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Path planning for spot welding robots based on improved ant colony algorithm

Published online by Cambridge University Press:  15 August 2022

Yuesheng Tan ,
Jie Ouyang Open the ORCID record for Jie Ouyang [Opens in a new window] ,
Zhuo Zhang ,
Yinglun Lao  and
Pengju Wen
Yuesheng Tan*
Affiliation:
School of Technology, Beijing Forestry University, Beijing 100083, China
Jie Ouyang
Affiliation:
School of Technology, Beijing Forestry University, Beijing 100083, China
Zhuo Zhang
Affiliation:
School of Technology, Beijing Forestry University, Beijing 100083, China
Yinglun Lao
Affiliation:
School of Technology, Beijing Forestry University, Beijing 100083, China
Pengju Wen
Affiliation:
School of Technology, Beijing Forestry University, Beijing 100083, China
*
*Corresponding author. E-mail: tanyuesheng@163.com
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Abstract

A welding path can be planned effectively for spot welding robots using the ant colony algorithm, but the initial parameters of the ant colony algorithm are usually selected through human experience, resulting in an unreasonable planned path. This paper combines the ant colony algorithm with the particle swarm algorithm and uses the particle swarm algorithm to train the initial parameters of the ant colony algorithm to plan an optimal path. Firstly, a mathematical model for spot welding path planning is established using the ant colony algorithm. Then, the particle swarm algorithm is introduced into the ant colony algorithm to find the optimal combination of parameters by treating the initial parameters $\alpha$ and $\beta$ of the ant colony algorithm and as two-dimensional coordinates in the particle swarm algorithm. Finally, the simulation analysis was carried out using MATLAB to obtain the paths of the improved ant colony algorithm for six different sets of parameters with an average path length of 10,357.7509 mm, but the average path length obtained by conventional algorithm was 10,830.8394 mm. Convergence analysis of the improved ant colony algorithm showed that the average number of iterations was 17. Therefore, the improved ant colony algorithm has higher solution quality and converges faster.

Keywords

path planningant colony algorithmparticle swarm algorithmspot welding robot
Type
Research Article
Information
Robotica , Volume 41 , Issue 3 , March 2023 , pp. 926 - 938
Copyright
© The Author(s), 2022. Published by Cambridge University Press

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