Hostname: page-component-7c8c6479df-995ml Total loading time: 0 Render date: 2024-03-28T18:55:36.141Z Has data issue: false hasContentIssue false

Autonomous Intelligent Planning Method for Welding Path of Complex Ship Components

Published online by Cambridge University Press:  18 June 2020

Tao Wang
Affiliation:
School of Automation, Guangdong University of Technology, Guanzhou510006, China. E-mails: wangtao_cps@gdut.edu.cn, 791396753@QQ.com
Zhilong Xue
Affiliation:
School of Automation, Guangdong University of Technology, Guanzhou510006, China. E-mails: wangtao_cps@gdut.edu.cn, 791396753@QQ.com
Xiaoqing Dong
Affiliation:
School of Automation, Guangdong University of Technology, Guanzhou510006, China. E-mails: wangtao_cps@gdut.edu.cn, 791396753@QQ.com
Senlin Xie*
Affiliation:
School of Automation, Guangdong University of Technology, Guanzhou510006, China. E-mails: wangtao_cps@gdut.edu.cn, 791396753@QQ.com
*
*Corresponding author. E-mail: senlinxie@163.com

Summary

Aiming at planning the welding path of complex ship components, a welding path planning optimization model was constructed with the shortest welding path and using the target and the welding process and welding starting and ending points as constraints. Based on the model, an improved ant colony algorithm with dynamic adaptive parameters was proposed to complete the path planning work. Simulation results showed that, compared with other classical optimization algorithms, the proposed algorithm improved optimization speed while ensuring optimization effect and achieving better results in path planning of complex ship components.

Type
Articles
Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Hu, Y. and Meng, Z., “Bottlenecks in the development of China’s shipbuilding industry and analysis of the development strategy of “big to strong”,Globalization (02), 94−104+136 (2019). DOI:10.16845/j.cnki.ccieeqqh.2019.02.012.Google Scholar
Hu, B., Wu, D. and Yang, J., “Development strategy of green ship technology,” Guangdong Shipbuild. 38(01), 8688 (2019).Google Scholar
Ministry of Industry and Information Technology Website, “The intelligent ship development action plan symposium was held in Dalian,” Shipbuild. Standardization Qual. 31(02), 712 (2018).Google Scholar
Wu, M., Huang, H. and Wang, X.. “Robot welding path planning based on improved ant colony algorithm,” Trans. China Weld. Soc. 39(10), 113118 (2018).Google Scholar
Wang, X., Tang, B. and Gu, X.. “Research on obstacle avoidance strategy of welding robots,” J. Mech. Eng. 55(17), 7178 (2019).Google Scholar
Zhang, R., Li, X. and Gao, H.. “Research on collaborative path planning of dual welding robots,” Modular Mach. Tool Autom. Manuf. Tech. (06), 8185 (2019). DOI:10.13462/j.cnki.mmtamt.2019.06.022.Google Scholar
Lin, Z. and Xu, L., “An improved ant colony optimization applied in programing laser welding path,” Trans. China Weld. Inst. 39(1), 107110 (2018).Google Scholar
Tu, H. and Xu, X.. “Optimizing logistics distribution routing problem based on improved ant colony algorithm,” Mach. Des. Manuf. (08), 265268 (2017). DOI:10.19356/j.cnki.1001-3997.2017.08.074.Google Scholar
He, S., Shi, J. and Wang, H., “Path planning of mobile robot based on improved ant colony particle swarm optimization algorithm,” Electr. Meas. Tech. 34(4), 765770 (2014).Google Scholar
Wu, H. and Chen, X., “Improved ant colony algorithm based on natural selection strategy for solving TSP problem”. J. Commun. 34(04), 165170 (2013).Google Scholar
Xu, L. and Pan, D., “An improved genetic ant colony algorithm for solving TSP problem,” Intell. Comput. Appl. 7(3), 3436 (2017).Google Scholar
Du, H. and Li, Y., “Research on affect performance of parameter settings in ant colony algorithm,” Mod. Comput. 13, 37 (2012).Google Scholar
Wei, X. and Li, Y.. “Research on parameters optimization and simulation of the ant colony algorithm,” Manuf. Autom. 37(10), 3335 (2015).Google Scholar
Dorigo, M., Maniezzo, V. and Colorni, A., “The ant system: Optimization by a colony of cooperating agents,” IEEE Trans. Syst. Man Cybern. 26(01), 2941 (1996).CrossRefGoogle ScholarPubMed
Kim, K. H. and Moon, K. C., “Berth scheduling by simulated annealing,” Transp. Res. Part B 37, 541560 (2003).CrossRefGoogle Scholar
Stutzle, T. and Hoos, H. H., “MAX-MIN ant system,” Future Gener. Comput. Syst. 16(9), 889914 (2000).CrossRefGoogle Scholar
Dorigo, M. and Gambardella, L. M., “Ant colony system : A cooperative learning approach to the traveling salesman problem,” IEEE Trans. Evolut. Comput. 1(1), 5366 (1997).CrossRefGoogle Scholar