Abstract With the advantages of flexibility and mobility, unmanned aerial vehicles (UAVs) have been widely used in the wireless rechargeable sensor networks (WRSNs) to collect data and supply energy for ground sensor nodes. Due to the limited battery capacity of UAVs and the continuity requirement of WRSN, mobile unmanned vehicles (MUVs) are introduced as mobile charging stations to ensure the energy supply for UAVs and mitigate energy wastage. This paper investigates the problem of Joint Optimization Mission Allocation and Cooperative Trajectory Planning for data collection in WRSNs. The goal is to maximize the minimum energy efficiency by optimizing mission allocation including UAV trajectory and MUV travel. This problem is proved to be NP-hard and solved by two proposed algorithms. The first algorithm incorporates the clustering utilizing the K-Means algorithm and genetic algorithm. The second algorithm is a self-attention architecture based on the reinforcement learning framework and formulate an actor-critic algorithm for training. The simulation results show the feasibility and efficiency of the proposed algorithms, which achieve better performance. The first algorithm has more advantages when the distribution of sensor nodes is relatively concentrated; and the second algorithm may be more suitable when more comprehensive global path planning optimization is required.
Lu et al. (Mon,) studied this question.