MATEC Web Conf.
Volume 232, 20182018 2nd International Conference on Electronic Information Technology and Computer Engineering (EITCE 2018)
|Number of page(s)||6|
|Section||Algorithm Study and Mathematical Application|
|Published online||19 November 2018|
AGV optimal path planning based on improved ant colony algorithm
School of Mechanical and Automotive Engineering, Shanghai University of Engineering and Technology, Longteng Road No.333, 201620, Shanghai, China
* Corresponding author: ChengWei He: firstname.lastname@example.org
Using the traditional Ant Colony Algorithm for AGV path planning is easy to fall into the local optimal solution and lacking the capability of obstacle avoidance in the complex storage environment. In this paper, by constructing the MAKLINK undirected network routes and the heuristic function is optimized in the Ant Colony Algorithm, then the AGV path reaches the global optimal path and has the ability to avoid obstacles. According to research, the improved Ant Colony Algorithm proposed in this paper is superior to the traditional Ant Colony Algorithm in terms of convergence speed and the distance of optimal path planning.
© The Authors, published by EDP Sciences, 2018
This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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