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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

Information

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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References

Zhuang, H. Z., Du, S. X. and Wu, T. J., “Research on path planning and related algorithms for robots,” Bull. Sci. Technol. 20(3), 6 (2004).Google Scholar
Zhu, J. and Yang, M. Y., “Path planning of manipulator to avoid obstacle based on improved artificial potential field method,” Comput. Meas. Cont. 26(10), 6 (2018).Google Scholar
Liang, K. and Mao, J. L., “Path planning of indoor mobile robot based on improved ant colony algorithm,” Electron. Meas. Technol. 42(11), (2019).Google Scholar
Lavalle, S. M., “Rapidly-exploring random trees: a new tool for path planning,” Computer Science Dept. 98(11), 1–4 (1998).Google Scholar
Zhu, X., Chen, R. W. and Xu, D. G., “Welding spot detection path planning method based on a novel particle swarm algorithm,” Chin J. Sci. Instrum. 35(11), 2482–2931 (2014).Google Scholar
Wurll, C. and Henrich, D., “Point-to-point and multi-goal path planning for industrial robots,” J. Intell. Robot Syst 18(8), 445–461 (2001).CrossRefGoogle Scholar
Pei, C. S., Jeng, S. P. and Shu, C. C., “A parallel compact cuckoo search algorithm for three-dimensional path planning,” Appl. Soft. Comput. 94, 106443 (2020).Google Scholar
Liu, L. S., Yao, J. X., D. W. He, J. Chen, J. Huang, H. Xu, B. Wang and J. F. Guo, “Global dynamic path planning fusion algorithm combining jump-A* algorithm and dynamic window approach,” IEEE Access 9, 32–38 (2021).Google Scholar
Ma, H. N., Zhang, Z. and Li, Q. L., “Improved artificial potential field method for dual-manipulator path planning algorithm,” J. Mech. Trans. 45(06), 77–84 (2021).Google Scholar
Zhang, Z., Li, X. Y., Dong, H. Z., L. Zhou and L. Gao, “Constrained sampling method based RRT algorithm for manipulator motion planning,” Comput. Integr. Manuf. 40(04), 1–18 (2022).Google Scholar
Xue, W. W., Yang, P. S., Rui, Y. and X. S. Gu, “Intelligent Welding Robot Path Planning,” In: Proceedings of the 2015 China Intelligent Automation Academic Conference (2nd fascicle) (2015), pp. 36–43.Google Scholar
Wang, J. H., Y and Wang, A study on BIW welding robot path planning based on ant colony algorithm,” Manufact. Automat. 30(5), 16–17 (2008).Google Scholar
Ling, J. G., Chen, S. X., H. C. Dai and W. J. Huang, “Application of ant colony algorithm in welding path planning of BIW,” Trans. China Weld. Inst. 36(01), 5–9 (2015).Google Scholar
Chen, G. and Liu, J., “Mobile robot path planning using ant colony algorithm and improved potential field method,” Comput. Intel. Neurosci. b(b), 1–10 (2019).Google Scholar
Zhang, Q., Chen, B. K., X. Y. Liu, X. Y. Liu and H. Yang, “Ant colony optimization with improved potential field heuristic for robot path planning,” Trans. Chin. Soc. Agric. Mach. 50(5), 11 (2019).Google Scholar
Xu, Y. Q., Luo, K., T., T. Li and W. G. Gao, “Path planning of mobile robot based on improved adaptive ant colony algorithm,” J. Electron. Meas. Instrument. 31(10), 531–533 (2019).Google Scholar
Jia, J. Z. and Li, X. J., “Research on evacuation path planning in single-story building fire based on genetic-ant colony algorithm,” J. Saf. Sci. Technol. 16(6) (2020).Google Scholar
Yang, J. F., “Ant Colony Algorithm and Its Application Research,” (Zhejiang University, Zhejiang, 2008) pp. 1–135.Google Scholar
Blum, C., “Ant colony optimization: introduction and recent trends,” Phys. Life. Rev 2(4), 353–373 (2005).CrossRefGoogle Scholar
Kazharov, A. A. and Kureichik, V. M., “Ant colony optimization algorithms for solving transportation problems,” J. Comput. Sys. Sc. Int 49(1), 30–43 (2010).CrossRefGoogle Scholar
Min, K. X., Ge, H. W., Zhang, Y. and Y. C. Liang, “Solving traveling salesman problem by an ACO and PSO based hybrid algorithm,” J. Jilin Univ. (Inform. Sci. Edn.) 13(4), 402–405 (2006).Google Scholar
Tang, B. W., Xiang, K., Pang, M. and Z. X. Zhu, “Multi-robot path planning using an improved self-adaptive particle swarm optimization,” Int. J. Adv Robot. Syst 17(5), 1–19 (2020).CrossRefGoogle Scholar