Research on multi-load AGV path planning of weaving workshop based on time priority

Li-zhen Du; Shanfu Ke; Zhen Wang; Jing Tao; Lianqing Yu; Hongjun Li; Li-zhen Du; Shanfu Ke; Zhen Wang; Jing Tao; Lianqing Yu; Hongjun Li

doi:10.3934/mbe.2019113

Mathematical Biosciences and Engineering

2019, Volume 16, Issue 4: 2277-2292. doi: 10.3934/mbe.2019113

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Research on multi-load AGV path planning of weaving workshop based on time priority

1.
School of Mechanical Engineering and Automation, Wuhan Textile University, 430200, Wuhan, China
2.
School of Mechanical and Electrical Engineering, Hubei Polytechnic University,435003, Huangshi, China

Received: 24 November 2018 Accepted: 25 February 2019 Published: 15 March 2019

The multi-load AGV (Automatic Guided Vehicle) is a new kind of materials handling equipment used to load cloth automatically in an intelligent weaving workshop. It can transport multiple rolls of cloth and choose the correct, most effective path to improve the transportation efficiency without people engaged in. This paper creates a feasible path topology according to the layout of the workshop and the logistics environment, and uses the Warshall-Floyd algorithm to search for the optimal route between two arbitrary points. The aim of the path planning is to maximize the machine efficiency, which is constrained by environmental limits, load limits and work limits. This paper establishes the mathematical model of the path planning problem using the mixed genetic particle swarm optimization algorithm (GA-PSO) to solve the problem, and the particle iteration mechanism based on the time priority is proposed to make the evolution more directional and accelerate the convergence speed of the algorithm. The effectiveness and practicability of the model and methods are verified by simulation and benefit analysis.

Keywords:

Citation: Li-zhen Du, Shanfu Ke, Zhen Wang, Jing Tao, Lianqing Yu, Hongjun Li. Research on multi-load AGV path planning of weaving workshop based on time priority[J]. Mathematical Biosciences and Engineering, 2019, 16(4): 2277-2292. doi: 10.3934/mbe.2019113

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Abstract

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