Abstract
Disaster areas involving floods and earthquakes are commonly large, with the rescue time being quite tight, suggesting multi-Unmanned Aerial Vehicles (UAV) exploration rather than employing a single UAV. For such scenarios, current UAV exploration is modeled as a Coverage Path Planning (CPP) problem to achieve full area coverage in the presence of obstacles. However, the UAV's endurance capability is limited, and the rescue time is constrained, prohibiting even multiple UAVs from completing disaster area coverage on time. Therefore, this paper defines a multi-Agent Endurance-limited CPP (MAEl-CPP) problem that is based on an a priori known heatmap of the disaster area, which affords to explore the most valuable areas under UAV limited energy constraints. 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 sensing aerial images and completing path planning according to this ranking. Experimental results demonstrate that the search efficiency of the proposed algorithm is 4.2 times that of the existing algorithm.
Published Version
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