Optimal path planning for unmanned aerial vehicles with multiple round-trip flights in coverage tasks

As high-tech equipment for rescue and relief, unmanned aerial vehicles (UAVs) are widely used in remote relief operations during disasters, significantly improving the efficiency of rescue efforts. However, one significant challenge of UAVs is the limitation of their onboard battery, which prohibits them from completing coverage tasks in a single journey, requiring multiple round-trip flights and frequent battery charging or replacement. As a result, it will greatly prolong the task time. To improve the efficiency of coverage tasks, we allocate task points reasonably to minimize the coverage rounds, and carry out path planning to optimize the travel time of each UAV. This study first formulates a path planning model with the optimization objective of minimizing the overall task time. Then, a task allocation strategy is designed based on the priority of task points, including a max-weight allocation scheme for special scenarios with absolute priority rules and a min-delay allocation scheme for general scenarios with relative priority rules. To optimize the paths of UAVs, we further develop an improved beetle antennae search algorithm based on mutation operations (MBAS). The performance of the developed integrated methods is finally tested through simulation, yielding good results. Source code of the algorithm can be found at https://github.com/lijing0966/MBAS.git.