Distributed Multi-Robot Obstacle Avoidance via Logarithmic Map-based Deep Reinforcement Learning

TL;DR

This paper introduces a novel logarithmic map-based deep reinforcement learning approach for multi-robot obstacle avoidance that operates without communication, improving accuracy, stability, and success rates in complex environments.

Contribution

It proposes a new logarithmic map representation and training framework for multi-robot obstacle avoidance without communication, enhancing generalization and efficiency.

Findings

Improved obstacle avoidance success rate.

Enhanced stability in complex scenarios.

Effective in both simulation and real-world tests.

Abstract

Developing a safe, stable, and efficient obstacle avoidance policy in crowded and narrow scenarios for multiple robots is challenging. Most existing studies either use centralized control or need communication with other robots. In this paper, we propose a novel logarithmic map-based deep reinforcement learning method for obstacle avoidance in complex and communication-free multi-robot scenarios. In particular, our method converts laser information into a logarithmic map. As a step toward improving training speed and generalization performance, our policies will be trained in two specially designed multi-robot scenarios. Compared to other methods, the logarithmic map can represent obstacles more accurately and improve the success rate of obstacle avoidance. We finally evaluate our approach under a variety of simulation and real-world scenarios. The results show that our method provides a more stable and effective navigation solution for robots in complex multi-robot scenarios and pedestrian scenarios. Videos are available at https://youtu.be/r0EsUXe6MZE.