论文标题
在灾难环境中耐力有限的多动能范围覆盖计划
Multi-UAV Coverage Planning with Limited Endurance in Disaster Environment
论文作者
论文摘要
对于洪水和地震等场景，灾区很大，救援时间很紧。多UAV探索比单个无人机更有效。现有的无人机勘探工作被建模为覆盖路径计划（CPP）任务，以在存在障碍的情况下完全覆盖该地区。但是，无人机的耐力能力是有限的，救援时间很紧急。因此，即使使用多个无人机也无法及时获得完整的灾区覆盖范围。因此，在本文中，我们提出了一个基于灾区的先验热图的多代理耐力有限的CPP（Mael-CPP）问题，该问题需要在有限的能源下探索更有价值的区域。此外，我们通过根据卫星或远程空中图像对可能的灾害区域对可能的灾害区域进行对可能的灾难区域进行对路径计划算法，并根据重要性水平来完成路径计划。实验结果表明，在搜索效率方面，我们提出的算法的有效性至少是现有方法的两倍。
For scenes such as floods and earthquakes, the disaster area is large, and rescue time is tight. Multi-UAV exploration is more efficient than a single UAV. Existing UAV exploration work is modeled as a Coverage Path Planning (CPP) task to achieve full coverage of the area in the presence of obstacles. However, the endurance capability of UAV is limited, and the rescue time is urgent. Thus, even using multiple UAVs cannot achieve complete disaster area coverage in time. Therefore, in this paper we propose a multi-Agent Endurance-limited CPP (MAEl-CPP) problem based on a priori heatmap of the disaster area, which requires the exploration of more valuable areas under limited energy. Furthermore, we propose a path planning algorithm for the MAEl-CPP problem, by ranking the possible disaster areas according to their importance through satellite or remote aerial images and completing path planning according to the importance level. Experimental results show that our proposed algorithm is at least twice as effective as the existing method in terms of search efficiency.
