Research on industrial robot path planning based on improved RRT algorithm

Fang Lu

doi:10.1117/12.3049817

27 September 2024 Research on industrial robot path planning based on improved RRT algorithm

Fang Lu

Proceedings Volume 13284, Third International Conference on Intelligent Mechanical and Human-Computer Interaction Technology (IHCIT 2024); 132841A (2024) https://doi.org/10.1117/12.3049817
Event: Third International Conference on Intelligent Mechanical and Human-Computer Interaction Technology (IHCIT 2024), 2024, Hangzhou, China

Abstract

Path planning is a supporting technology in the field of industrial robot research. Appropriate path planning algorithms can ensure the safety of industrial robots at work and improve their work efficiency. This paper proposes an SRRT path planning algorithm based on Sarsa reinforcement learning, which retains the high efficiency of RRT node expansion and uses the feedback value of the expanded node as the evaluation criterion for the expansion process for subsequent learning processes. By improving the greedy strategy and reward function, the random tree follows the idea of "exploring first and then using", selects valid nodes with high feedback values as much as possible, reduces the amount of calculation in the subsequent learning process, improves the utilization rate of valid nodes, and expands the applicable environment of the algorithm.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Fang Lu "Research on industrial robot path planning based on improved RRT algorithm", Proc. SPIE 13284, Third International Conference on Intelligent Mechanical and Human-Computer Interaction Technology (IHCIT 2024), 132841A (27 September 2024); https://doi.org/10.1117/12.3049817

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available