Humanoid motion planning of robotic arm based on human arm action feature and reinforcement learning

Mechatronics ◽  
2021 ◽  
Vol 78 ◽  
pp. 102630
Author(s):  
Aolei Yang ◽  
Yanling Chen ◽  
Wasif Naeem ◽  
Minrui Fei ◽  
Ling Chen
Keyword(s):  
Reinforcement Learning ◽  
Motion Planning ◽  
Robotic Arm ◽  
Human Arm

Coordinated Motion Planning of Dual-arm Space Robot with Deep Reinforcement Learning

2019 ◽  
Author(s):  
Mengying Tang ◽  
Xiaofei Yue ◽  
Zhan Zuo ◽  
Xiaoping Huang ◽  
Yanfang Liu ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Motion Planning ◽  
Space Robot ◽  
Coordinated Motion ◽  
Dual Arm

Manipulator Motion Planning based on Actor-Critic Reinforcement Learning

2021 ◽  
Author(s):  
Qiang Li ◽  
Jun Nie ◽  
Haixia Wang ◽  
Xiao Lu ◽  
Shibin Song
Keyword(s):  
Reinforcement Learning ◽  
Motion Planning

Design Human-Robot Collaborative Lifting Task Using Optimization

2021 ◽  
Author(s):  
Asif Arefeen ◽  
Yujiang Xiang
Keyword(s):  
Degrees Of Freedom ◽  
Joint Angle ◽  
Joint Torque ◽  
Robotic Arm ◽  
Inverse Dynamic ◽  
Floating Base ◽  
Grasping Forces ◽  
Design Variables ◽  
Human Arm ◽  
Lifting Task

Abstract In this paper, an optimization-based dynamic modeling method is used for human-robot lifting motion prediction. The three-dimensional (3D) human arm model has 13 degrees of freedom (DOFs) and the 3D robotic arm (Sawyer robotic arm) has 10 DOFs. The human arm and robotic arm are built in Denavit-Hartenberg (DH) representation. In addition, the 3D box is modeled as a floating-base rigid body with 6 global DOFs. The interactions between human arm and box, and robot and box are modeled as a set of grasping forces which are treated as unknowns (design variables) in the optimization formulation. The inverse dynamic optimization is used to simulate the lifting motion where the summation of joint torque squares of human arm is minimized subjected to physical and task constraints. The design variables are control points of cubic B-splines of joint angle profiles of the human arm, robotic arm, and box, and the box grasping forces at each time point. A numerical example is simulated for huma-robot lifting with a 10 Kg box. The human and robotic arms’ joint angle, joint torque, and grasping force profiles are reported. These optimal outputs can be used as references to control the human-robot collaborative lifting task.

Sign in / Sign up

Export Citation Format

Share Document