Multi-robot Cooperative Pursuit via Potential Field-Enhanced Reinforcement Learning

TL;DR

This paper introduces a hybrid cooperative pursuit algorithm combining reinforcement learning and potential fields, enabling decentralized multi-robot pursuit with improved efficiency and real-world applicability.

Contribution

It presents a novel hybrid approach that integrates artificial potential fields into deep reinforcement learning for decentralized multi-robot pursuit tasks.

Findings

Hybrid approach outperforms vanilla RL and pure potential field methods.

Numerical simulations validate improved pursuit policies.

Real-world robot experiments confirm feasibility and effectiveness.

Abstract

It is of great challenge, though promising, to coordinate collective robots for hunting an evader in a decentralized manner purely in light of local observations. In this paper, this challenge is addressed by a novel hybrid cooperative pursuit algorithm that combines reinforcement learning with the artificial potential field method. In the proposed algorithm, decentralized deep reinforcement learning is employed to learn cooperative pursuit policies that are adaptive to dynamic environments. The artificial potential field method is integrated into the learning process as predefined rules to improve the data efficiency and generalization ability. It is shown by numerical simulations that the proposed hybrid design outperforms the pursuit policies either learned from vanilla reinforcement learning or designed by the potential field method. Furthermore, experiments are conducted by transferring the learned pursuit policies into real-world mobile robots. Experimental results demonstrate the feasibility and potential of the proposed algorithm in learning multiple cooperative pursuit strategies.