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

被引:3
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
北京市自然科学基金; 中国国家自然科学基金;
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
[11]   Graph Matching-Based Formation Reconfiguration of Networked Agents With Connectivity Maintenance [J].
Kan, Zhen ;
Navaravong, Leenhapat ;
Shea, John M. ;
Pasiliao, Eduardo L., Jr. ;
Dixon, Warren E. .
IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS, 2015, 2 (01) :24-35
[12]  
Karaboga D., 2005, IDEA BASED HONEY BEE
[13]   A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm [J].
Karaboga, Dervis ;
Basturk, Bahriye .
JOURNAL OF GLOBAL OPTIMIZATION, 2007, 39 (03) :459-471
[14]   Numerical solution for a near-minimum-time trajectory for two coordinated manipulators [J].
Lee, KY ;
Dissanayake, MWMG .
ENGINEERING OPTIMIZATION, 1998, 30 (3-4) :227-247
[15]   A Hybrid Offline Optimization Method for Reconfiguration of Multi-UAV Formations [J].
Li, Bin ;
Zhang, Jiangwei ;
Dai, Li ;
Teo, Kok Lay ;
Wang, Song .
IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS, 2021, 57 (01) :506-520
[16]   Transition Optimization for a VTOL Tail-Sitter UAV [J].
Li, Boyang ;
Sun, Jingxuan ;
Zhou, Weifeng ;
Wen, Chih-Yung ;
Low, Kin Huat ;
Chen, Chih-Keng .
IEEE-ASME TRANSACTIONS ON MECHATRONICS, 2020, 25 (05) :2534-2545
[17]   Resource Allocation and 3-D Placement for UAV-Enabled Energy-Efficient IoT Communications [J].
Liu, Yanming ;
Liu, Kai ;
Han, Jinglin ;
Zhu, Lipeng ;
Xiao, Zhenyu ;
Xia, Xiang-Gen .
IEEE INTERNET OF THINGS JOURNAL, 2021, 8 (03) :1322-1333
[18]   Multi-UAV Trajectory Control, Resource Allocation, and NOMA User Pairing for Uplink Energy Minimization [J].
Nguyen, Minh Tri ;
Le, Long Bao .
IEEE INTERNET OF THINGS JOURNAL, 2022, 9 (23) :23728-23740
[19]   A Malicious Node Detection Strategy Based on Fuzzy Trust Model and the ABC Algorithm in Wireless Sensor Network [J].
Pang, Baohe ;
Teng, Zhijun ;
Sun, Huiyang ;
Du, Chunqiu ;
Li, Meng ;
Zhu, Weihua .
IEEE WIRELESS COMMUNICATIONS LETTERS, 2021, 10 (08) :1613-1617
[20]  
Peng Z, 2011, P 2011 2 INT C INT C