Improved Particle Swarm Optimization Algorithm for AGV Path Planning
| Autor: | Tao Qiuyun, Sang Hongyan, Guo Hengwei, Wang Ping |
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| Rok vydání: | 2021 |
| Předmět: |
Production line
Automated guided vehicle 0209 industrial biotechnology business.product_category General Computer Science Computer science Crossover 02 engineering and technology Computer Science::Robotics 020901 industrial engineering & automation Local optimum 0202 electrical engineering electronic engineering information engineering General Materials Science Motion planning routing plan scheduling optimization General Engineering Particle swarm optimization Machine tool improved particle swarm optimization algorithm Mutation (genetic algorithm) Path (graph theory) 020201 artificial intelligence & image processing lcsh:Electrical engineering. Electronics. Nuclear engineering business lcsh:TK1-9971 Algorithm |
| Zdroj: | IEEE Access, Vol 9, Pp 33522-33531 (2021) |
| ISSN: | 2169-3536 |
| Popis: | In smart manufacturing workshops, automated guided vehicles (AGVs) are increasingly used to transport materials required for machine tools. This paper studies the AGV path planning problem of a one-line production line in the workshop, establishes a mathematical model with the shortest transportation time as the objective function, and proposes an improved particle swarm optimization(IPSO) algorithm to obtain an optimal path. In order to be suitable for solving the path planning problem, we propose a new coding method based on this algorithm, design a crossover operation to update the particle position, and adopt a mutation mechanism to avoid the algorithm from falling into the local optimum. By calculating the shortest transportation time obtained, the improved algorithm is compared with other intelligent optimization algorithms. The experimental results show that the algorithm can improve the efficiency of AGV in material transportation and verify the effectiveness of related improvement mechanisms. |
| Databáze: | OpenAIRE |
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