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Acta Aeronautica et Astronautica Sinica ›› 2023, Vol. 44 ›› Issue (20): 628775-628775.doi: 10.7527/S1000-6893.2023.28775

• special column • Previous Articles    

Recovery trajectory optimization for UAV towed aerial recovery based on trajectory mapping

Honglun WANG1,2(), Yanxiang WANG1,2,3, Yiheng LIU1,2,4   

  1. 1.School of Automation Science and Electrical Engineering,Beihang University,Beijing  100191,China
    2.The Science and Technology on Aircraft Control Laboratory,Beihang University,Beijing  100191,China
    3.Shenyuan Honors College,Beihang University,Beijing  100191,China
    4.Beijing Institute of Astronautical Systems Engineering,Beijing  100076,China
  • Received:2023-03-30 Revised:2023-04-27 Accepted:2023-05-30 Online:2023-06-07 Published:2023-06-05
  • Contact: Honglun WANG E-mail:wang_hl_12@126.com
  • Supported by:
    National Natural Science Foundation of China(62173022)

Abstract:

For the problem of trajectory optimization in the process of Unmanned Aerial Vehicle (UAV) towed aerial recovery, an optimization method of UAV recovery trajectory is proposed based on trajectory mapping. First, an aerial recovery system model, including the cable-drogue-UAV assembly model and the wing fold model, is established. Second, the idea of trajectory mapping is put forward, and the accurate mapping relationship between the recovery instruction and the recovery trajectory in the recovery system is established by using the Bidirectional Long Short-Term Memory (BiLSTM) neural network. Third, the trajectory mapping network is utilized to predict the real recovery trajectory under different instructions in real time, and the Particle Swarm Optimization (PSO) algorithm is used to optimize the optimal recovery instruction according to the calculated predicted trajectory cost. Finally, the simulation results show that the proposed trajectory mapping network has high prediction accuracy and calculation speed, and the proposed optimization method can achieve stable and rapid recovery of UAV.

Key words: aerial recovery, trajectory optimization, trajectory mapping, cable-drogue-UAV assembly, Bidirectional Long Short-Term Memory (BiLSTM), neural network

CLC Number: