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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 Publication INTELLIGENT SERVICE ROBOTICS ISSN 1861-2776 2018 Volume 11Issue:3Pages:301-312 Indexed By SCI ; EI EI Accession number 20182605368099 WOS ID WOS:000435825400007 Contribution Rank 3 Funding Organization National Natural Science Foundation of China ; Aeronautical Science Foundation of China ; Natural Science Foundation of Liaoning Province ; Youth Innovation Promotion Association (CAS) Keyword Unmanned aerial vehicle (UAV) Path planning Geometrical shortest path 2D/3D complex environment Convex obstacles Optimal and real-time planning Abstract This 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 Subject Robotics WOS Keyword OBSTACLE AVOIDANCE ; TARGET TRACKING ; MOBILE ROBOT ; UNDERWATER VEHICLES ; GENETIC ALGORITHM ; SPACE ; OPTIMIZATION ; COLONY ; FLUID WOS Research Area Robotics Funding Project National 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:3[WOS]   [WOS Record]     [Related Records in WOS] Document Type 期刊论文 Identifier http://ir.sia.cn/handle/173321/22134 Collection 空间自动化技术研究室 Corresponding Author Liang X(梁宵) Affiliation 1.School of Automation, Shenyang Aerospace University, South DaoYi Street No.37, ShenBei New District, Shenyang, Liaoning 110136, China2.School of Automation, Northwestern Polytechnical University, Chang’an Campus of Northwest Polytechnical University, Dongxiang Street No.1, Chang’an District, Xi’an, Shanxi 710129, China3.Shenyang Institute of Automation, Chinese Academy of Sciences, Nanta Street No.114, Shenhe District, Shenyang, Liaoning 110016, China Recommended CitationGB/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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