Distributed maze exploration using multiple agents and optimal goal assignment

TL;DR

This paper introduces a distributed multiagent maze exploration method that accounts for real-world constraints like limited broadcast range, using Voronoi diagrams for efficient area partitioning and goal assignment, improving exploration efficiency.

Contribution

It proposes CU-LVP, a novel distributed exploration strategy that incorporates broadcast range limitations and Voronoi diagrams for better multiagent coordination.

Findings

Effective exploration in diverse maze topologies

Improved coverage efficiency over traditional methods

Practical applicability demonstrated through experiments

Abstract

Robotic exploration has long captivated researchers aiming to map complex environments efficiently. Techniques such as potential fields and frontier exploration have traditionally been employed in this pursuit, primarily focusing on solitary agents. Recent advancements have shifted towards optimizing exploration efficiency through multiagent systems. However, many existing approaches overlook critical real-world factors, such as broadcast range limitations, communication costs, and coverage overlap. This paper addresses these gaps by proposing a distributed maze exploration strategy (CU-LVP) that assumes constrained broadcast ranges and utilizes Voronoi diagrams for better area partitioning. By adapting traditional multiagent methods to distributed environments with limited broadcast ranges, this study evaluates their performance across diverse maze topologies, demonstrating the efficacy and practical applicability of the proposed method. The code and experimental results supporting this study are available in the following repository: https://github.com/manouslinard/multiagent-exploration/.