一种基于深度强化学习的无人小车双层路径规划方法

Bi-level Path Planning Method for Unmanned Vehicle Based on Deep Reinforcement Learning

随着智能无人小车的广泛应用,智能化导航、路径规划和避障技术成为了重要的研究内容。文中提出了基于无模型的DDPG和SAC深度强化学习算法,利用环境信息循迹至目标点,躲避静态与动态的障碍物并且使其普适于不同环境。通过全局规划和局部避障相结合的方式,该方法以更好的全局性与鲁棒性解决路径规划问题,以更好的动态性与泛化性解决避障问题,并缩短了迭代时间;在网络训练阶段结合PID和A~*等传统算法,提高了所提方法的收敛速度和稳定性。最后,在机器人操作系统ROS和仿真程序gazebo中设计了导航和避障等多种实验场景,仿真实验结果验证了所提出的兼顾问题全局性和动态性的方法具有可靠性,生成的路径和时间效率有所优化。

With the wide application of intelligent unmanned vehicles,intelligent navigation,path planning and obstacle avoidance technology have become important research contents.This paper proposes model-free deep reinforcement learning algorithms DDPG and SAC,which use environmental information to track to the target point,avoid static and dynamic obstacles,and can be generally suitable for different environments.Through the combination of global planning and local obstacle avoidance,it solves the path planning problem with better globality and robustness,solves the obstacle avoidance problem with better dynamicity and generalization,and shortens the iteration time.In the network training stage,PID,Aand other traditional algorithms are combined to improve the convergence speed and stability of the method.Finally,a variety of experimental scenarios such as navigation and obstacle avoidance are designed in the robot operating system ROS and the simulation program gazebo.Simulation results verify the reliability of the proposed approach,which takes the global and dynamic nature of the problem into account and optimizes the generated paths and time efficiency.