UAV Path Planning With Terrain Constraints for Aerial Scanning

doi:10.1109/TIV.2023.3307217

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 平行智能技术与系统团队

	UAV Path Planning With Terrain Constraints for Aerial Scanning
	Yuan, Jinbiao 1; Liu, Zhenbao 2; Xiong, Xiaoyu 3,4; Ai, Yunfeng 3; Chen, Long5 ; Tian, Bin5,6
发表期刊	IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
ISSN	2379-8858
	2024
卷号	9 期号:1 页码:1189-1203
通讯作者	Tian, Bin(bin.tian@ia.ac.cn)
摘要	A global path planning method constrained by dynamics, kinematics, and terrain is proposed for Vertical Takeoff and Landing (VTOL) Unmanned Aerial Vehicles (UAVs). Firstly, new constant-altitude waypoints are interpolated above the terrain based on altitude information and terrain resolution. Account for the coverage of airborne sensors over the surface area, a horizontal waypoint interpolation optimization (HOPT) is performed at positions with excessive bending in the horizontal direction. Secondly, for vertical plane optimization (VOPT), i.e., height optimization, a Soft Actor-Critic-based Particle Swarm Optimization (SAC-PSO) is employed to optimize the convergence speed of the method and achieve terrain following (TF). Thirdly, to evaluate the generated paths, a deep residual network (DRSN) is designed to mitigate the impact of optimization failures during the iteration process and improve the stability of the algorithm. Simulation experiments demonstrate the efficiency and path quality of the proposed method, while real-world tasks validate its practicality.
关键词	Autonomous aerial vehicles Optimization Task analysis Path planning Turning Kinematics Trajectory UAV DRSN SAC-PSO path planning terrain following
DOI	10.1109/TIV.2023.3307217
关键词[WOS]	SAFE TRAJECTORY GENERATION ; VEHICLES ; ENVIRONMENTS
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation Fund[52072309] ; Key Research and Development Program of Shaanxi Program[2019ZDLGY14-02-01] ; Shenzhen Fundamental Research Program[JCYJ20190806152203506] ; Aeronautical Science Foundation of China[ASFC-2018ZC53026] ; Key-Area Research and Development Program of Guangdong Province[2020B0909050001] ; Key-Area Research and Development Program of Guangdong Province[2020B090921003] ; Natural Science Foundation of Hebei Province[2021402011] ; National Key Research and Development Program of China[2022YFB4703700]
项目资助者	National Natural Science Foundation Fund ; Key Research and Development Program of Shaanxi Program ; Shenzhen Fundamental Research Program ; Aeronautical Science Foundation of China ; Key-Area Research and Development Program of Guangdong Province ; Natural Science Foundation of Hebei Province ; National Key Research and Development Program of China
WOS研究方向	Computer Science ; Engineering ; Transportation
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic ; Transportation Science & Technology
WOS记录号	WOS:001173317800104
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/58768
专题	多模态人工智能系统全国重点实验室_平行智能技术与系统团队
通讯作者	Tian, Bin
作者单位	1.Polytech Univ, Sch Northwestern, Xian 710072, Peoples R China 2.Northwestern Polytech Univ, Res & Dev Inst Shenzhen, Sch Civil Aviat, Xian 710072, Peoples R China 3.Univ Chinese Acad Sci, Dept Artificial Intelligence, Beijing 100049, Peoples R China 4.Waytous Inc, Qingdao 266109, Peoples R China 5.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 6.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100190, Peoples R China
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Yuan, Jinbiao,Liu, Zhenbao,Xiong, Xiaoyu,et al. UAV Path Planning With Terrain Constraints for Aerial Scanning[J]. IEEE TRANSACTIONS ON INTELLIGENT VEHICLES,2024,9(1):1189-1203.
APA	Yuan, Jinbiao,Liu, Zhenbao,Xiong, Xiaoyu,Ai, Yunfeng,Chen, Long,&Tian, Bin.(2024).UAV Path Planning With Terrain Constraints for Aerial Scanning.IEEE TRANSACTIONS ON INTELLIGENT VEHICLES,9(1),1189-1203.
MLA	Yuan, Jinbiao,et al."UAV Path Planning With Terrain Constraints for Aerial Scanning".IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 9.1(2024):1189-1203.