We present algorithms for uniformly covering an unknown indoor region with a swarm of simple, anonymous and autonomous mobile agents. The exploration of such regions is made difficult by the lack of a common global reference frame, severe degradation of radio-frequency communication, and numerous ground obstacles. We propose addressing these challenges by using airborne agents, such as Micro Air Vehicles, in dual capacity, both as mobile explorers and (once they land) as beacons that help other agents navigate the region. The algorithms we propose are designed for a swarm of simple, identical, ant-like agents with local sensing capabilities. The agents enter the region, which is discretized as a graph, over time from one or more entry points and are tasked with occupying all of its vertices. Unlike many works in this area, we consider the requirement of informing an outside operator with limited information that the coverage mission is complete. Even with this additional requirement we show, both through simulations and mathematical proofs, that the dual role concept results in linear-time termination, while also besting many well-known algorithms in the literature in terms of energy use.

翻译：我们提出了一种使用简单、匿名且自主移动的智能体群体均匀覆盖未知室内区域的算法。由于缺乏共同的全局参考框架、射频通信严重衰减以及大量地面障碍物，此类区域的探索面临诸多挑战。我们建议通过使用空中智能体（如微型飞行器）的双重角色来应对这些挑战：既作为移动探索者，又（在着陆后）作为信标帮助其他智能体导航区域。我们提出的算法专为具备本地感知能力的简单、相同、蚂蚁类智能体群体设计。这些智能体随时间推移从一个或多个入口点进入被离散化为图的区域，并负责占据其所有顶点。与许多相关研究不同，我们考虑了向外部操作员发送有限信息以告知覆盖任务完成的这一需求。即使增加了这一要求，我们通过仿真和数学证明表明，这种双重角色概念不仅实现了线性时间终止，还在能耗方面优于文献中许多著名算法。