Multi-UAV WRSN charging path planning based on improved heed and IA-DRL

Abstract

Aiming at the problem of how to improve the energy utilization rate of UAV (Unmanned Aerial Vehicle) in the charging process of wireless rechargeable sensor network, a charging path planning scheme for multi-UAV wireless rechargeable sensor network based on deep reinforcement learning is proposed. Firstly, the multi-UAV path planning problem is described and a network model is established, and then the network model is optimized by using the improved dynamic clustering algorithm of HEED, and then an intelligent path optimization algorithm based on the intelligent algorithm and deep reinforcement learning IA-DRL is proposed for the problem model. According to this algorithm, the optimal charging path of multiple drones is obtained, and finally the drones charge each node to be charged in the network. The experimental results show, compared with other traditional heuristic methods, IA-DRL has more advantages in solving small and medium-scale UAV path planning problems, and compared with the neural network model without PSO, the minimum AVG performance of IA-DRL is improved by about 3.8% and has a faster convergence speed about 550 episodes.