Path Planning for Mount Robot Based on Improved Particle Swarm Optimization Algorithm
To address the problem of cooperative work among right-angle coordinate robots in spacecraft structural plate mount tasks, an improved particle swarm optimization (PSO) algorithm was proposed to assign paths to three robots in a surface-mounted technology (SMT) machine. First, the optimization objec...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/15/3289 |
| _version_ | 1797586895761309696 |
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| author | Xudong Li Bin Tian Shuaidong Hou Xinxin Li Yang Li Chong Liu Jingmin Li |
| author_facet | Xudong Li Bin Tian Shuaidong Hou Xinxin Li Yang Li Chong Liu Jingmin Li |
| author_sort | Xudong Li |
| collection | DOAJ |
| description | To address the problem of cooperative work among right-angle coordinate robots in spacecraft structural plate mount tasks, an improved particle swarm optimization (PSO) algorithm was proposed to assign paths to three robots in a surface-mounted technology (SMT) machine. First, the optimization objective of path planning was established by analyzing the working process of the SMT machine. Then, the inertia weight update strategy was designed to overcome the early convergence of the traditional PSO algorithm, and the learning factor of each particle was calculated using fuzzy control to improve the global search capability. To deal with the concentration phenomenon of particles in the iterative process, the genetic algorithm (GA) was introduced when the particles were similar. The particles were divided into elite, high-quality, or low-quality particles according to their performance. New particles were generated through selection and crossover operations to maintain the particle diversity. The performance of the proposed algorithm was verified with the simulation results, which could shorten the planning path and quicken the convergence compared to the traditional PSO or GA. For large and complex maps, the proposed algorithm shortens the path by 7.49% and 11.49% compared to traditional PSO algorithms, and by 3.98% and 4.02% compared to GA. |
| first_indexed | 2024-03-11T00:28:38Z |
| format | Article |
| id | doaj.art-a8b22f999bdc48409718505f3adda5ef |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-11T00:28:38Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-a8b22f999bdc48409718505f3adda5ef2023-11-18T22:49:02ZengMDPI AGElectronics2079-92922023-07-011215328910.3390/electronics12153289Path Planning for Mount Robot Based on Improved Particle Swarm Optimization AlgorithmXudong Li0Bin Tian1Shuaidong Hou2Xinxin Li3Yang Li4Chong Liu5Jingmin Li6School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, ChinaSchool of Mechanical Engineering, Dalian University of Technology, Dalian 116024, ChinaSchool of Mechanical Engineering, Dalian University of Technology, Dalian 116024, ChinaSchool of Mechanical Engineering, Dalian University of Technology, Dalian 116024, ChinaSchool of Mechanical Engineering, Dalian University of Technology, Dalian 116024, ChinaSchool of Mechanical Engineering, Dalian University of Technology, Dalian 116024, ChinaSchool of Mechanical Engineering, Dalian University of Technology, Dalian 116024, ChinaTo address the problem of cooperative work among right-angle coordinate robots in spacecraft structural plate mount tasks, an improved particle swarm optimization (PSO) algorithm was proposed to assign paths to three robots in a surface-mounted technology (SMT) machine. First, the optimization objective of path planning was established by analyzing the working process of the SMT machine. Then, the inertia weight update strategy was designed to overcome the early convergence of the traditional PSO algorithm, and the learning factor of each particle was calculated using fuzzy control to improve the global search capability. To deal with the concentration phenomenon of particles in the iterative process, the genetic algorithm (GA) was introduced when the particles were similar. The particles were divided into elite, high-quality, or low-quality particles according to their performance. New particles were generated through selection and crossover operations to maintain the particle diversity. The performance of the proposed algorithm was verified with the simulation results, which could shorten the planning path and quicken the convergence compared to the traditional PSO or GA. For large and complex maps, the proposed algorithm shortens the path by 7.49% and 11.49% compared to traditional PSO algorithms, and by 3.98% and 4.02% compared to GA.https://www.mdpi.com/2079-9292/12/15/3289mount robotpath planningparticle swarm optimization (PSO)adaptive strategy |
| spellingShingle | Xudong Li Bin Tian Shuaidong Hou Xinxin Li Yang Li Chong Liu Jingmin Li Path Planning for Mount Robot Based on Improved Particle Swarm Optimization Algorithm Electronics mount robot path planning particle swarm optimization (PSO) adaptive strategy |
| title | Path Planning for Mount Robot Based on Improved Particle Swarm Optimization Algorithm |
| title_full | Path Planning for Mount Robot Based on Improved Particle Swarm Optimization Algorithm |
| title_fullStr | Path Planning for Mount Robot Based on Improved Particle Swarm Optimization Algorithm |
| title_full_unstemmed | Path Planning for Mount Robot Based on Improved Particle Swarm Optimization Algorithm |
| title_short | Path Planning for Mount Robot Based on Improved Particle Swarm Optimization Algorithm |
| title_sort | path planning for mount robot based on improved particle swarm optimization algorithm |
| topic | mount robot path planning particle swarm optimization (PSO) adaptive strategy |
| url | https://www.mdpi.com/2079-9292/12/15/3289 |
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