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/.

翻译：机器人探索长期以来一直吸引着研究人员，旨在高效地绘制复杂环境地图。传统上，在此追求中采用了诸如势场法和前沿探索等技术，主要聚焦于单智能体。最近的进展已转向通过多智能体系统来优化探索效率。然而，许多现有方法忽视了关键的现实世界因素，例如广播范围限制、通信成本和覆盖重叠。本文通过提出一种分布式迷宫探索策略（CU-LVP）来弥补这些不足，该策略假设了受限的广播范围，并利用Voronoi图实现更优的区域划分。通过将传统的多智能体方法适配到具有有限广播范围的分布式环境中，本研究评估了它们在多种迷宫拓扑结构上的性能，证明了所提方法的有效性和实际适用性。支持本研究的代码和实验结果可在以下存储库中获取：https://github.com/manouslinard/multiagent-exploration/。