Multi-UAV Coverage Planning with Limited Endurance in Disaster Environment
This addresses the urgent need for faster and more efficient disaster response using UAVs, though it is incremental as it builds on existing coverage path planning methods.
The paper tackles the problem of efficiently covering large disaster areas with multiple UAVs that have limited endurance, by prioritizing more valuable areas based on a priori heatmap. Experimental results show the proposed algorithm is at least twice as effective as existing methods in search efficiency.
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.