机器人2025，Vol.47Issue(3)：383-393,11.DOI:10.13973/j.cnki.robot.240341
复杂环境下基于TCP-DQN算法的低空飞行器动态航路规划
Dynamic Path Planning of Low-altitude Aircraft Based on TCP-DQN Algorithm in Complex Environment
摘要
Abstract
To address the issues of inefficient training,slow convergence,and poor path feasibility encountered by deep reinforcement learning algorithms in solving dynamic path planning for low-altitude aircraft,a TCP-DQN(target-guided curriculum learning and prioritized replay deep Q-network)based dynamic path planning algorithm is proposed.Firstly,a curriculum learning mechanism is introduced into the framework of reinforcement learning algorithms,where target-guided maneuver strategies are set to improve the training speed of the algorithm while optimizing the feasibility of the planned paths.Secondly,a combined reward function for training is constructed to resolve the sparsity problem of DQN reward values,and obstacle avoidance experiences of low-altitude aircraft are prioritized for replay to enhance the learning performance of the algorithm.Finally,simulation results of the TCP-DQN algorithm for path planning in 3D low-altitude dynamic environment are presented.The simulation results demonstrate that the algorithm can quickly plan the safe and efficient paths for low-altitude aircraft in dynamic and unknown threat environments.关键词
低空飞行器/深度强化学习/动态航路规划/DQN算法Key words
low-altitude aircraft/deep reinforcement learning/dynamic path planning/DQN(deep Q-network)algorithm引用本文复制引用
许振阳,陈谋,韩增亮,邵书义..复杂环境下基于TCP-DQN算法的低空飞行器动态航路规划[J].机器人,2025,47(3):383-393,11.基金项目
国家重点研发计划(2023YFB4704400) （2023YFB4704400）
中国博士后科学基金(GZC20242230) （GZC20242230）
国防科工局稳定支持项目(HTKJ2024KL502029,HTKJ2023KL502002). （HTKJ2024KL502029,HTKJ2023KL502002）
