基于强化学习的机器人手臂仿人运动规划方法

面向人-机器人交互共融环境对机械臂仿人运动规划的重大需求,本文提出了一种基于强化学习的机器人手臂仿人运 动规划方法。 首先,基于人体手臂的结构特征,设计了体现机械臂运动特性的肩夹角、肘夹角和腕关节运动角,并采用正态性和 相关性分析方法,对 VICON 运动捕捉系统获取的人体手臂运动数据进行分析,以获取人臂运动特性规则。 然后,根据不同的运 动特性规则,设计对应的回报函数,并采用强化学习方法进行机械臂仿人运动模型的训练。 最后,搭建机械臂仿人运动平台,实 验统计仿人运动的成功率为 91. 25% ,验证了所提规划方法的可行性和有效性,可用于提高机械臂运动的仿人性。

Humanoid motion planning of robotic arm based on reinforcement learning

To meet the requirement of humanoid motion planning of the robotic arm in human-robot interaction environment, a humanoid motion planning method of the robotic arm based on reinforcement learning is proposed in this article. Firstly, based on the structural characteristics of the human arm, the shoulder angle, the elbow angle and the wrist joint motion angle are designed to reflect motion characteristics of the robotic arm. The motion data of human arm captured by the VICON system are analyzed by using normality and correlation analysis methods to achieve the motion characteristics of the human arm. Then, according to different motion characteristics rules, the corresponding reward functions are designed, and the humanoid motion model is trained by the reinforcement learning method. Finally, the humanoid motion platform of the robot arm is established, and the success rate of the humanoid motion is 91. 25% . It evaluates the feasibility and effectiveness of the proposed method, which could be used to improve the humanization of robot motion.