A Balanced Shadow-Following Coverage Path Planning Approach under Energy Constraints
Huan Wang, Dianxi Shi, Yunlong Wu, Lin Li, Ning Li, Jiachi Xu · 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC) · 2021
Area coverage is one of the significant fundamental problems in mobile robotics. In this paper, we study the coverage path planning (CPP) problem of unmanned aerial vehicle(s) (UAVs) with limited energy. Most existing solutions for this problem adopted that the UAV returns to the charging station and replenishes energy, which ensures the complete coverage for the area of interest. However, to guarantee the integrity of the uncovered area, there is usually no gain to the cover task during the process of returning, which leads to poor efficiency. To address this problem, we propose a shadow-following (SF) strategy which performs the cover task when the UAV returns along the shadow path, improving the energy utilization and coverage efficiency. Then, we extend for multi-UAV scenarios and propose a load-balanced (LB) multi-UAV CPP algorithm, which minimizes the number of robots and assigns the balanced task to each UAV. Finally, we validate the results in experiments performed with a UAV and multiple UAVs. The results show that the SF and LB algorithm performs well in the coverage efficiency.