to robots modeled as shepherding dogs. FroShe is robust across varying environment sizes and obstacle densities, requiring minimal parameter tuning for deployment across multiple agents. Simulation results demonstrate that the proposed method performs consistently, regardless of environment complexity, and outperforms state-of-the-art exploration strategies by an average of 20% with three UAVs. The approach was further validated in real-world experiments using single- and dual-drone deployments in a forest-like environment.