Unmanned Aerial Vehicle Path-Planning Method Based on Improved P-RRT* Algorithm
This paper proposed an improved potential rapidly exploring random tree star (P-RRT*) algorithm for unmanned aerial vehicles (UAV). The algorithm has faster expansion and convergence speeds and better path quality. Path planning is an important part of the UAV control system. Rapidly exploring rando...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/22/4576 |
| _version_ | 1797459531451596800 |
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| author | Xing Xu Feifan Zhang Yun Zhao |
| author_facet | Xing Xu Feifan Zhang Yun Zhao |
| author_sort | Xing Xu |
| collection | DOAJ |
| description | This paper proposed an improved potential rapidly exploring random tree star (P-RRT*) algorithm for unmanned aerial vehicles (UAV). The algorithm has faster expansion and convergence speeds and better path quality. Path planning is an important part of the UAV control system. Rapidly exploring random tree (RRT) is a path-planning algorithm that is widely used, including in UAV, and its altered body, P-RRT*, is an asymptotic optimal algorithm with bias sampling. The algorithm converges slowly and has a large random sampling area. To overcome the above drawbacks, we made the following improvements. First, the algorithm used the direction of the artificial potential field (APF) to determine whether to perform greedy expansion, increasing the search efficiency. Second, as the random tree obtained the initial path and updated the path cost, the algorithm rejected high-cost nodes and sampling points based on the heuristic cost and current path cost to speed up the convergence rate. Then, the random tree was pruned to remove the redundant nodes in the path. The simulation results demonstrated that the proposed algorithm could significantly decrease the path cost and inflection points, speed up initial path obtaining and convergence, and is suitable for the path planning of UAVs. |
| first_indexed | 2024-03-09T16:52:42Z |
| format | Article |
| id | doaj.art-59d7155b203e4cf695a11659eed7905e |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T16:52:42Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-59d7155b203e4cf695a11659eed7905e2023-11-24T14:39:03ZengMDPI AGElectronics2079-92922023-11-011222457610.3390/electronics12224576Unmanned Aerial Vehicle Path-Planning Method Based on Improved P-RRT* AlgorithmXing Xu0Feifan Zhang1Yun Zhao2School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, ChinaSchool of Mechanical and Energy Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, ChinaSchool of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, ChinaThis paper proposed an improved potential rapidly exploring random tree star (P-RRT*) algorithm for unmanned aerial vehicles (UAV). The algorithm has faster expansion and convergence speeds and better path quality. Path planning is an important part of the UAV control system. Rapidly exploring random tree (RRT) is a path-planning algorithm that is widely used, including in UAV, and its altered body, P-RRT*, is an asymptotic optimal algorithm with bias sampling. The algorithm converges slowly and has a large random sampling area. To overcome the above drawbacks, we made the following improvements. First, the algorithm used the direction of the artificial potential field (APF) to determine whether to perform greedy expansion, increasing the search efficiency. Second, as the random tree obtained the initial path and updated the path cost, the algorithm rejected high-cost nodes and sampling points based on the heuristic cost and current path cost to speed up the convergence rate. Then, the random tree was pruned to remove the redundant nodes in the path. The simulation results demonstrated that the proposed algorithm could significantly decrease the path cost and inflection points, speed up initial path obtaining and convergence, and is suitable for the path planning of UAVs.https://www.mdpi.com/2079-9292/12/22/4576path planningRRT*artificial potential fieldgreedy strategyhigh-cost rejection |
| spellingShingle | Xing Xu Feifan Zhang Yun Zhao Unmanned Aerial Vehicle Path-Planning Method Based on Improved P-RRT* Algorithm Electronics path planning RRT* artificial potential field greedy strategy high-cost rejection |
| title | Unmanned Aerial Vehicle Path-Planning Method Based on Improved P-RRT* Algorithm |
| title_full | Unmanned Aerial Vehicle Path-Planning Method Based on Improved P-RRT* Algorithm |
| title_fullStr | Unmanned Aerial Vehicle Path-Planning Method Based on Improved P-RRT* Algorithm |
| title_full_unstemmed | Unmanned Aerial Vehicle Path-Planning Method Based on Improved P-RRT* Algorithm |
| title_short | Unmanned Aerial Vehicle Path-Planning Method Based on Improved P-RRT* Algorithm |
| title_sort | unmanned aerial vehicle path planning method based on improved p rrt algorithm |
| topic | path planning RRT* artificial potential field greedy strategy high-cost rejection |
| url | https://www.mdpi.com/2079-9292/12/22/4576 |
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