Path planning of mobile robot based on improved ant colony algorithm for logistics

Tian Xue; Liu Li; Liu Shuang; Du Zhiping; Pang Ming; Tian Xue; Liu Li; Liu Shuang; Du Zhiping; Pang Ming

doi:10.3934/mbe.2021152

Mathematical Biosciences and Engineering

2021, Volume 18, Issue 4: 3034-3045. doi: 10.3934/mbe.2021152

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Research article Special Issues

Path planning of mobile robot based on improved ant colony algorithm for logistics

1.
Logistics School, Beijing Wuzi University, Beijing 101149, China
2.
School of management, Harbin University of Commerce, Harbin 150080, China
3.
School of computer and Information Engineering, Harbin University of Commerce, Harbin 150080, China
4.
College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, Heilongjiang 150001, China

Academic Editor: Jida Huang

Received: 05 February 2021 Accepted: 16 March 2021 Published: 30 March 2021

The path planning of robot is of great significance for the logistics industry, which helps to improve the efficiency of warehousing, sorting and distribution. On the basis of ant colony algorithm, multi step search strategy is used instead of single step search strategy, pheromone update mechanism is redesigned, and path smoothing is configured to improve the performance of the algorithm. The experimental results show that the improved ant colony algorithm proposed in this paper can plan a shorter optimal path on the 16 * 16 grid logistics storage site, and the path length is saved by 9.21%.
- robot,
- logistics,
- route planning,
- optimal path,
- multi step search
Citation: Tian Xue, Liu Li, Liu Shuang, Du Zhiping, Pang Ming. Path planning of mobile robot based on improved ant colony algorithm for logistics[J]. Mathematical Biosciences and Engineering, 2021, 18(4): 3034-3045. doi: 10.3934/mbe.2021152

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Abstract

The path planning of robot is of great significance for the logistics industry, which helps to improve the efficiency of warehousing, sorting and distribution. On the basis of ant colony algorithm, multi step search strategy is used instead of single step search strategy, pheromone update mechanism is redesigned, and path smoothing is configured to improve the performance of the algorithm. The experimental results show that the improved ant colony algorithm proposed in this paper can plan a shorter optimal path on the 16 * 16 grid logistics storage site, and the path length is saved by 9.21%.

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