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Improved Particle Swarm Optimization Algorithm for AGV Path Planning
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DOI：10.1109/ACCESS.2021.3061288.png)
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Abstract
En 中文
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.
Keywords:
Machine tools
Conferences
Production
Transportation
Task analysis
Heuristic algorithms
Path planning
Automated guided vehicle
improved particle swarm optimization algorithm
scheduling optimization
routing plan
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