A Vehicle Path Planning Algorithm: QDDG-RRT
Autonomous vehicles require highly reliable collision-free capabilities, necessitating extensive research in path planning. Path planning determines an optimal path, crucial for safe and efficient driving. The Rapidly-exploring Random Tree (RRT) algorithm, while widely used, suffers from slow search...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11007093/ |
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| author | Ruixin Zhang Qing Xu Kai Sun Yi Liu Ximming Zhu Guo Zhang Xiang Cheng |
| author_facet | Ruixin Zhang Qing Xu Kai Sun Yi Liu Ximming Zhu Guo Zhang Xiang Cheng |
| author_sort | Ruixin Zhang |
| collection | DOAJ |
| description | Autonomous vehicles require highly reliable collision-free capabilities, necessitating extensive research in path planning. Path planning determines an optimal path, crucial for safe and efficient driving. The Rapidly-exploring Random Tree (RRT) algorithm, while widely used, suffers from slow search speeds, numerous inflection points, and redundant operations. To address these issues, We propose a global path planning algorithm: Quick Dynamic Directional Guidance-RRT (QDDG-RRT) algorithm. Key improvements include dynamically constraining the search space using a direction guidance strategy, employing steering techniques to avoid obstacles, optimizing path length to minimize global cost, and refining trajectories using second-order Bessel curves. Simulation experiments compare QDDG-RRT with P-RRT(Probabilistic Rapidly-exploring Random Tree), P-RRT*, APF(Artificial Potential Field), and A* algorithms. Results show that QDDG-RRT outperforms others in execution speed, path length, and smoothness, effectively avoiding obstacles and maintaining safe distances in complex environments. |
| format | Article |
| id | doaj-art-73cadb79f1154b3daddf0237e6d0f81e |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-73cadb79f1154b3daddf0237e6d0f81e2025-08-20T03:21:33ZengIEEEIEEE Access2169-35362025-01-0113902129022210.1109/ACCESS.2025.357145311007093A Vehicle Path Planning Algorithm: QDDG-RRTRuixin Zhang0https://orcid.org/0009-0000-4622-4462Qing Xu1Kai Sun2https://orcid.org/0009-0007-6983-6112Yi Liu3https://orcid.org/0009-0005-7726-9811Ximming Zhu4https://orcid.org/0009-0002-6061-9393Guo Zhang5https://orcid.org/0000-0002-3987-5336Xiang Cheng6Geospatial Information Institute, Information Engineering University, Zhengzhou, ChinaGeospatial Information Institute, Information Engineering University, Zhengzhou, ChinaGeospatial Information Institute, Information Engineering University, Zhengzhou, ChinaGeospatial Information Institute, Information Engineering University, Zhengzhou, ChinaGeospatial Information Institute, Information Engineering University, Zhengzhou, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, ChinaUnit 66069, Luoyang, ChinaAutonomous vehicles require highly reliable collision-free capabilities, necessitating extensive research in path planning. Path planning determines an optimal path, crucial for safe and efficient driving. The Rapidly-exploring Random Tree (RRT) algorithm, while widely used, suffers from slow search speeds, numerous inflection points, and redundant operations. To address these issues, We propose a global path planning algorithm: Quick Dynamic Directional Guidance-RRT (QDDG-RRT) algorithm. Key improvements include dynamically constraining the search space using a direction guidance strategy, employing steering techniques to avoid obstacles, optimizing path length to minimize global cost, and refining trajectories using second-order Bessel curves. Simulation experiments compare QDDG-RRT with P-RRT(Probabilistic Rapidly-exploring Random Tree), P-RRT*, APF(Artificial Potential Field), and A* algorithms. Results show that QDDG-RRT outperforms others in execution speed, path length, and smoothness, effectively avoiding obstacles and maintaining safe distances in complex environments.https://ieeexplore.ieee.org/document/11007093/RRTdirection guidedirectional rotationpath cost minimizationBessel curve |
| spellingShingle | Ruixin Zhang Qing Xu Kai Sun Yi Liu Ximming Zhu Guo Zhang Xiang Cheng A Vehicle Path Planning Algorithm: QDDG-RRT IEEE Access RRT direction guide directional rotation path cost minimization Bessel curve |
| title | A Vehicle Path Planning Algorithm: QDDG-RRT |
| title_full | A Vehicle Path Planning Algorithm: QDDG-RRT |
| title_fullStr | A Vehicle Path Planning Algorithm: QDDG-RRT |
| title_full_unstemmed | A Vehicle Path Planning Algorithm: QDDG-RRT |
| title_short | A Vehicle Path Planning Algorithm: QDDG-RRT |
| title_sort | vehicle path planning algorithm qddg rrt |
| topic | RRT direction guide directional rotation path cost minimization Bessel curve |
| url | https://ieeexplore.ieee.org/document/11007093/ |
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