Task-oriented trajectory planning and power optimization for multi-UAV area coverage

Abstract

This thesis proposes an intelligent scheduling system for task-oriented area coverage by multiple unmanned aerial vehicles (UAVs). This system enables multi-UAV to complete tasks more efficiently and energy-efficiently by optimizing their flight trajectory, channel resources, and transmission power. It consists of two main modules: flight energy optimization and transmission energy optimization. In the flight energy optimization module, target points are clustered and assigned to UAVs, and a travelling salesman problem (TSP) solver is used to plan the shortest paths for each UAV. In the transmission energy optimization module, the base stations with the best channel quality are assigned to UAVs, and the transmit power of UAVs is dynamically optimized by reinforcement learning. Simulation results demonstrate that the proposed method effectively reduces the total energy consumption while maintaining satisfactory image transmission quality and complete target point coverage. The observed energy-saving rate varies between 15% and 50%, depending on the scale and complexity of the task configuration.