A Case-based Online Trajectory Planning Method of Autonomous Unmanned Combat Aerial Vehicles with Weapon Release Constraints

被引:2
作者
Tang, Jiayu [1 ]
Li, Xiangmin [1 ]
Dai, Jinjin [1 ]
Bo, Ning [1 ]
机构
[1] Naval Aviat Univ, Yantai, Peoples R China
关键词
Unmanned combat air vehicle; UCAV; Trajectory planning; Receding horizon control; Threat environment;
D O I
10.14429/dsj.70.15040
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
As a challenging and highly complex problem, the trajectory planning for unmanned combat aerial vehicle (UCAV) focuses on optimising flight trajectory under such constraints as kinematics and complicated battlefield environment. An online case-based trajectory planning strategy is proposed in this study to achieve rapid control variables solution of UCAV flight trajectory for the of delivery airborne guided bombs. Firstly, with an analysis of the ballistic model of airborne guided bombs, the trajectory planning model of UCAVs is established with launch acceptable region (LAR) as a terminal constraint. Secondly, a case-based planning strategy is presented, which involves four cases depending on the situation of UCAVs at the current moment. Finally, the feasibility and efficiency of the proposed planning strategy is validated by numerical simulations, and the results show that the presented strategy is suitable for UCAV performing airborne guided delivery missions in dynamic environments.
引用
收藏
页码:374 / 382
页数:9
相关论文
共 18 条
[1]   Survey of numerical methods for trajectory optimization [J].
Betts, JT .
JOURNAL OF GUIDANCE CONTROL AND DYNAMICS, 1998, 21 (02) :193-207
[2]   Path Planning Algorithm based on Arnold Cat Map for Surveillance UAVs [J].
Curiac, Daniel-Ioan ;
Volosencu, Constantin .
DEFENCE SCIENCE JOURNAL, 2015, 65 (06) :483-488
[3]   Imperialist competitive algorithm optimized artificial neural networks for UCAV global path planning [J].
Duan, Haibin ;
Huang, Linzhi .
NEUROCOMPUTING, 2014, 125 :166-171
[4]   Novel intelligent water drops optimization approach to single UCAV smooth trajectory planning [J].
Duan, Haibin ;
Liu, Senqi ;
Wu, Jiang .
AEROSPACE SCIENCE AND TECHNOLOGY, 2009, 13 (08) :442-449
[5]   A Survey of Motion Planning Algorithms from the Perspective of Autonomous UAV Guidance [J].
Goerzen, C. ;
Kong, Z. ;
Mettler, B. .
JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS, 2010, 57 (1-4) :65-100
[6]   Online Path Planning of Autonomous UAVs for Bearing-Only Stand off Multi-Target Following in Threat Environment [J].
Jiang, Hao ;
Liang, Yueqian .
IEEE ACCESS, 2018, 6 :22531-22544
[7]   A Motif-based Mission Planning Method for UAV Swarms Considering Dynamic Reconfiguration [J].
Liu, Jiajie ;
Wang, Weiping ;
Li, Xiaobo ;
Wang, Tao ;
Wang, Tongqing .
DEFENCE SCIENCE JOURNAL, 2018, 68 (02) :159-166
[8]  
Wang G., 2012, ADV SCI ENG MED, V4, P550, DOI DOI 10.1166/ASEM.2012.1223
[9]   Monarch butterfly optimization [J].
Wang, Gai-Ge ;
Deb, Suash ;
Cui, Zhihua .
NEURAL COMPUTING & APPLICATIONS, 2019, 31 (07) :1995-2014
[10]   Moth search algorithm: a bio-inspired metaheuristic algorithm for global optimization problems [J].
Wang, Gai-Ge .
MEMETIC COMPUTING, 2018, 10 (02) :151-164