Smooth Path Planning of 6-DOF Robot Based on Reinforcement Learning

The current path planning algorithms such as A-star(all stars) algorithm and RRT (Rapidly-exploring Random Trees) algorithm can meet the obstacle avoidance planning of the 6-DOF robot, but the smoothness of the path is not considered. Working in an unreasonable path for a long time will produce a gr...

Full description

Saved in:
Bibliographic Details
Published in2022 4th International Conference on Control and Robotics (ICCR) pp. 89 - 93
Main Authors Tian, Jiawei, Li, Dazi
Format Conference Proceeding
LanguageEnglish
Published IEEE 02.12.2022
Subjects
6-DOF
6-DOF robot
A-star
Path planning
Production
Reinforcement learning
RRT
Solids
Stars
Transforms
Online AccessGet full text
DOI10.1109/ICCR55715.2022.10053875

Cover

More Information
Summary:The current path planning algorithms such as A-star(all stars) algorithm and RRT (Rapidly-exploring Random Trees) algorithm can meet the obstacle avoidance planning of the 6-DOF robot, but the smoothness of the path is not considered. Working in an unreasonable path for a long time will produce a great load on the joints of the 6-DOF robot and seriously affect its life. In this paper, we use reinforcement learning reconcile A-star algorithm and RRT algorithm for smooth path planning of the robot. Experimental results show that compared with A-star algorithm and RRT algorithm, the fusion algorithm has smoother path and more reasonable time.
DOI:10.1109/ICCR55715.2022.10053875