A novel RRT*-Connect algorithm for path planning on robotic arm collision avoidance
Abstract To address the limitations of the original algorithm, several optimization techniques are proposed. This article presents an original RRT*-Connect algorithm for the planning of obstacle avoidance paths on robotic arms. These strategies include implementing a target biasing algorithm, using...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-87113-5 |
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| author | Miaolong Cao Huawei Mao Xiaohui Tang Yuzhou Sun Tiandong Chen |
| author_facet | Miaolong Cao Huawei Mao Xiaohui Tang Yuzhou Sun Tiandong Chen |
| author_sort | Miaolong Cao |
| collection | DOAJ |
| description | Abstract To address the limitations of the original algorithm, several optimization techniques are proposed. This article presents an original RRT*-Connect algorithm for the planning of obstacle avoidance paths on robotic arms. These strategies include implementing a target biasing algorithm, using elliptic space sampling to enhance the sampling process, the revision of the cost function to better guide path planning, and implementing an artificial potential field and gradient descent strategy to design adaptive step sizes. Furthermore, the use of segmented Bézier curves facilitates the generation of a more fluid trajectory when constructing the final path. The effectiveness of these augmentation strategies is corroborated by both simulations and experimental verification on a robotic arm. The simulations showed a 19.39% reduction in average run time and a 5% reduction in average path length compared to the existing RRT*-Connect algorithm. Therefore, The enhanced algorithm meets the requirement for optimal obstacle avoidance path planning by consistently finding the shortest path while avoiding obstacles. |
| format | Article |
| id | doaj-art-22a5657e97164aa1bc3bad5625273dfe |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-22a5657e97164aa1bc3bad5625273dfe2025-01-26T12:33:19ZengNature PortfolioScientific Reports2045-23222025-01-0115111910.1038/s41598-025-87113-5A novel RRT*-Connect algorithm for path planning on robotic arm collision avoidanceMiaolong Cao0Huawei Mao1Xiaohui Tang2Yuzhou Sun3Tiandong Chen4School of Mechanical and Energy Engineering, Zhejiang University of Science and TechnologySchool of Mechanical and Energy Engineering, Zhejiang University of Science and TechnologyZhejiang Ansheng Science & Technology Stock Co.,LtdSchool of Mechanical and Energy Engineering, Zhejiang University of Science and TechnologyZhejiang Ansheng Science & Technology Stock Co.,LtdAbstract To address the limitations of the original algorithm, several optimization techniques are proposed. This article presents an original RRT*-Connect algorithm for the planning of obstacle avoidance paths on robotic arms. These strategies include implementing a target biasing algorithm, using elliptic space sampling to enhance the sampling process, the revision of the cost function to better guide path planning, and implementing an artificial potential field and gradient descent strategy to design adaptive step sizes. Furthermore, the use of segmented Bézier curves facilitates the generation of a more fluid trajectory when constructing the final path. The effectiveness of these augmentation strategies is corroborated by both simulations and experimental verification on a robotic arm. The simulations showed a 19.39% reduction in average run time and a 5% reduction in average path length compared to the existing RRT*-Connect algorithm. Therefore, The enhanced algorithm meets the requirement for optimal obstacle avoidance path planning by consistently finding the shortest path while avoiding obstacles.https://doi.org/10.1038/s41598-025-87113-5Robotic armPath planningRRT algorithmSampling-based algorithms |
| spellingShingle | Miaolong Cao Huawei Mao Xiaohui Tang Yuzhou Sun Tiandong Chen A novel RRT*-Connect algorithm for path planning on robotic arm collision avoidance Scientific Reports Robotic arm Path planning RRT algorithm Sampling-based algorithms |
| title | A novel RRT*-Connect algorithm for path planning on robotic arm collision avoidance |
| title_full | A novel RRT*-Connect algorithm for path planning on robotic arm collision avoidance |
| title_fullStr | A novel RRT*-Connect algorithm for path planning on robotic arm collision avoidance |
| title_full_unstemmed | A novel RRT*-Connect algorithm for path planning on robotic arm collision avoidance |
| title_short | A novel RRT*-Connect algorithm for path planning on robotic arm collision avoidance |
| title_sort | novel rrt connect algorithm for path planning on robotic arm collision avoidance |
| topic | Robotic arm Path planning RRT algorithm Sampling-based algorithms |
| url | https://doi.org/10.1038/s41598-025-87113-5 |
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