Path Planning Algorithm for Manipulators in Complex Scenes Based on Improved RRT*
Aiming at the problems of a six-degree-of-freedom robotic arm in a three-dimensional multi-obstacle space, such as low sampling efficiency and path search failure, an improved fast extended random tree (RRT*) algorithm for robotic arm path planning method (abbreviated as HP-APF-RRT*) is proposed. Th...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/2/328 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832587607432757248 |
|---|---|
| author | Xiqing Zhang Pengyu Wang Yongrui Guo Qianqian Han Kuoran Zhang |
| author_facet | Xiqing Zhang Pengyu Wang Yongrui Guo Qianqian Han Kuoran Zhang |
| author_sort | Xiqing Zhang |
| collection | DOAJ |
| description | Aiming at the problems of a six-degree-of-freedom robotic arm in a three-dimensional multi-obstacle space, such as low sampling efficiency and path search failure, an improved fast extended random tree (RRT*) algorithm for robotic arm path planning method (abbreviated as HP-APF-RRT*) is proposed. The algorithm generates multiple candidate points per iteration, selecting a sampling point probabilistically based on heuristic values, thereby optimizing sampling efficiency and reducing unnecessary nodes. To mitigate increased search times in obstacle-dense areas, an artificial potential field (APF) approach is integrated, establishing gravitational and repulsive fields to guide sampling points around obstacles toward the target. This method enhances path search in complex environments, yielding near-optimal paths. Furthermore, the path is simplified using the triangle inequality, and redundant intermediate nodes are utilized to further refine the path. Finally, the simulation experiment of the improved HP-APF-RRT* is executed on Matlab R2022b and ROS, and the physical experiment is performed on the NZ500-500 robotic arm. The effectiveness and superiority of the improved algorithm are determined by comparing it with the existing algorithms. |
| format | Article |
| id | doaj-art-eec0b07750bc4fd5a098bc31a2739fa2 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-eec0b07750bc4fd5a098bc31a2739fa22025-01-24T13:48:32ZengMDPI AGSensors1424-82202025-01-0125232810.3390/s25020328Path Planning Algorithm for Manipulators in Complex Scenes Based on Improved RRT*Xiqing Zhang0Pengyu Wang1Yongrui Guo2Qianqian Han3Kuoran Zhang4School of Vehicle and Transportation Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, ChinaSchool of Vehicle and Transportation Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, ChinaSchool of Vehicle and Transportation Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, ChinaSchool of Vehicle and Transportation Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, ChinaSchool of Vehicle and Transportation Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, ChinaAiming at the problems of a six-degree-of-freedom robotic arm in a three-dimensional multi-obstacle space, such as low sampling efficiency and path search failure, an improved fast extended random tree (RRT*) algorithm for robotic arm path planning method (abbreviated as HP-APF-RRT*) is proposed. The algorithm generates multiple candidate points per iteration, selecting a sampling point probabilistically based on heuristic values, thereby optimizing sampling efficiency and reducing unnecessary nodes. To mitigate increased search times in obstacle-dense areas, an artificial potential field (APF) approach is integrated, establishing gravitational and repulsive fields to guide sampling points around obstacles toward the target. This method enhances path search in complex environments, yielding near-optimal paths. Furthermore, the path is simplified using the triangle inequality, and redundant intermediate nodes are utilized to further refine the path. Finally, the simulation experiment of the improved HP-APF-RRT* is executed on Matlab R2022b and ROS, and the physical experiment is performed on the NZ500-500 robotic arm. The effectiveness and superiority of the improved algorithm are determined by comparing it with the existing algorithms.https://www.mdpi.com/1424-8220/25/2/328six-degree-of-freedom robotic armpath planningRRT*APFtriangular inequalities |
| spellingShingle | Xiqing Zhang Pengyu Wang Yongrui Guo Qianqian Han Kuoran Zhang Path Planning Algorithm for Manipulators in Complex Scenes Based on Improved RRT* Sensors six-degree-of-freedom robotic arm path planning RRT* APF triangular inequalities |
| title | Path Planning Algorithm for Manipulators in Complex Scenes Based on Improved RRT* |
| title_full | Path Planning Algorithm for Manipulators in Complex Scenes Based on Improved RRT* |
| title_fullStr | Path Planning Algorithm for Manipulators in Complex Scenes Based on Improved RRT* |
| title_full_unstemmed | Path Planning Algorithm for Manipulators in Complex Scenes Based on Improved RRT* |
| title_short | Path Planning Algorithm for Manipulators in Complex Scenes Based on Improved RRT* |
| title_sort | path planning algorithm for manipulators in complex scenes based on improved rrt |
| topic | six-degree-of-freedom robotic arm path planning RRT* APF triangular inequalities |
| url | https://www.mdpi.com/1424-8220/25/2/328 |
| work_keys_str_mv | AT xiqingzhang pathplanningalgorithmformanipulatorsincomplexscenesbasedonimprovedrrt AT pengyuwang pathplanningalgorithmformanipulatorsincomplexscenesbasedonimprovedrrt AT yongruiguo pathplanningalgorithmformanipulatorsincomplexscenesbasedonimprovedrrt AT qianqianhan pathplanningalgorithmformanipulatorsincomplexscenesbasedonimprovedrrt AT kuoranzhang pathplanningalgorithmformanipulatorsincomplexscenesbasedonimprovedrrt |