Several distributed algorithms are presented for the exploration of unknown indoor regions by a swarm of flying, energy constrained agents. The agents, which are identical, autonomous, anonymous and oblivious, uniformly cover the region and thus explore it using predefined action rules based on locally sensed information and the energy level of the agents. While flying drones have many advantages in search and rescue scenarios, their main drawback is a high power consumption during flight combined with limited, on-board energy. Furthermore, in these scenarios agent size is severely limited and consequently so are the total weight and capabilities of the agents. The region is modeled as a connected sub-set of a regular grid composed of square cells that the agents enter, over time, via entry points. Some of the agents may settle in unoccupied cells as the exploration progresses. Settled agents conserve energy and become virtual pheromones for the exploration and coverage process, beacons that subsequently aid the remaining, and still exploring, mobile agents. The termination of the coverage process is based on a backward propagating information diffusion scheme. Various algorithmical alternatives are discussed and upper bounds derived and compared to experimental results. Finally, an optimal entry rate that minimizes the total energy consumption is derived for the case of a linear regions.

翻译：本文提出了几种分布式算法，用于由飞行且受能量约束的智能体组成的集群对未知室内区域进行探索。这些智能体相同、自主、匿名且无记忆，它们基于局部感知信息和自身能量水平，利用预定义的动作规则均匀覆盖并探索该区域。尽管飞行无人机在搜救场景中具有诸多优势，但其主要缺点在于飞行过程中功耗高，且机载能量有限。此外，在此类场景中，智能体尺寸受到严格限制，因此其总重量和能力也随之受限。该区域被建模为一个由正方形单元格组成的规则网格的连通子集，智能体随时间通过入口点进入这些单元格。随着探索的进行，部分智能体可能驻留在未占用的单元格中。驻留的智能体可节省能量，并成为探索与覆盖过程中的虚拟信息素，即信标，这些信标随后将帮助其余仍在探索的移动智能体。覆盖过程的终止基于一种后向传播的信息扩散方案。文中讨论了多种算法变体，推导了上界，并将其与实验结果进行了比较。最后，针对线性区域的情况，推导出了使总能耗最小化的最优进入速率。