This article introduces multi-agent strategy enabling multiple agents to explore an unknown environment with many obstacles, while generating a topological map in a cooperative way. Once a topological map is built, it can be used for various purposes, such as path planning or intruder capture. Multiple agents generate a Voronoi graph as a topological map of the environment, while dropping indexed markers at Voronoi vertices. Each agent has range sensors to detect nearby obstacles, thus can move along a Voronoi edge. Also, each agent stores the boundary for the explored region thus far, and unite its boundary with the boundary of another agent if some conditions are met. In this way, multiple agents can explore the entire workspace in a time efficient manner. The proposed exploration strategy doesn’t require localization of an agent or a marker in global coordinate systems. To the best of our knowledge, this article is unique in addressing a multi-agent exploration and map building strategy, such that each agent drops indexed markers for generating a topological map of the environment. The effectiveness of the proposed exploration and mapping strategy is demonstrated utilizing MATLAB simulations.

本文介绍了多智能体策略，使多个智能体能够探索具有许多障碍的未知环境，同时以协作方式生成拓扑图。一旦构建了拓扑图，它就可以用于各种目的，例如路径规划或入侵者捕获。多个代理生成 Voronoi 图作为环境的拓扑图，同时在 Voronoi 顶点处删除索引标记。每个代理都有距离传感器来检测附近的障碍物，因此可以沿着 Voronoi 边缘移动。此外，每个代理存储迄今为止探索区域的边界，如果满足某些条件，则将其边界与另一个代理的边界合并。通过这种方式，多个代理可以以高效的方式探索整个工作空间。提议的探索策略不需要在全局坐标系中定位代理或标记。据我们所知，本文在解决多代理探索和地图构建策略方面是独一无二的，这样每个代理都会丢弃索引标记以生成环境的拓扑图。利用 MATLAB 仿真证明了所提出的探索和映射策略的有效性。