Path Planning and Obstacle Avoidance of Unmanned Aerial Vehicle Based on Improved Genetic Algorithms

被引：0
|
作者：
Wang Yang [1 ]
Chen Wenjie [1 ,2 ]
机构：
[1] Beijing Inst Technol, Sch Automat, Beijing 100081, Peoples R China
[2] Key Lab Intelligent Control & Decis Complex Syst, Beijing 100081, Peoples R China
来源：
2014 33RD CHINESE CONTROL CONFERENCE (CCC) | 2014年
关键词：
Genetic Algorithms; Obstacle Avoidance; UAV; ASSIGNING COOPERATING UAVS; TASKS;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Path planning is always an essential issue and complicated optimum problem for unmanned aerial vehicle (UAV). Genetic algorithms are well applied to solve such problems as a stochastic search method. In this paper, a new method of path planning for UAV based on genetic algorithms is introduced. Reasonable coding way and fitness function are used in this improved genetic algorithm, and prior knowledge is added to the genetic algorithm. By selecting essential points and moving strategy in advance, this new method can highly reduce the computation cost and find the optimal path more efficiently. The simulation result shows that this new approach is proved to improve the search efficiency.
引用
收藏
页码：8612 / 8616
页数：5
相关论文
共 50 条
  • [1] Q-learning-based unmanned aerial vehicle path planning with dynamic obstacle avoidance
    Sonny, Amala
    Yeduri, Sreenivasa Reddy
    Cenkeramaddi, Linga Reddy
    [J]. APPLIED SOFT COMPUTING, 2023, 147
  • [2] A Study on Path Planning of Unmanned Aerial Vehicle Based on Improved Genetic Algorithm
    Tao, Jihua
    Zhong, Chaoliang
    Gao, Li
    Deng, Hao
    [J]. 2016 8TH INTERNATIONAL CONFERENCE ON INTELLIGENT HUMAN-MACHINE SYSTEMS AND CYBERNETICS (IHMSC), VOL. 2, 2016, : 392 - 395
  • [3] Monocular vision based obstacle avoidance trajectory planning for Unmanned Aerial Vehicle
    Zhang, Zhouyu
    Cao, Yunfeng
    Ding, Meng
    Zhuang, Likui
    Tao, Jiang
    [J]. AEROSPACE SCIENCE AND TECHNOLOGY, 2020, 106 (106)
  • [4] Path Planning with Potential Field-Based Obstacle Avoidance in a 3D Environment by an Unmanned Aerial Vehicle
    Batinovic, Ana
    Goricanec, Jurica
    Markovic, Lovro
    Bogdan, Stjepan
    [J]. 2022 INTERNATIONAL CONFERENCE ON UNMANNED AIRCRAFT SYSTEMS (ICUAS), 2022, : 394 - 401
  • [5] Block Matching Based Obstacle Avoidance for Unmanned Aerial Vehicle
    Ivanovas, Adomas
    Ostreika, Armantas
    Maskeliunas, Rytis
    Damasevicius, Robertas
    Polap, Dawid
    Wozniak, Marcin
    [J]. ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING, ICAISC 2018, PT I, 2018, 10841 : 58 - 69
  • [6] Automatic Obstacle Avoidance Path Planning Method for Unmanned Ground Vehicle Based on Improved Bee Colony Algorithm
    Ren, Yan
    Liu, Jiayong
    [J]. JORDAN JOURNAL OF MECHANICAL AND INDUSTRIAL ENGINEERING, 2022, 16 (01): : 11 - 18
  • [7] Hierarchical Path Planning and Obstacle Avoidance Control for Unmanned Surface Vehicle
    Du, Hongbao
    Wang, Zhengjie
    Zhang, Zhide
    Li, Qiaoyi
    [J]. 2021 4TH INTERNATIONAL CONFERENCE ON MECHATRONICS, ROBOTICS AND AUTOMATION (ICMRA 2021), 2020, : 134 - 138
  • [8] Path planning of unmanned aerial vehicle based on improved gravitational search algorithm
    LI Pei DUAN HaiBin Science and Technology on Aircraft Control LaboratorySchool of Automation Science and Electrical EngineeringBeihang UniversityBeijing China State Key Laboratory of Virtual Reality Technology and SystemsBeihang UniversityBeijing China
    [J]. Science China(Technological Sciences), 2012, 55 (10) : 2712 - 2719
  • [9] Local obstacle avoidance method for unmanned aerial vehicle based on improved artificial potential field
    Lin L.
    He H.
    He B.
    Chen G.
    [J]. Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of Science and Technology (Natural Science Edition), 2021, 49 (08): : 86 - 91
  • [10] Path planning of unmanned aerial vehicle based on improved gravitational search algorithm
    Li Pei
    Duan HaiBin
    [J]. SCIENCE CHINA-TECHNOLOGICAL SCIENCES, 2012, 55 (10) : 2712 - 2719
12345