Reconfiguration for UAV Formation: A Novel Method Based on Modified Artificial Bee Colony Algorithm

被引:2
作者
Yang, Zipeng [1 ]
Yang, Futing [1 ]
Mao, Tianqi [1 ]
Xiao, Zhenyu [1 ]
Han, Zhu [2 ]
Xia, Xianggen [3 ]
机构
[1] Beihang Univ, Sch Elect & Informat Engn, Beijing 100191, Peoples R China
[2] Univ Houston, Dept Elect & Comp Engn, Houston, TX 77004 USA
[3] Univ Delaware, Dept Elect & Comp Engn, Newark, DE 19716 USA
基金
中国国家自然科学基金; 北京市自然科学基金;
关键词
unmanned aerial vehicle (UAV); formation reconfiguration; optimal control; artificial bee colony algorithm; swarm intelligence; RESOURCE-ALLOCATION; OPTIMIZATION; STRATEGY;
D O I
10.3390/drones7100595
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
The flight formation of unmanned aerial vehicles (UAVs) needs to be reconfigured whenever necessary to cope with complex environments and varying tasks. However, the continuity, nonlinearity and high dimensionality of the UAV formation control parameters bring significant challenges to the efficiency and safety of UAV formation reconfiguration. To this end, this paper proposes a reconfiguration strategy of the UAV formation based on a modified Artificial Bee Colony (ABC) algorithm, which ensures superior efficiency and safety level simultaneously. Specifically, we first formulate the formation reconfiguration problem minimizing the time consumed for reconfiguration under the constraints of safety and connection. Then the continuous optimization problem is discretized by using the control parameterization and time discretization (CPTD) method. Finally, we use a modified ABC algorithm to find the solution of formation reconfiguration. Extensive performance evaluations are conducted to verify the superiority of the proposed method. It is concluded that the proposed algorithm achieves a better performance than the existing approaches in literature in solving the problem of 3-D formation reconfiguration.
引用
收藏
页数:17
相关论文
共 32 条
  • [1] UAV-Based Relay System for IoT Networks with Strict Reliability and Latency Requirements
    Abbas, Nadine
    Mrad, Ali
    Ghazleh, Ali
    Sharafeddine, Sanaa
    [J]. IEEE Networking Letters, 2021, 3 (03): : 110 - 113
  • [2] A modified Artificial Bee Colony algorithm for real-parameter optimization
    Akay, Bahriye
    Karaboga, Dervis
    [J]. INFORMATION SCIENCES, 2012, 192 : 120 - 142
  • [3] Low-Thrust Reconfiguration Strategy and Optimization for Formation Flying Using Jordan Normal Form
    Bai, Xue
    He, Yanchao
    Xu, Ming
    [J]. IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS, 2021, 57 (05) : 3279 - 3295
  • [4] Differential Evolution With Neighborhood and Direction Information for Numerical Optimization
    Cai, Yiqiao
    Wang, Jiahai
    [J]. IEEE TRANSACTIONS ON CYBERNETICS, 2013, 43 (06) : 2202 - 2215
  • [5] Hybrid Particle Swarm Optimization and Genetic Algorithm for Multi-UAV Formation Reconfiguration
    Duan, Haibin
    Luo, Qinan
    Ma, Guanjun
    Shi, Yuhui
    [J]. IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE, 2013, 8 (03) : 16 - 27
  • [6] Reinforcement Learning-Based Control Strategy for Multi-Agent Systems Subjected to Actuator Cyberattacks During Affine Formation Maneuvers
    El-Ferik, Sami
    Maaruf, Muhammad
    Al-Sunni, Fouad M.
    Saif, Abdulwahid Abdulaziz
    Al Dhaifallah, Mujahed Mohammad
    [J]. IEEE ACCESS, 2023, 11 : 77656 - 77668
  • [7] Time-subminimal trajectory planning for discrete non-linear systems
    Furukawa, T
    [J]. ENGINEERING OPTIMIZATION, 2002, 34 (03) : 219 - 243
  • [8] Furukawa T., 2003, P IEEE INT S COMP IN
  • [9] Hybrid swarm intelligent algorithm for multi-UAV formation reconfiguration
    Gao, Chenyang
    Ma, Jianfeng
    Li, Teng
    Shen, Yulong
    [J]. COMPLEX & INTELLIGENT SYSTEMS, 2023, 9 (02) : 1929 - 1962
  • [10] Survey of Important Issues in UAV Communication Networks
    Gupta, Lav
    Jain, Raj
    Vaszkun, Gabor
    [J]. IEEE COMMUNICATIONS SURVEYS AND TUTORIALS, 2016, 18 (02): : 1123 - 1152