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/).

翻译：在现代机器人学领域，机器人已被证明能有效应对高风险场景，例如在火灾建筑、地震灾区或犯罪高发街道等危险环境中进行导航与巡逻，以及探索未知洞穴。这些场景在复杂性上与迷宫探索问题具有相似性。虽然已有多种面向单智能体系统的方法被提出，涵盖从势场法到洪泛填充法等不同技术，但近期研究重点已转向开发专为多智能体设计的算法，以提升迷宫覆盖的质量与效率。本文的贡献在于实现了多种成熟的迷宫探索方法，并将其与一种新型的多智能体成本-效用算法进行比较。该算法融合了现有方法以优化探索效果。通过全面的对比分析，本文评估了新方法相对于文献中已实现的基准方法的性能，凸显了其在多种场景下的有效性与潜在优势。支持本研究的代码与实验结果可在以下代码库中获取（https://github.com/manouslinard/multiagent-exploration/）。