A novel trajectories optimizing method for dynamic soaring based on deep reinforcement learning  

作　　者：Wanyong Zou Ni Li Fengcheng An Kaibo Wang Changyin Dong 

机构地区：[1]School of Aeronautics,Northwestern Polytechnical University,Xi'an 710072,China [2]National Key Laboratory of Aircraft Configuration Design,Xi'an 710072,China

出　　处：《Defence Technology(防务技术)》2025年第4期99-108,共10页Defence Technology

基　　金：support received by the National Natural Science Foundation of China(Grant Nos.52372398&62003272).

摘　　要：Dynamic soaring,inspired by the wind-riding flight of birds such as albatrosses,is a biomimetic technique which leverages wind fields to enhance the endurance of unmanned aerial vehicles(UAVs).Achieving a precise soaring trajectory is crucial for maximizing energy efficiency during flight.Existing nonlinear programming methods are heavily dependent on the choice of initial values which is hard to determine.Therefore,this paper introduces a deep reinforcement learning method based on a differentially flat model for dynamic soaring trajectory planning and optimization.Initially,the gliding trajectory is parameterized using Fourier basis functions,achieving a flexible trajectory representation with a minimal number of hyperparameters.Subsequently,the trajectory optimization problem is formulated as a dynamic interactive process of Markov decision-making.The hyperparameters of the trajectory are optimized using the Proximal Policy Optimization(PPO2)algorithm from deep reinforcement learning(DRL),reducing the strong reliance on initial value settings in the optimization process.Finally,a comparison between the proposed method and the nonlinear programming method reveals that the trajectory generated by the proposed approach is smoother while meeting the same performance requirements.Specifically,the proposed method achieves a 34%reduction in maximum thrust,a 39.4%decrease in maximum thrust difference,and a 33%reduction in maximum airspeed difference.

关 键 词：Dynamic soaring Differential flatness Trajectory optimization Proximal policy optimization 

分 类 号：V279[航空宇航科学与技术—飞行器设计]