Reinforcement Learning-Based Energy-Saving Path Planning for UAVs in Turbulent Wind
Shaonan Chen, Yuhong Mo, Xiaorui Wu, Jing Xiao, Quan Liu
Abstract
The unmanned aerial vehicle (UAV) is prevalent in power inspection. However, due to a limited battery life, turbulent wind, and its motion, it brings some challenges. To address these problems, a reinforcement learning-based energy-saving path-planning algorithm (ESPP-RL) in a turbulent wind environment is proposed. The algorithm dynamically adjusts flight strategies for UAVs based on reinforcement learning to find the most energy-saving flight paths. Thus, the UAV can navigate and overcome real-world constraints in order to save energy. Firstly, an observation processing module is designed to combine battery energy consumption prediction with multi-target path planning. Then, the multi-target path-planning problem is decomposed into iterative, dynamically optimized single-target subproblems, which aim to derive the optimal discrete path solution for energy consumption prediction. Additionally, an adaptive path-planning reward function based on reinforcement learning is designed. Finally, a simulation scenario for a quadcopter UAV is set up in a 3-D turbulent wind environment. Several simulations show that the proposed algorithm can effectively resist the disturbance of turbulent wind and improve convergence.