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
相关论文
共 50 条
  • [21] Path Planning of Mobile Robots Based on Ant Colony Algorithm and Artificial Potential Field Algorithm
    Shi W.
    Ning N.
    Song C.
    Ning W.
    Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery, 2023, 54 (12): : 407 - 416
  • [22] UAV Path Planning Based on Chaos Ant Colony Algorithm
    Zhang, Daqiao
    Xian, Yong
    Li, Jie
    Lei, Gang
    Chang, Yan
    2015 International Conference on Computer Science and Mechanical Automation (CSMA), 2015, : 81 - 85
  • [23] An improved ant colony algorithm for robot path planning
    Liu, Jianhua
    Yang, Jianguo
    Liu, Huaping
    Tian, Xingjun
    Gao, Meng
    SOFT COMPUTING, 2017, 21 (19) : 5829 - 5839
  • [24] An improved ant colony algorithm for robot path planning
    Jianhua Liu
    Jianguo Yang
    Huaping Liu
    Xingjun Tian
    Meng Gao
    Soft Computing, 2017, 21 : 5829 - 5839
  • [25] Improved Ant Colony Algorithm for Global Path Planning
    Li, Pengfei
    Wang, Hongbo
    Li, Xiaogang
    ADVANCES IN MATERIALS, MACHINERY, ELECTRONICS I, 2017, 1820
  • [26] Research on the Application of Ant Colony Algorithm in Underwater Path Planning
    Feng, Wei
    Rao, Zhe
    Wang, Zhong
    PROCEEDINGS OF THE 2016 INTERNATIONAL SYMPOSIUM ON ADVANCES IN ELECTRICAL, ELECTRONICS AND COMPUTER ENGINEERING (ISAEECE), 2016, 69 : 46 - 50
  • [27] Mobile Robot Global Path Planning Based on Improved Ant Colony System Algorithm with Potential Field
    Ma X.
    Mei H.
    Ma, Xiaolu (77578249@qq.com), 1600, Chinese Mechanical Engineering Society (57): : 19 - 27
  • [28] A Path Planning Algorithm based on Artificial Potential Field Method and Ant Colony Algorithm
    Yan, Zheping
    Jiang, Liyuan
    Wu, Di
    2021 IEEE INTERNATIONAL CONFERENCE ON MECHATRONICS AND AUTOMATION (IEEE ICMA 2021), 2021, : 1454 - 1459
  • [29] Route Planning of UAV Based on Improved Ant Colony Algorithm
    Qian, Zhengxiang
    Wang, Guocheng
    Wang, Jingen
    Shi, Yongxin
    PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON LOGISTICS, ENGINEERING, MANAGEMENT AND COMPUTER SCIENCE (LEMCS 2015), 2015, 117 : 1421 - 1426
  • [30] Multi-obstacle Path Planning of UAV Based on Improved Ant Colony System Algorithm
    Ling, Fuyuan
    Chen, Jinchao
    Du, Chenglie
    PROCEEDINGS OF 2020 IEEE 5TH INFORMATION TECHNOLOGY AND MECHATRONICS ENGINEERING CONFERENCE (ITOEC 2020), 2020, : 1731 - 1735
12345