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 50 条
  • [31] A local trajectory planning and control method for autonomous vehicles based on the RRT algorithm
    Feraco, Stefano
    Luciani, Sara
    Bonfitto, Angelo
    Amati, Nicola
    Tonoli, Andrea
    2020 AEIT INTERNATIONAL CONFERENCE OF ELECTRICAL AND ELECTRONIC TECHNOLOGIES FOR AUTOMOTIVE (AEIT AUTOMOTIVE), 2020,
  • [32] A Collaborative Path Planning Method for Intelligent Agricultural Machinery Based on Unmanned Aerial Vehicles
    Shi, Min
    Feng, Xia
    Pan, Senshan
    Song, Xiangmei
    Jiang, Linghui
    ELECTRONICS, 2023, 12 (15)
  • [33] Evaluation of unmanned aerial vehicles cooperative combat effectiveness based on conditional entropy combination weight method
    Sun, Lifan
    Chang, Jiashun
    Zhang, Jinjin
    Fu, Zhumu
    Zou, Jie
    Journal of Aerospace Technology and Management, 2021, 13
  • [34] Evaluation of Unmanned Aerial Vehicles Cooperative Combat Effectiveness Based on Conditional Entropy Combination Weight Method
    Sun, Lifan
    Chang, Jiashun
    Zhang, Jinjin
    Fu, Zhumu
    Zou, Jie
    JOURNAL OF AEROSPACE TECHNOLOGY AND MANAGEMENT, 2021, 13
  • [35] Fair-Energy Trajectory Planning for Multi-Target Positioning Based on Cooperative Unmanned Aerial Vehicles
    Ji, Yao
    Dong, Chao
    Zhu, Xiaojun
    Wu, Qihui
    IEEE ACCESS, 2020, 8 (08): : 9782 - 9795
  • [36] CNN-based Real-time Prediction Method of Flight Trajectory of Unmanned Combat Aerial Vehicle
    Zhang H.
    Huang C.
    Tang S.
    Xuan Y.
    Binggong Xuebao/Acta Armamentarii, 2020, 41 (09): : 1894 - 1903
  • [37] UAV air combat autonomous trajectory planning method based on robust adversarial reinforcement learning
    Wang, Lixin
    Zheng, Sizhuang
    Tai, Shang
    Liu, Hailiang
    Yue, Ting
    AEROSPACE SCIENCE AND TECHNOLOGY, 2024, 153
  • [38] Autonomous trajectory planning method for hypersonic vehicles in glide phase based on DDPG algorithm
    Bao, Cunyu
    Wang, Peng
    He, Ruizhi
    Tang, Guojian
    PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING, 2023, 237 (08) : 1855 - 1867
  • [39] Visual Camouflage and Online Trajectory Planning for Unmanned Aerial Vehicle-Based Disguised Video Surveillance: Recent Advances and a Case Study
    Hu, Shuyan
    Yuan, Xin
    Ni, Wei
    Wang, Xin
    Jamalipour, Abbas
    IEEE VEHICULAR TECHNOLOGY MAGAZINE, 2023, 18 (03): : 48 - 57
  • [40] A Trajectory Planning Method of Autonomous Underwater Vehicles Based on Repulsive Field Model Prediction
    Gan, Wenyang
    Cai, Caixia
    Li, Chengsi
    Wang, Haojie
    2022 34TH CHINESE CONTROL AND DECISION CONFERENCE, CCDC, 2022, : 4671 - 4676