Ant Colony Optimization Solutions for Path Planning of Logistic Vehicle

Luo Jun-Qi, Wei Chien, Xu Jia-Xin and Liang Xi-Qiu
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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

International Journal of Technology and Engineering Studies, 2018, vol. 4, issue 3, 95-101

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.

Keywords: Ant colony optimization; optimal path planning; logistic vehicle; obstacle avoidance (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:apa:ijtess:2018:p:95-101

DOI: 10.20469/ijtes.4.10003-3

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