Path planning for unmanned surface vehicle based on improved ant colony algorithm

被引：0
|
作者：
Sun G.-W. [1 ]
Su Y.-X. [2 ]
Gu Y.-C. [1 ]
Xie J.-R. [1 ]
Wang J.-X. [1 ]
机构：
[1] State Key Laboratory of Deep-sea Manned Vehicles, China Ship Scientific Research Center, Wuxi
[2] School of Automation, Wuhan University of Technology, Wuhan
来源：
Kongzhi yu Juece/Control and Decision | 2021年 / 36卷 / 04期
关键词：
Adaptive heuristic function; Ant colony algorithm; Deadlock; Optimal path; Path planning; Unmanned surface vehicle;
D O I：
10.13195/j.kzyjc.2019.0839
中图分类号：
学科分类号：
摘要：
An improved ant colony algorithm for USV (unmanned surface vehicle) path planning is presented. A local tabu list and a global tabu list with different effect duration are set up to catalog the ants passed grids.Various deadlock processing strategies are introduced upon barrier deadlock and self deadlock situation in order to reduce the number of invalid ants and to improve the diversity of solutions. An adaptive heuristic function is designed by adopting the Euclidean distance between the ant and the destination to avoid the initial blindness and later singleness of ant path searching. The current worst path would be superseded by the historical best path when appropriate to retain the previous effort. Simulation results under different maps show that the improved algorithm considerably increases the number of effecive ants during the searhing process and the probability to find the optimal path, as well as the search speed. The improved algorithm performs even better in larger and more complex grid maps. Copyright ©2021 Control and Decision.
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页码：847 / 856
页数：9
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