Research on Improved Potential Field Ant Colony Algorithm for UAV Path Planning

被引：4
|
作者：
Chen, Tao [1 ]
Lv, Xinyu [1 ]
Wang, Shengying [1 ]
Ta, Na [1 ]
Zhao, Jing [2 ]
Chen, Xinpei [3 ]
Xiao, Mingxia [1 ]
Wei, Haicheng [1 ]
机构：
[1] North Minzu Univ, Sch Elect & Informat Engn, Yinchuan 750021, Ningxia, Peoples R China
[2] Ningxia Univ, Sch Informat Engn, Yinchuan 750021, Ningxia, Peoples R China
[3] Ningxia Presch Educ Coll, Educ Informat Ctr, Yinchuan 750001, Ningxia, Peoples R China
来源：
PROCEEDINGS OF THE 33RD CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2021) | 2021年
基金：
中国国家自然科学基金;
关键词：
path planning; ant colony algorithm; artificial potential field algorithm; the decrease coefficient of potential field resultant force;
D O I：
10.1109/CCDC52312.2021.9602445
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In order to solve the problems of slow convergence speed and prone to local optimization in ant colony algorithm, an improved ant colony algorithm of potential field is proposed in this study. Firstly, the method of pheromone affected by artificial potential field is introduced to reduce the blindness of the ant colony. Then, the potential field heuristic information is used to accelerate the convergence speed of ant colony algorithm. Finally, the decrease coefficient of potential field resultant force is used to solve the local optimal problem. Compare with the traditional algorithm, the experiments show that the improved algorithm in this paper can get better results, in terms of path length and the number of convergence iterations, the average search time for convergence is reduced by 44.83% in the 20x20 map.
引用
收藏
页码：535 / 539
页数：5
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