公开(公告)号：US20240354657A1

公开(公告)日：2024-10-24

申请号：US18739779

申请日：2024-06-11

Applicant: ZHENGZHOU UNIVERSITY OF LIGHT INDUSTRY

Inventor： Hao LI ,  Gen LIU ,  Xingyou HE ,  Haoqi WANG ,  Linli LI ,  Xiaoyu WEN ,  Shizhong WEI ,  Yuyan ZHANG ,  Weifei GUO ,  Wenchao YANG

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: A method for digital twin virtual-reality synchronization mapping of a mechanical arm comprises acquiring a virtual mechanical arm model built based on an actual mechanical arm in a virtual environment, where the virtual mechanical arm model is configured to map the actual mechanical arm; acquiring real motion information collected when the actual mechanical arm moves; constructing a training set and a test set based on the real motion information; training a target multilayer perceptron model based on the training set, and testing the target multilayer perceptron model based on the test set, where the target multilayer perceptron model is configured to predict a motion of the virtual mechanical arm model in the virtual environment; and deploying the target multilayer perceptron model on the actual mechanical arm when the target multilayer perceptron model meets a preset condition. According to this method, operation accuracy and efficiency of the mechanical arm are improved.

公开(公告)号：US20240351199A1

公开(公告)日：2024-10-24

申请号：US18739773

申请日：2024-06-11

Applicant: ZHENGZHOU UNIVERSITY OF LIGHT INDUSTRY

Inventor： Hao LI ,  Gen LIU ,  Yonglei WU ,  Haoqi WANG ,  Linli LI ,  Xiaoyu WEN ,  Shizhong WEI ,  Yuyan ZHANG ,  Like ZHANG ,  Weifei GUO

IPC: B25J9/16

CPC classification number: B25J9/163 ,  B25J9/1661

Abstract: A method for intelligently controlling a mechanical arm includes building a twin model of a mechanical arm, and extracting a state parameter and an action parameter corresponding to task characteristics from the twin model; determining a reward function corresponding to the task characteristics; training a twin delayed deep deterministic policy gradient (TD3) reinforcement learning model; simulating in the twin model based on a physical state parameter of the mechanical arm by using the TD3 reinforcement learning model, to obtain a controllable parameter; and controlling the mechanical arm to execute a corresponding task by using the controllable parameter. The TD3 reinforcement learning model is built based on the state parameter and the action parameter corresponding to the task characteristics and the reward function corresponding to the task characteristics, which can adapt to a dynamically changing environment and requirements for multiple tasks.