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A geometrical path planning method for unmanned aerial vehicle in 2D/3D complex environment
Liang X(梁宵)1; Meng GL(孟光磊)1; Xu, Yimin2; Luo HT(骆海涛)3
Department空间自动化技术研究室
Source PublicationINTELLIGENT SERVICE ROBOTICS
ISSN1861-2776
2018
Volume11Issue:3Pages:301-312
Indexed BySCI ; EI
EI Accession number20182605368099
WOS IDWOS:000435825400007
Contribution Rank3
Funding OrganizationNational Natural Science Foundation of China ; Aeronautical Science Foundation of China ; Natural Science Foundation of Liaoning Province ; Youth Innovation Promotion Association (CAS)
KeywordUnmanned aerial vehicle (UAV) Path planning Geometrical shortest path 2D/3D complex environment Convex obstacles Optimal and real-time planning
AbstractThis paper presents a geometrical path planning method, and it can help unmanned aerial vehicle to find a collision-free path in two-dimensional and three-dimensional (2D and 3D) complex environment quickly. First, a list of tree is designed to describe obstacles, and it is used to query the obstacles which block the line from starting point to finishing point (blocking obstacle). Specially, the list also stores the edge information of blocking obstacle. For the obstacles with short distance, a reasonable way to fly over is studied. Then, a shortest path planning method based on geometrical computation is proposed according to different shapes of obstacles. The obstacles are convex and divided into two cases of 2D and 3D. 2D environment includes rectangular obstacle, trapezoidal obstacle, triangular obstacle, circular obstacle and elliptic obstacle. In 3D, it includes cuboid, sphere and ellipsoid. To compare with other methods, the simulation is made in different environments. In 2D environment with circular obstacles, the method is similar to the artificial potential field. In 2D environment with rectangular obstacles, the performance of the proposed method is better than A-star. Compared with genetic algorithm, the proposed method gives a better result in 3D environment with cuboid obstacles. In 3D environment with hybrid obstacles, it is similar to interfered fluid dynamical system. Through comprehensive comparison and analysis, the conclusion is that the method has good adaptability and does not require grid modeling. It can find a shorter path in 2D/3D complex environment within a short time, so it has the ability of real-time path planning.
Language英语
WOS SubjectRobotics
WOS KeywordOBSTACLE AVOIDANCE ; TARGET TRACKING ; MOBILE ROBOT ; UNDERWATER VEHICLES ; GENETIC ALGORITHM ; SPACE ; OPTIMIZATION ; COLONY ; FLUID
WOS Research AreaRobotics
Funding ProjectNational Natural Science Foundation of China[61503255] ; National Natural Science Foundation of China[51505470] ; Aeronautical Science Foundation of China[2016ZC54011] ; Natural Science Foundation of Liaoning Province[2015020063] ; Youth Innovation Promotion Association (CAS)
Citation statistics
Cited Times:4[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/22134
Collection空间自动化技术研究室
Corresponding AuthorLiang X(梁宵)
Affiliation1.School of Automation, Shenyang Aerospace University, South DaoYi Street No.37, ShenBei New District, Shenyang, Liaoning 110136, China
2.School of Automation, Northwestern Polytechnical University, Chang’an Campus of Northwest Polytechnical University, Dongxiang Street No.1, Chang’an District, Xi’an, Shanxi 710129, China
3.Shenyang Institute of Automation, Chinese Academy of Sciences, Nanta Street No.114, Shenhe District, Shenyang, Liaoning 110016, China
Recommended Citation
GB/T 7714
Liang X,Meng GL,Xu, Yimin,et al. A geometrical path planning method for unmanned aerial vehicle in 2D/3D complex environment[J]. INTELLIGENT SERVICE ROBOTICS,2018,11(3):301-312.
APA Liang X,Meng GL,Xu, Yimin,&Luo HT.(2018).A geometrical path planning method for unmanned aerial vehicle in 2D/3D complex environment.INTELLIGENT SERVICE ROBOTICS,11(3),301-312.
MLA Liang X,et al."A geometrical path planning method for unmanned aerial vehicle in 2D/3D complex environment".INTELLIGENT SERVICE ROBOTICS 11.3(2018):301-312.
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