Motion Planning for Dual-Arm Robot Based on Soft Actor-Critic
學年 109
學期 2
出版（發表）日期 2021-02-03
作品名稱 Motion Planning for Dual-Arm Robot Based on Soft Actor-Critic
作品名稱（其他語言）
著者 Ching-Chang Wong; Shao-Yu Chien; Hsuan-Ming Feng; Hisasuki Aoyama
單位
出版者
著錄名稱、卷期、頁數 IEEE Access 9, p.26871-26885
摘要 In this paper, a motion planning method based on the Soft Actor-Critic (SAC) is designed for a dual-arm robot with two 7-Degree-of-Freedom (7-DOF) arms so that the robot can effectively avoid self-collision and at the same time can avoid the joint limits and singularities of the arm. The left-arm and right-arm of the dual-arm robot each have a neural network to control its position and orientation. Dual-agent training, distributed training structure, and progressive training environment are used to train neural networks. During the training process, the motion of one arm is regarded as the environment of the other arm, and the two agents are trained at the same time. In the input part of the neural network of the proposed method, all parameters come from the angle of each axis and kinematic calculation, no additional sensors are needed, so the method is easier to transplant to different dual-arm robots. With some appropriate neural network inputs and reward functions design, the robot can perform the expected self-collision avoidance and effectively avoid the joint limits and singularities of the arm. Finally, some experiments of the simulation tests in the Gazebo simulator and actual tests in a laboratory-made dual-arm robot are presented to illustrate the proposed SAC-based motion planning method is feasible and practicable in the avoidance of self-collision, joint limits, and singularities.
關鍵字 Dual-arm robot;soft actor-critic (SAC);deep reinforcement learning (DRL);motion planning;self-collision avoidance
語言 en_US
ISSN 2169-3536
期刊性質 國外
收錄於 SCI Scopus
產學合作
通訊作者
審稿制度 否
國別 USA
公開徵稿
出版型式 ,電子版
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機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/120904 )

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