Escaping Local Minima: Hybrid Artificial Potential Field with Wall-Follower for Decentralized Multi-Robot Navigation

TL;DR

This paper presents a hybrid decentralized multi-robot navigation method combining artificial potential fields with wall-following behavior to effectively escape local minima in environments with nonconvex obstacles, using only local sensing.

Contribution

It introduces a novel hybrid approach integrating wall-following with APF, including two switching algorithms, to improve navigation success without global maps or communication.

Findings

Higher success rates than existing methods.

Effective in complex nonconvex environments.

No reliance on global maps or inter-robot communication.

Abstract

We tackle the challenges of decentralized multi-robot navigation in environments with nonconvex obstacles, where complete environmental knowledge is unavailable. While reactive methods like Artificial Potential Field (APF) offer simplicity and efficiency, they suffer from local minima, causing robots to become trapped due to their lack of global environmental awareness. Other existing solutions either rely on inter-robot communication, are limited to single-robot scenarios, or struggle to overcome nonconvex obstacles effectively. Our proposed methods enable collision-free navigation using only local sensor and state information without a map. By incorporating a wall-following (WF) behavior into the APF approach, our method allows robots to escape local minima, even in the presence of nonconvex and dynamic obstacles including other robots. We introduce two algorithms for switching between APF and WF: a rule-based system and an encoder network trained on expert demonstrations. Experimental results show that our approach achieves substantially higher success rates compared to state-of-the-art methods, highlighting its ability to overcome the limitations of local minima in complex environments