Density gradient-RRT: An improved rapidly exploring random tree algorithm for UAV path planning
In-depth studies of algorithms for solving motion planning problems have been conducted due to the rapid popularization and development of unmanned aerial vehicles in previous decades. Among them, the classic rapidly exploring random tree (RRT) algorithm has derivative algorithms (e.g., RRT*, Q-RRT*...
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| Veröffentlicht in: | Expert systems with applications Jg. 252; S. 124121 |
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| Sprache: | Englisch |
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| Abstract | In-depth studies of algorithms for solving motion planning problems have been conducted due to the rapid popularization and development of unmanned aerial vehicles in previous decades. Among them, the classic rapidly exploring random tree (RRT) algorithm has derivative algorithms (e.g., RRT*, Q-RRT*, and F-RRT*) that focus on the optimal path cost of the initial solution. Other improved algorithms, such as RRT-connect and BG-RRT, focus on the optimal time of the initial solution. This article proposes an improved density gradient-RRT (DG-RRT) algorithm based on RRT that improves the efficiency of the guide point and reduces the time lost in the process of obtaining the initial solution through the dynamic gradient sampling strategy. Simultaneously, it reduces the path cost by reconstructing the output path. The proposed algorithm is an expansion algorithm of a random tree, and the performance of the algorithm can be further improved by combining it with other RRT optimization algorithms. DG-RRT and other algorithms are compared in different environments through simulation experiments to verify the advantages of DG-RRT. In addition, it used a set of simulation flight tests to verify the feasibility of the DG-RRT algorithm for UAV path planning. |
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| AbstractList | In-depth studies of algorithms for solving motion planning problems have been conducted due to the rapid popularization and development of unmanned aerial vehicles in previous decades. Among them, the classic rapidly exploring random tree (RRT) algorithm has derivative algorithms (e.g., RRT*, Q-RRT*, and F-RRT*) that focus on the optimal path cost of the initial solution. Other improved algorithms, such as RRT-connect and BG-RRT, focus on the optimal time of the initial solution. This article proposes an improved density gradient-RRT (DG-RRT) algorithm based on RRT that improves the efficiency of the guide point and reduces the time lost in the process of obtaining the initial solution through the dynamic gradient sampling strategy. Simultaneously, it reduces the path cost by reconstructing the output path. The proposed algorithm is an expansion algorithm of a random tree, and the performance of the algorithm can be further improved by combining it with other RRT optimization algorithms. DG-RRT and other algorithms are compared in different environments through simulation experiments to verify the advantages of DG-RRT. In addition, it used a set of simulation flight tests to verify the feasibility of the DG-RRT algorithm for UAV path planning. |
| ArticleNumber | 124121 |
| Author | Huang, Tai Fan, Kuangang Sun, Wen |
| Author_xml | – sequence: 1 givenname: Tai orcidid: 0000-0003-4412-573X surname: Huang fullname: Huang, Tai email: supguangguang_71@163.com organization: School of Mechanical and Electrical Engineering, Jiangxi University of Science and Technology, Hongqi Street No. 86, Ganzhou 34100, China – sequence: 2 givenname: Kuangang orcidid: 0000-0001-9793-2098 surname: Fan fullname: Fan, Kuangang email: fankuangang@jxust.edu.cn organization: Magnetic Suspension Technology Key Laboratory of Jiangxi Province, Jiangxi University of Science and Technology, Hongqi Street No. 86, Ganzhou 34100, China – sequence: 3 givenname: Wen orcidid: 0000-0001-8806-601X surname: Sun fullname: Sun, Wen email: SunWen270019@163.com organization: School of Mechanical and Electrical Engineering, Jiangxi University of Science and Technology, Hongqi Street No. 86, Ganzhou 34100, China |
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| Cites_doi | 10.1016/j.ast.2021.107004 10.1109/TSSC.1968.300136 10.1016/j.eswa.2021.115457 10.1016/j.comcom.2020.02.043 10.3390/drones7050331 10.1016/j.isatra.2022.07.032 10.1109/TSP.2020.3015276 10.3390/ijgi11020112 10.1109/ACCESS.2019.2908100 10.1109/70.660866 10.1177/027836498600500106 10.1016/j.eswa.2015.02.033 10.1007/s10514-021-10012-x 10.1016/j.eswa.2020.113425 10.1177/0278364911406761 |
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