Decentralized Global Connectivity Maintenance for Multi-Robot Navigation: A Reinforcement Learning Approach

TL;DR

This paper presents a decentralized reinforcement learning method for multi-robot navigation that maintains global connectivity in unknown environments, demonstrating effective generalization and reduced exploration complexity.

Contribution

It introduces a novel RL framework with connectivity constraints and behavior cloning for decentralized multi-robot navigation, improving generalization and efficiency.

Findings

The RL policy effectively maintains connectivity in various scenarios.

Behavior cloning reduces policy exploration complexity.

The approach generalizes well to unseen environments.

Abstract

The problem of multi-robot navigation of connectivity maintenance is challenging in multi-robot applications. This work investigates how to navigate a multi-robot team in unknown environments while maintaining connectivity. We propose a reinforcement learning (RL) approach to develop a decentralized policy, which is shared among multiple robots. Given range sensor measurements and the positions of other robots, the policy aims to generate control commands for navigation and preserve the global connectivity of the robot team. We incorporate connectivity concerns into the RL framework as constraints and introduce behavior cloning to reduce the exploration complexity of policy optimization. The policy is optimized with all transition data collected by multiple robots in random simulated scenarios. We validate the effectiveness of the proposed approach by comparing different combinations of connectivity constraints and behavior cloning. We also show that our policy can generalize to unseen scenarios in both simulation and holonomic robots experiments.