TY - GEN
T1 - Path planning for fixed-wing UAVs based on expert knowledge and improved VFH in cluttered environments
AU - Zhang, Haozhe
AU - Zhang, Yongping
AU - Guo, Chubing
AU - Wang, Teng
AU - Fan, Liyuan
AU - Hu, Jinwen
AU - Xu, Zhao
AU - Dou, Zengfa
AU - Zhang, Kai
AU - Liang, Jingyuan
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Obstacle avoidance is a vital problem for flight safety. This paper proposes a fixed wing unmanned aerial vehicle (UAV) path planning algorithm for obstacle avoidance in cluttered environments. Two critical problems have been solved. One is the path planning, i.e., to detour the obstacles at a higher energy cost or to fly through the gaps between obstacles at a lower energy cost by evaluating the collision risk. The other is the optimization of smooth path and pose which is indispensable when the gaps are small. First, the UAV makes the decision of detouring or flying through gaps based on the safety threshold set by the algorithm and the density of the obstacles. Second, if the UAV decides to detour the obstacles, a path planning scheme is developed based on a Dubins-based improved vector field histogram (VFH) for collision avoidance satisfying the kinematic constraints of the fixed-wing UAV. If the UAV decides to fly through the gaps, a pose planner based on machine learning technique is developed, which integrates the human flight experience obtained from trials. The novelty of the proposed strategy is to make the human behavior understandable to machines and let the UAV mimic human behavior in different environments with good adaptability so that the complex nonlinear control and path planning problem can be solved efficiently. Finally, a human-machine interactive simulator is built up to verify the effectiveness of the proposed strategy, and the results show the stable performance in simulations.
AB - Obstacle avoidance is a vital problem for flight safety. This paper proposes a fixed wing unmanned aerial vehicle (UAV) path planning algorithm for obstacle avoidance in cluttered environments. Two critical problems have been solved. One is the path planning, i.e., to detour the obstacles at a higher energy cost or to fly through the gaps between obstacles at a lower energy cost by evaluating the collision risk. The other is the optimization of smooth path and pose which is indispensable when the gaps are small. First, the UAV makes the decision of detouring or flying through gaps based on the safety threshold set by the algorithm and the density of the obstacles. Second, if the UAV decides to detour the obstacles, a path planning scheme is developed based on a Dubins-based improved vector field histogram (VFH) for collision avoidance satisfying the kinematic constraints of the fixed-wing UAV. If the UAV decides to fly through the gaps, a pose planner based on machine learning technique is developed, which integrates the human flight experience obtained from trials. The novelty of the proposed strategy is to make the human behavior understandable to machines and let the UAV mimic human behavior in different environments with good adaptability so that the complex nonlinear control and path planning problem can be solved efficiently. Finally, a human-machine interactive simulator is built up to verify the effectiveness of the proposed strategy, and the results show the stable performance in simulations.
UR - http://www.scopus.com/inward/record.url?scp=85135778131&partnerID=8YFLogxK
U2 - 10.1109/ICCA54724.2022.9831848
DO - 10.1109/ICCA54724.2022.9831848
M3 - 会议稿件
AN - SCOPUS:85135778131
T3 - IEEE International Conference on Control and Automation, ICCA
SP - 255
EP - 260
BT - 2022 IEEE 17th International Conference on Control and Automation, ICCA 2022
PB - IEEE Computer Society
T2 - 17th IEEE International Conference on Control and Automation, ICCA 2022
Y2 - 27 June 2022 through 30 June 2022
ER -