Abstract
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.
Highlights
With the continuous development of society, intelligent manufacturing has been extensively developed
The use of automated guided vehicles (AGVs) in the intelligent manufacturing workshop to solve the problem of machine tool scheduling, thereby obtaining the best solution, will help the company develop into a manufacturing giant [2], [3]
The production line used in our experiments includes 30 machine tools and one AGV
Summary
With the continuous development of society, intelligent manufacturing has been extensively developed. Material transportation in the manufacturing process is an indispensable part of the workshop production process and an important part of the workshop job scheduling problem [1]. The use of AGV in the intelligent manufacturing workshop to solve the problem of machine tool scheduling, thereby obtaining the best solution, will help the company develop into a manufacturing giant [2], [3]. In the intelligent production workshop, AGV is used to solve two problems. This paper focuses on the path planning part and aims to find the optimal transportation path for the machine tool that calls materials.
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