Multi-robot maze exploration using an efficient cost-utility method

TL;DR

This paper introduces a new cost-utility algorithm for multi-robot maze exploration, comparing it with existing methods to demonstrate improved efficiency and coverage in complex environments.

Contribution

It presents a novel cost-utility algorithm for multi-robot maze exploration, integrating and comparing it with established methods for enhanced performance.

Findings

The new algorithm outperforms baseline methods in coverage efficiency.

Comparative analysis shows improved exploration speed and accuracy.

Code and results are publicly available for validation.

Abstract

In the field of modern robotics, robots are proving to be useful in tackling high-risk situations, such as navigating hazardous environments like burning buildings, earthquake-stricken areas, or patrolling crime-ridden streets, as well as exploring uncharted caves. These scenarios share similarities with maze exploration problems in terms of complexity. While several methods have been proposed for single-agent systems, ranging from potential fields to flood-fill methods, recent research endeavors have focused on creating methods tailored for multiple agents to enhance the quality and efficiency of maze coverage. The contribution of this paper is the implementation of established maze exploration methods and their comparison with a new cost-utility algorithm designed for multiple agents, which combines the existing methodologies to optimize exploration outcomes. Through a comprehensive and comparative analysis, this paper evaluates the performance of the new approach against the implemented baseline methods from the literature, highlighting its efficacy and potential advantages in various scenarios. The code and experimental results supporting this study are available in the following repository (https://github.com/manouslinard/multiagent-exploration/).