Content area

Abstract

Facing the problems of large-scale rapid and disorderly loading, the robotic arm has the problems of large start-stop impact, easy to shake, and reduced production efficiency and service life, this paper proposes a robotic arm motion planning method based on the improved multi-objective algorithm called LNSGA-II. Firstly, the artificial potential field method is used to plan the shortest path without collision, extract the key motion sequences, and establish the multi-objective function to improve the operating efficiency of the robotic arm, the smoothness of the motion trajectory, and the reduction of energy consumption. Then to solve the nonlinear constraints in the multi-objective trajectory planning, the infeasibility degree is designed, and the NSGA-II is improved by using the mutation chaos strategy and the dynamic goal-oriented development strategy. Numerical and trajectory planning experiments are conducted successively with the remaining five well-known multi-objective algorithms, and the experimental results demonstrate the superiority of LNSGA-II. Finally, the digital twin platform of MATLAB-CoppeliaSim-UR16e verifies the effectiveness of the method in real grasping tasks.

Details

1009240
Business indexing term
Location
Title
Advanced multi-objective trajectory planning for robotic arms using a multi-strategy enhanced NSGA-II algorithm
Author
Publication title
PLoS One; San Francisco
Volume
20
Issue
5
First page
e0324567
Publication year
2025
Publication date
May 2025
Section
Research Article
Publisher
Public Library of Science
Place of publication
San Francisco
Country of publication
United States
e-ISSN
19326203
Source type
Scholarly Journal
Language of publication
English
Document type
Journal Article
Publication history
 
 
Milestone dates
2025-01-07 (Received); 2025-04-25 (Accepted); 2025-05-29 (Published)
ProQuest document ID
3213835034
Document URL
https://www.proquest.com/scholarly-journals/advanced-multi-objective-trajectory-planning/docview/3213835034/se-2?accountid=208611
Copyright
© 2025 Fan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Last updated
2025-05-30
Database
ProQuest One Academic