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Ant Colony Optimization Solutions for Path Planning of Logistic Vehicle

Author

Listed:
  • Luo Jun-Qi

    (Department of School of Electric and Information Engineering, Qinzhou University, Guangxi, China)

  • Wei Chien

    (Department of School of Electric and Information Engineering, Qinzhou University, Guangxi, China)

  • Xu Jia-Xin

    (Qinzhou University, Guangxi, China)

  • Liang Xi-Qiu

    (Qinzhou University, Guangxi, China)

Abstract

In recent years, online shopping has greatly promoted the development of the logistics industry. Logistics path planning has become a hot research topic among many researchers. Although path planning has been discussed by several previous studies, some real logistics conditions are not considered like obstacle and road slope. This paper presents a novel proposal to solve the problem of path planning for logistic vehicle based on Ant Colony Optimization (ACO) algorithm in the environment in which exists the obstacle. There are two kinds of environment in the path planner application, one is a single obstacle placed between the starting point and the terminal point in a known map which is recognized, then uses the ACO to find an optimal path with the capability to avoid impact with the obstacle for a logistics vehicle. The other works in the model with multi-obstacle and the same map as before to explore whether the best path solution can be found successfully by the ACO algorithm. Through experimental evaluations, the AOC can be verified to solve the path planning problem in the static, the grid and muti-obstacles environment model. Additionally, different from the common path planning algorithm, the size of the logistics vehicle is considered, the situation of touching the fringes of obstacles could be avoided, which is able to apply the logistic vehicle in the real environment.

Suggested Citation

  • Luo Jun-Qi & Wei Chien & Xu Jia-Xin & Liang Xi-Qiu, 2018. "Ant Colony Optimization Solutions for Path Planning of Logistic Vehicle," International Journal of Technology and Engineering Studies, PROF.IR.DR.Mohid Jailani Mohd Nor, vol. 4(3), pages 95-101.
    • Handle: RePEc:apa:ijtess:2018:p:95-101
    DOI: 10.20469/ijtes.4.10003-3
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    References listed on IDEAS

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    2. Jiang Zhao & Dingding Cheng & Chongqing Hao, 2016. "An Improved Ant Colony Algorithm for Solving the Path Planning Problem of the Omnidirectional Mobile Vehicle," Mathematical Problems in Engineering, Hindawi, vol. 2016, pages 1-10, September.
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