Tools
ToolsDong, Gui-rong, Zhang, Fu-qiang, Hou, Pi-hong, Han, Zhi-xing, Liu, Dian-zi and Zhou, Shi-sheng (2022) Inverse Kinematics Obstacle Avoidance Solution for Industrial Robot Based on Quaternion - 基于四元数的工业机器人逆运动学避障求解. Digital Printing, 3. pp. 49-56. ISSN 2095-9540
Preview |
PDF (Inverse Kinematics Obstacle Avoidance Solution)
- Published Version
Download (2MB) | Preview |
Abstract
With the development of artificial intelligence technology, industrial robots are widely used in work scenarios such as gripping and handling. However, due to the complex inverse kinematics solution and the existence of multiple solutions for poses, the robot has poor robustness and its industrial application range is limited. To simplify the solving process of inverse kinematics of industrial robot and realize the accurate control of robot pose in complex obstacle scene, quaternion was used to solve the robot pose, and an improved particle swarm optimization algorithm (F-PSO) was proposed combined with obstacle avoidance module in this paper. Through the comparative experimental analysis with the simulated annealing algorithm (SA) and the genetic algorithm (GA) under different target poses, it was proved that the F-PSO algorithm performed better, and the convergence accuracy was more than 36.41% higher than that of the traditional algorithm. The F-PSO algorithm was more than 83.82% faster than the traditional algorithm. The experimental results showed that the F-PSO algorithm proposed in this paper can not only precisely control the pose of the robot, but also effectively improve the work efficiency and realize the optimization of the robot gripping process in the complex obstacle scene.
| Item Type: | Article |
|---|---|
| Faculty \ School: | Faculty of Science > School of Engineering (former - to 2024) |
| UEA Research Groups: | Faculty of Science > Research Groups > Sustainable Energy Faculty of Science > Research Groups > Materials, Manufacturing & Process Modelling |
| Depositing User: | LivePure Connector |
| Date Deposited: | 01 Nov 2022 14:31 |
| Last Modified: | 19 Nov 2024 01:28 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/89458 |
| DOI: | 10.19370/j.cnki.cn10-1304/ts.2022.03.006 |
Downloads
Downloads per month over past year
Actions (login required)
 |
View Item |