TY - GEN
T1 - An Improved Algorithm of UAV 3D Path Planning
AU - Guo, Yicong
AU - Liu, Xiaoxiong
AU - Zhang, Weiguo
AU - Yang, Yue
N1 - Publisher Copyright:
© 2022, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2022
Y1 - 2022
N2 - Path planning is the key of UAV autonomous flight. In order to improve the efficiency of UAV path planning in 3D complex environment, an improved path planning algorithm is proposed in this paper. First of all, in terms of map construction, aiming at the problem that sampling points cannot cover narrow free space in the traditional probabilistic roadmaps (PRM), this paper proposes a sampling point translation strategy to move out the sampling points within the obstacle threat. Then, in the aspect of path generation, this paper proposes an improved ant colony optimization (ACO), which improves the transition probability and pheromone update rule of traditional ACO. In order to improve the efficiency of the ACO, target guidance information is added to the calculation of transfer probability. Besides, collision threat cost and path length are added to the pheromone update rule to solve the problem that the traditional ACO can only optimize a single target. The simulation results show that the algorithm in this paper effectively makes up for the shortcomings of the traditional PRM and the traditional ACO. The path planning method in the 3D complex environment of UAV is feasible.
AB - Path planning is the key of UAV autonomous flight. In order to improve the efficiency of UAV path planning in 3D complex environment, an improved path planning algorithm is proposed in this paper. First of all, in terms of map construction, aiming at the problem that sampling points cannot cover narrow free space in the traditional probabilistic roadmaps (PRM), this paper proposes a sampling point translation strategy to move out the sampling points within the obstacle threat. Then, in the aspect of path generation, this paper proposes an improved ant colony optimization (ACO), which improves the transition probability and pheromone update rule of traditional ACO. In order to improve the efficiency of the ACO, target guidance information is added to the calculation of transfer probability. Besides, collision threat cost and path length are added to the pheromone update rule to solve the problem that the traditional ACO can only optimize a single target. The simulation results show that the algorithm in this paper effectively makes up for the shortcomings of the traditional PRM and the traditional ACO. The path planning method in the 3D complex environment of UAV is feasible.
KW - Ant colony optimization (ACO)
KW - Path planning
KW - Probabilistic roadmaps (PRM)
KW - Unmanned aerial vehicle (UAV)
UR - http://www.scopus.com/inward/record.url?scp=85120630331&partnerID=8YFLogxK
U2 - 10.1007/978-981-15-8155-7_71
DO - 10.1007/978-981-15-8155-7_71
M3 - 会议稿件
AN - SCOPUS:85120630331
SN - 9789811581540
T3 - Lecture Notes in Electrical Engineering
SP - 849
EP - 859
BT - Advances in Guidance, Navigation and Control - Proceedings of 2020 International Conference on Guidance, Navigation and Control, ICGNC 2020
A2 - Yan, Liang
A2 - Duan, Haibin
A2 - Yu, Xiang
PB - Springer Science and Business Media Deutschland GmbH
T2 - International Conference on Guidance, Navigation and Control, ICGNC 2020
Y2 - 23 October 2020 through 25 October 2020
ER -