FRRT*-Connect: A Bidirectional Sampling-Based Path Planner with Potential Field Guidance for Complex Obstacle Environments
This paper addresses the path planning problem in high-dimensional complex environments and proposes an improved FRRT*-Connect algorithm to enhance the efficiency, precision, and robustness of path generation. The algorithm first introduces a goal-directed attractive force control mechanism, integra...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/9/2761 |
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| _version_ | 1850032186689847296 |
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| author | Wenshan Yan Xiangrong Xu Aleksandar Rodić Petar B. Petrovich |
| author_facet | Wenshan Yan Xiangrong Xu Aleksandar Rodić Petar B. Petrovich |
| author_sort | Wenshan Yan |
| collection | DOAJ |
| description | This paper addresses the path planning problem in high-dimensional complex environments and proposes an improved FRRT*-Connect algorithm to enhance the efficiency, precision, and robustness of path generation. The algorithm first introduces a goal-directed attractive force control mechanism, integrating artificial potential field methods to guide the tree expansion more effectively toward the goal, thereby reducing redundant sampling and significantly improving convergence speed. Secondly, an adaptive step-size strategy is proposed, dynamically adjusting the tree expansion step size based on the complexity of the environment, which enhances the algorithm’s adaptability in narrow passages and complex topological structures, effectively avoiding local minima. The results show that, compared to the RRT*-Connect algorithm, the proposed method exhibits significant advantages in path quality, convergence efficiency, and success rate: the average path length is reduced by 19.7%, convergence speed is improved by 58.4%, and the success rate reaches 98% in narrow passage scenarios. These improvements effectively overcome the issues of path redundancy, slow convergence, and local minima inherent in traditional RRT-based algorithms, demonstrating superior performance in challenging scenarios with complex obstacles and narrow passages. |
| format | Article |
| id | doaj-art-5f45ba57faac4d8fb0c4727ef43f1e82 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-5f45ba57faac4d8fb0c4727ef43f1e822025-08-20T02:58:44ZengMDPI AGSensors1424-82202025-04-01259276110.3390/s25092761FRRT*-Connect: A Bidirectional Sampling-Based Path Planner with Potential Field Guidance for Complex Obstacle EnvironmentsWenshan Yan0Xiangrong Xu1Aleksandar Rodić2Petar B. Petrovich3School of Mechanical Engineering, Anhui University of Technology, Ma’anshan 243032, ChinaSchool of Mechanical Engineering, Anhui University of Technology, Ma’anshan 243032, ChinaFaculty of Mechanical Engineering, University of Belgrade, 11120 Belgrade, SerbiaFaculty of Mechanical Engineering, University of Belgrade, 11120 Belgrade, SerbiaThis paper addresses the path planning problem in high-dimensional complex environments and proposes an improved FRRT*-Connect algorithm to enhance the efficiency, precision, and robustness of path generation. The algorithm first introduces a goal-directed attractive force control mechanism, integrating artificial potential field methods to guide the tree expansion more effectively toward the goal, thereby reducing redundant sampling and significantly improving convergence speed. Secondly, an adaptive step-size strategy is proposed, dynamically adjusting the tree expansion step size based on the complexity of the environment, which enhances the algorithm’s adaptability in narrow passages and complex topological structures, effectively avoiding local minima. The results show that, compared to the RRT*-Connect algorithm, the proposed method exhibits significant advantages in path quality, convergence efficiency, and success rate: the average path length is reduced by 19.7%, convergence speed is improved by 58.4%, and the success rate reaches 98% in narrow passage scenarios. These improvements effectively overcome the issues of path redundancy, slow convergence, and local minima inherent in traditional RRT-based algorithms, demonstrating superior performance in challenging scenarios with complex obstacles and narrow passages.https://www.mdpi.com/1424-8220/25/9/2761robot path planningrapid exploration of random treesRRT*-Connectartificial potential fieldspath optimization |
| spellingShingle | Wenshan Yan Xiangrong Xu Aleksandar Rodić Petar B. Petrovich FRRT*-Connect: A Bidirectional Sampling-Based Path Planner with Potential Field Guidance for Complex Obstacle Environments Sensors robot path planning rapid exploration of random trees RRT*-Connect artificial potential fields path optimization |
| title | FRRT*-Connect: A Bidirectional Sampling-Based Path Planner with Potential Field Guidance for Complex Obstacle Environments |
| title_full | FRRT*-Connect: A Bidirectional Sampling-Based Path Planner with Potential Field Guidance for Complex Obstacle Environments |
| title_fullStr | FRRT*-Connect: A Bidirectional Sampling-Based Path Planner with Potential Field Guidance for Complex Obstacle Environments |
| title_full_unstemmed | FRRT*-Connect: A Bidirectional Sampling-Based Path Planner with Potential Field Guidance for Complex Obstacle Environments |
| title_short | FRRT*-Connect: A Bidirectional Sampling-Based Path Planner with Potential Field Guidance for Complex Obstacle Environments |
| title_sort | frrt connect a bidirectional sampling based path planner with potential field guidance for complex obstacle environments |
| topic | robot path planning rapid exploration of random trees RRT*-Connect artificial potential fields path optimization |
| url | https://www.mdpi.com/1424-8220/25/9/2761 |
| work_keys_str_mv | AT wenshanyan frrtconnectabidirectionalsamplingbasedpathplannerwithpotentialfieldguidanceforcomplexobstacleenvironments AT xiangrongxu frrtconnectabidirectionalsamplingbasedpathplannerwithpotentialfieldguidanceforcomplexobstacleenvironments AT aleksandarrodic frrtconnectabidirectionalsamplingbasedpathplannerwithpotentialfieldguidanceforcomplexobstacleenvironments AT petarbpetrovich frrtconnectabidirectionalsamplingbasedpathplannerwithpotentialfieldguidanceforcomplexobstacleenvironments |