基于强化学习的机械臂运动规划算法综述OA

With the rapid development of intelligent manufacturing and the rapid growing demand for autonomous robotic operation,traditional manipulator motion planning algorithms exhibit limitations in handling high-dimensional spaces,unstructured environments and complex manipulation tasks.Reinforcement learning,with its ability to autonomously learn through trial-and-error interactions with the environment,has made remarkable progress in the field of robotic arm motion planning in recent years,providing a novel solution to the above problems.Reinforcement learning-based motion planning algorithms for robotic arms is systematically reviewed,it outlines the basic theories and classification of reinforcement learning algorithms,and focuses on analyzing the fundamental principles of four typical reinforcement learning algorithms including proximal policy optimization(PPO),deep deterministic policy gradient(DDPG),twin delayed deep deterministic policy gradient(TD3),and soft actor-critic(SAC)as well as their applications in manipulator motion planning.A comparative analysis of these four algorithms is conducted,summarizing their advantages and limitations.The main challenges currently faced are discussed from the perspectives of reward function,sample efficiency,sim-to-real transfer,and model generalization ability,and potential future research directions are prospected accordingly.