Finite-Time Formation Control for Clustered UAVs with Obstacle Avoidance Inspired by Pigeon Hierarchical Behavior

被引:0
作者
Zhang, Zhaoyu [1 ]
Yuan, Yang [1 ]
Duan, Haibin [1 ]
机构
[1] Beihang Univ, Sch Automat Sci & Elect Engn, Natl Key Lab Aircraft Integrated Flight Control, Beijing 100083, Peoples R China
基金
中国国家自然科学基金;
关键词
finite-time formation control; terminal sliding mode surface; unmanned aerial vehicles; obstacle avoidance; pigeon flock behavior; FORMATION TRACKING CONTROL; COLLISION-AVOIDANCE;
D O I
10.3390/drones9040276
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
To address the formation control issue of multiple unmanned aerial vehicles (UAVs), a finite-time control scheme based on terminal sliding mode (TSM) is investigated in this paper. A quadcopter UAV with the vertical takeoff property is considered, with cascaded kinematics composed of rotational and translational loops. To strengthen the application in the low-cost UAV system, the applied torque is synthesized with an auxiliary rotational system, which can avoid utilizing direct attitude measurement. Furthermore, a terminal sliding mode surface is established and employed in the finite-time formation control protocol (FTFCP) as the driven thrust of multiple UAVs over an undirected topology in the translational system. To maintain the safe flight of the UAV clusters in an environment to avoid collision with obstacles or with other UAV neighbors, a pigeon-hierarchy-inspired obstacle avoidance protocol (PHOAP) is proposed. By imitating the interactive hierarchy that exists among the homing pigeon flocks, the collision avoidance scheme is separately enhanced to generate the repulsive potential field for the leader maneuver target and the follower UAV cluster. Subsequently, the collision avoidance laws based on pigeon homing behavior are combined with the finite-time sliding mode formation protocol, and the applied torque is attached as a cascaded structure in the attitude loop to synthesize an obstacle avoidance cooperative control framework. Finally, simulation scenarios of multiple UAVs to reach a desired formation among obstacles is investigated, and the effectiveness of the proposed approach is validated.
引用
收藏
页数:23
相关论文
共 45 条
  • [1] Vision-Based UAV Detection and Tracking Using Deep Learning and Kalman Filter
    Alshaer, Nancy
    Abdelfatah, Reham
    Ismail, Tawfik
    Mahmoud, Haitham
    [J]. COMPUTATIONAL INTELLIGENCE, 2025, 41 (01)
  • [2] Collective Predation and Escape Strategies
    Angelani, Luca
    [J]. PHYSICAL REVIEW LETTERS, 2012, 109 (11)
  • [3] Pipe Routing with Topology Control for Decentralized and Autonomous UAV Networks
    Devaraju, Shreyas
    Garg, Shivam
    Ihler, Alexander
    Bentley, Elizabeth Serena
    Kumar, Sunil
    [J]. DRONES, 2025, 9 (02)
  • [4] Distributed finite-time formation control for multiple quadrotors via local communications
    Dou, Liqian
    Yang, Chuang
    Wang, Dandan
    Tian, Bailing
    Zong, Qun
    [J]. INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, 2019, 29 (16) : 5588 - 5608
  • [5] Model Predictive Formation Tracking-Containment Control for Multi-UAVs With Obstacle Avoidance
    Du, Zhixu
    Zhang, Hao
    Wang, Zhuping
    Yan, Huaicheng
    [J]. IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2024, 54 (06): : 3404 - 3414
  • [6] Distributed Robust Learning Control for Multiple Unmanned Surface Vessels With Fixed-Time Prescribed Performance
    Duan, Haibin
    Yuan, Yang
    Zeng, Zhigang
    [J]. IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2024, 54 (02): : 787 - 799
  • [7] Combinatorial optimization for UAV swarm path planning and task assignment in multi-obstacle battlefield environment
    Guo, Cong
    Huang, Lei
    Tian, Kuo
    [J]. APPLIED SOFT COMPUTING, 2025, 171
  • [8] An Efficient Spatial-Temporal Trajectory Planner for Autonomous Vehicles in Unstructured Environments
    Han, Zhichao
    Wu, Yuwei
    Li, Tong
    Zhang, Lu
    Pei, Liuao
    Xu, Long
    Li, Chengyang
    Ma, Changjia
    Xu, Chao
    Shen, Shaojie
    Gao, Fei
    [J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2024, 25 (02) : 1797 - 1814
  • [9] An improved Bi-RRT*-based path planning algorithm with adaptive search strategy assignment mechanism for ultra-low-altitude penetration of fixed-wing aircraft
    Huang, Haixiang
    Shang, Yaoxing
    Liu, Xianfei
    Liu, Xiaochao
    Qi, Pengyuan
    [J]. AEROSPACE SCIENCE AND TECHNOLOGY, 2024, 152
  • [10] A 3D Path Planning Algorithm for UAVs Based on an Improved Artificial Potential Field and Bidirectional RRT*
    Huang, Yijun
    Li, Hao
    Dai, Yi
    Lu, Gehao
    Duan, Minglei
    [J]. Drones, 2024, 8 (12)