东华大学学报（英文版）2025，Vol.42Issue(6)：639-649,11.DOI:10.19884/j.1672-5220.202407004
融合RRT∗与TD3深度强化学习的无人机三维部分未知环境路径规划算法
A Hybrid of RRT∗ and TD3 Deep Reinforcement Learning Algorithm for UAV Path Planning in 3D Partially Unknown Environments
摘要
Abstract
To guide an unmanned aerial vehicle(UAV)flying in complex three-dimensional(3D)environments with unknown obstacles,a novel UAV path planning algorithm named IRRT∗-C2TD3 is proposed.The algorithm combines the rapidly-exploring random tree star(RRT∗)algorithm with the twin delayed deep deterministic policy gradients(TD3)algorithm(a deep reinforcement learning algorithm).By employing exploration strategies from reinforcement learning,IRRT∗-C2TD3 improves the RRT∗ algorithm.IRRT∗-C2TD3 is a two-stage path planning algorithm comprising pre-planning and real-time planning.It performs pre-planning of paths by generating paths based on geometric connections toward the goal and smoothing them using cubic B-spline curves.By designing the network architecture and reward function of the TD3 algorithm,real-time planning in unknown environments is achieved based on the pre-planned path from the first stage.Simulation results show that IRRT∗-C2TD3 demonstrates better path planning performance in 3D partially unknown environments than RRT∗-C2TD3,M-C2TD3 and MOD-RRT∗ algorithms.关键词
三维路径规划/深度强化学习/快速探索随机树/无人机Key words
3D path planning/deep reinforcement learning/rapidly-exploring random tree(RRT)/UAV分类
信息技术与安全科学引用本文复制引用
何彦熹,齐洁,吴乃龙..融合RRT∗与TD3深度强化学习的无人机三维部分未知环境路径规划算法[J].东华大学学报（英文版）,2025,42(6):639-649,11.基金项目
National Natural Science Foundation of China(No.62173084) （No.62173084）
Foundation of Shanghai Committee of Science and Technology,China(Nos.23ZR1401800 and 22JC1401403) （Nos.23ZR1401800 and 22JC1401403）
