Asymmetric Information Enhanced Mapping Framework for Multirobot Exploration based on Deep Reinforcement Learning

TL;DR

This paper introduces AIM-Mapping, a novel multirobot exploration framework leveraging asymmetric information and deep reinforcement learning to improve environment mapping and exploration efficiency.

Contribution

It proposes an asymmetric actor-critic training framework that utilizes privilege information for environment representation and decision-making in multirobot exploration.

Findings

Significant performance improvement over existing methods.

Effective use of privilege information in training.

Successful validation in Gibson simulation environments.

Abstract

Despite the great development of multirobot technologies, efficiently and collaboratively exploring an unknown environment is still a big challenge. In this paper, we propose AIM-Mapping, a Asymmetric InforMation Enhanced Mapping framework. The framework fully utilizes the privilege information in the training process to help construct the environment representation as well as the supervised signal in an asymmetric actor-critic training framework. Specifically, privilege information is used to evaluate the exploration performance through an asymmetric feature representation module and a mutual information evaluation module. The decision-making network uses the trained feature encoder to extract structure information from the environment and combines it with a topological map constructed based on geometric distance. Utilizing this kind of topological map representation, we employ topological graph matching to assign corresponding boundary points to each robot as long-term goal points. We conduct experiments in real-world-like scenarios using the Gibson simulation environments. It validates that the proposed method, when compared to existing methods, achieves great performance improvement.