基于强化学习的机器人手臂仿人运动规划方法  

Humanoid motion planning of robotic arm based on reinforcement learning

作　　者：杨傲雷[1,2] 陈燕玲 徐昱琳 Yang Aolei;Chen Yanling;Xu Yulin(School of Mechatronic Engineering and Automation,Shanghai University,Shanghai 200444,China;Shanghai Key Laboratory of Power Station Automation Technology,Shanghai 200444,China)

机构地区：[1]上海大学机电工程与自动化学院,上海200444 [2]上海市电站自动化技术重点实验室,上海200444

出　　处：《仪器仪表学报》2021年第12期136-145,共10页Chinese Journal of Scientific Instrument

基　　金：国家自然科学基金(61873158);上海市自然科学基金(18ZR1415100)项目资助。

摘　　要：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。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.

关 键 词：人臂运动特性 仿人运动规划 强化学习 运动捕捉系统 

分 类 号：TP391[自动化与计算机技术—计算机应用技术] TH86[自动化与计算机技术—计算机科学与技术]