Improved Particle Swarm Optimization Algorithm for AGV Path Planning

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 transportatio...

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Veröffentlicht in:IEEE access Jg. 9; S. 33522 - 33531
Hauptverfasser: Qiuyun, Tao, Hongyan, Sang, Hengwei, Guo, Ping, Wang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Automated guided vehicle
Automated guided vehicles
Conferences
Heuristic algorithms
improved particle swarm optimization algorithm
Machine tools
Mutation
Optimization
Particle swarm optimization
Path planning
Production
routing plan
scheduling optimization
Task analysis
Transportation
Workshops
ISSN:2169-3536, 2169-3536
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Zusammenfassung: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.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3061288