Recent advancements in robotics have paved the way for robots to replace humans in perilous situations, such as searching for victims in blazing buildings, earthquake-damaged structures, uncharted caves, traversing minefields, or patrolling crime-ridden streets. These challenges can be generalized as problems where agents need to explore unknown mazes. Although various algorithms for single-agent maze exploration exist, extending them to multi-agent systems poses complexities. We propose a solution: a cooperative multi-agent system of automated mobile agents for exploring unknown mazes and locating stationary targets. Our algorithm employs a potential field governing maze exploration, integrating cooperative agent behaviors like collision avoidance, coverage coordination, and path planning. This approach builds upon the Heat Equation Driven Area Coverage (HEDAC) method by Ivi\'c, Crnkovi\'c, and Mezi\'c. Unlike previous continuous domain applications, we adapt HEDAC for discrete domains, specifically mazes divided into nodes. Our algorithm is versatile, easily modified for anti-collision requirements, and adaptable to expanding mazes and numerical meshes over time. Comparative evaluations against alternative maze-solving methods illustrate our algorithm's superiority. The results highlight significant enhancements, showcasing its applicability across diverse mazes. Numerical simulations affirm its robustness, adaptability, scalability, and simplicity, enabling centralized parallel computation in autonomous systems of basic agents/robots.

翻译：近年来，机器人技术的进步为机器人替代人类执行危险任务铺平了道路，例如在燃烧的建筑、地震损毁的建筑物、未知洞穴中搜寻幸存者，穿越雷区，或在犯罪高发区域巡逻。这些问题可概括为智能体需探索未知迷宫的挑战。尽管针对单智能体迷宫探索已有多种算法，但将其拓展至多智能体系统仍存在复杂性。我们提出一种解决方案：一种用于探索未知迷宫并定位静态目标的协作式多智能体自动系统。该算法利用控制迷宫探索的势场，整合了碰撞避免、覆盖协调和路径规划等协作智能体行为。该方法基于Ivić、Crnković和Mezić提出的热方程驱动区域覆盖（HEDAC）方法。与先前针对连续域的应用不同，我们将HEDAC适配至离散域，特别是划分为节点的迷宫。我们的算法具有通用性，可轻松调整以满足防碰撞需求，并能适应随时间扩展的迷宫及数值网格。与替代迷宫求解方法的比较评估表明，该算法具有优越性。结果揭示了其显著提升，并展示了其在不同迷宫中的适用性。数值模拟验证了其鲁棒性、适应性、可扩展性及简洁性，使其能够支持由基础智能体/机器人组成的自主系统中的集中式并行计算。