信息与控制2023，Vol.52Issue(6)：736-746,772,12.DOI:10.13976/j.cnki.xk.2022.0447
基于改进深度强化学习的无人机自主导航方法
Autonomous Navigation Algorithm of UAV Based on Improved Deep-reinforcement-learning
摘要
Abstract
The deep reinforcement learning algorithm is widely used in UAV navigation tasks.However,in the training process using the fusion prior strategy,the model training speed is slow,and the success rate of navigation decreases due to the linear attenuation of its proportion.First,we estab-lish a virtual UAV environment model and construct the action space based on UAV autonomous navigation.Next,we design the reward function built on the nonsparsity idea.Coupled with the self-adaptive attenuation factor based on state,the weight of prior policy under the different states is ameliorated.Finally,we realize the autonomous navigation decision-making of UAVs using the trained network model.Simulation results manifest that the training time when the navigation suc-cess rate is stable at a high level is reduced by 20％from the prototype algorithm,indicating that we increase the training efficiency and cut down the time cost.In addition,the navigational quality and success rate are slightly enhanced.The proposed algorithm provides a new idea to facilitate the practical use of deep reinforcement learning in UAV autonomous navigation.关键词
深度强化学习/无人机导航/先验策略/自适应衰减Key words
deep-reinforcement-learning/UAV navigation/prior policy/self-adaptive attenuation分类
航空航天引用本文复制引用
郭子恒,蔡晨晓..基于改进深度强化学习的无人机自主导航方法[J].信息与控制,2023,52(6):736-746,772,12.基金项目
国家自然科学基金(61973164) （61973164）
