A Hybrid-Driven Optimization Framework for Fixed-Wing UAV Maneuvering Flight Planning

Electronics

Su Cao¹  Huangchao Yu¹  Kuang Zhao²  Renshan Zhang²  Yongyang Hu² 

[1] Institute of Unmanned Systems, National University of Defense Technology, Changsha 410073, China;Nanjing Telecommunication Technology Research Institute, National University of Defense Technology, Nanjing 210007, China;

关键词: flight maneuvers;    hybrid data and model driven;    key-frame;    motion primitives;   

DOI  :  10.3390/electronics10192330

来源: DOAJ

【 摘 要 】

Performing autonomous maneuvering flight planning and optimization remains a challenge for unmanned aerial vehicles (UAVs), especially for fixed-wing UAVs due to its high maneuverability and model complexity. A novel hybrid-driven fixed-wing UAV maneuver optimization framework, inspired by apprenticeship learning and nonlinear programing approaches, is proposed in this paper. The work consists of two main aspects: (1) Identifying the model parameters for a certain fixed-wing UAV based on the demonstrated flight data performed by human pilot. Then, the features of the maneuvers can be described by the positional/attitude/compound key-frames. Eventually, each of the maneuvers can be decomposed into several motion primitives. (2) Formulating the maneuver planning issue into a minimum-time optimization problem, a novel nonlinear programming algorithm was developed, which was unnecessary to determine the exact time for the UAV to pass by the key-frames. The simulation results illustrate the effectiveness of the proposed framework in several scenarios, as both the preservation of geometric features and the minimization of maneuver times were ensured.

【 授权许可】

Unknown