Accelerating RRT* convergence with novel nonuniform and uniform sampling approach
Abstract Path planning plays a crucial role in autonomous mobile robotics. Sampling-based path planners are widely and frequently employed to generate collision-free paths between a start and goal location. Due to its asymptotic optimality, the optimal rapidly-exploring random tree (RRT*) algorithm...
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Nature Portfolio
2025-08-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-09992-y |
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| author | Sivasankar Ganesan Mohanraj Thangamuthu Balakrishnan Ramalingam Madan Mohan Rayguru Sethu Narayanan Tamilselvan |
| author_facet | Sivasankar Ganesan Mohanraj Thangamuthu Balakrishnan Ramalingam Madan Mohan Rayguru Sethu Narayanan Tamilselvan |
| author_sort | Sivasankar Ganesan |
| collection | DOAJ |
| description | Abstract Path planning plays a crucial role in autonomous mobile robotics. Sampling-based path planners are widely and frequently employed to generate collision-free paths between a start and goal location. Due to its asymptotic optimality, the optimal rapidly-exploring random tree (RRT*) algorithm is the most widely used among these. However, its reliance on uniform sampling often results in slow convergence. To address this issue, this work proposes a novel hybrid sampling method called RRT*-NUS (nonuniform–uniform sampler), which combines both uniform and nonuniform sampling to improve exploration efficiency. The proposed RRT*-NUS method is evaluated against six baseline algorithms: RRT*, Informed RRT*, RRT*-N (normal sampling RRT*), GS-RRT* (goal-oriented sampling RRT*), DR-RRT* (directional random sampling RRT*), and hybrid-RRT* in three different 384*384 2D simulation scenarios. The numerical simulation results indicate that the proposed RRT*-NUS surpasses the baseline RRT* algorithms in terms of planning time and convergence. It outperforms RRT* by 67.5% and Hybrid RRT* by 54% in time performance. Additionally, it achieves a convergence rate of 0.41 units/s, which is 3× faster than RRT* and almost 2× faster than Hybrid RRT*. |
| format | Article |
| id | doaj-art-0eb3ac0cff8d47268baf8355b1455c16 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-0eb3ac0cff8d47268baf8355b1455c162025-08-20T04:01:51ZengNature PortfolioScientific Reports2045-23222025-08-0115111810.1038/s41598-025-09992-yAccelerating RRT* convergence with novel nonuniform and uniform sampling approachSivasankar Ganesan0Mohanraj Thangamuthu1Balakrishnan Ramalingam2Madan Mohan Rayguru3Sethu Narayanan Tamilselvan4Department of Mechanical Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa VidyapeethamDepartment of Mechanical Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa VidyapeethamSchool of Electronics Engineering, Vellore Institute of TechnologyLouisville Automation and Robotics Research Institute, University of LouisvilleSingapore University of Technology and DesignAbstract Path planning plays a crucial role in autonomous mobile robotics. Sampling-based path planners are widely and frequently employed to generate collision-free paths between a start and goal location. Due to its asymptotic optimality, the optimal rapidly-exploring random tree (RRT*) algorithm is the most widely used among these. However, its reliance on uniform sampling often results in slow convergence. To address this issue, this work proposes a novel hybrid sampling method called RRT*-NUS (nonuniform–uniform sampler), which combines both uniform and nonuniform sampling to improve exploration efficiency. The proposed RRT*-NUS method is evaluated against six baseline algorithms: RRT*, Informed RRT*, RRT*-N (normal sampling RRT*), GS-RRT* (goal-oriented sampling RRT*), DR-RRT* (directional random sampling RRT*), and hybrid-RRT* in three different 384*384 2D simulation scenarios. The numerical simulation results indicate that the proposed RRT*-NUS surpasses the baseline RRT* algorithms in terms of planning time and convergence. It outperforms RRT* by 67.5% and Hybrid RRT* by 54% in time performance. Additionally, it achieves a convergence rate of 0.41 units/s, which is 3× faster than RRT* and almost 2× faster than Hybrid RRT*.https://doi.org/10.1038/s41598-025-09992-yNonuniform–uniform samplerRRT*Path planningNavigationAutonomous mobile robot |
| spellingShingle | Sivasankar Ganesan Mohanraj Thangamuthu Balakrishnan Ramalingam Madan Mohan Rayguru Sethu Narayanan Tamilselvan Accelerating RRT* convergence with novel nonuniform and uniform sampling approach Scientific Reports Nonuniform–uniform sampler RRT* Path planning Navigation Autonomous mobile robot |
| title | Accelerating RRT* convergence with novel nonuniform and uniform sampling approach |
| title_full | Accelerating RRT* convergence with novel nonuniform and uniform sampling approach |
| title_fullStr | Accelerating RRT* convergence with novel nonuniform and uniform sampling approach |
| title_full_unstemmed | Accelerating RRT* convergence with novel nonuniform and uniform sampling approach |
| title_short | Accelerating RRT* convergence with novel nonuniform and uniform sampling approach |
| title_sort | accelerating rrt convergence with novel nonuniform and uniform sampling approach |
| topic | Nonuniform–uniform sampler RRT* Path planning Navigation Autonomous mobile robot |
| url | https://doi.org/10.1038/s41598-025-09992-y |
| work_keys_str_mv | AT sivasankarganesan acceleratingrrtconvergencewithnovelnonuniformanduniformsamplingapproach AT mohanrajthangamuthu acceleratingrrtconvergencewithnovelnonuniformanduniformsamplingapproach AT balakrishnanramalingam acceleratingrrtconvergencewithnovelnonuniformanduniformsamplingapproach AT madanmohanrayguru acceleratingrrtconvergencewithnovelnonuniformanduniformsamplingapproach AT sethunarayanantamilselvan acceleratingrrtconvergencewithnovelnonuniformanduniformsamplingapproach |