Distributed multi-robot potential-field-based exploration with submap-based mapping and noise-augmented strategy

TL;DR

This paper introduces a distributed multi-robot exploration method combining submap-based mapping and a noise-augmented potential field strategy, significantly improving exploration speed and collaboration in unknown environments.

Contribution

It presents a novel distributed mapping approach and an enhanced exploration strategy with noise augmentation, advancing multi-robot autonomous exploration capabilities.

Findings

Outperforms existing methods in exploration speed

Enhances collaboration among robots

Proven effective in both simulation and real-world scenarios

Abstract

Multi-robot collaboration has become a needed component in unknown environment exploration due to its ability to accomplish various challenging situations. Potential-field-based methods are widely used for autonomous exploration because of their high efficiency and low travel cost. However, exploration speed and collaboration ability are still challenging topics. Therefore, we propose a Distributed Multi-Robot Potential-Field-Based Exploration (DMPF-Explore). In particular, we first present a Distributed Submap-Based Multi-Robot Collaborative Mapping Method (DSMC-Map), which can efficiently estimate the robot trajectories and construct the global map by merging the local maps from each robot. Second, we introduce a Potential-Field-Based Exploration Strategy Augmented with Modified Wave-Front Distance and Colored Noises (MWF-CN), in which the accessible frontier neighborhood is extended, and the colored noise provokes the enhancement of exploration performance. The proposed exploration method is deployed for simulation and real-world scenarios. The results show that our approach outperforms the existing ones regarding exploration speed and collaboration ability.