Mobile Robot Path Planning Based on Kinematically Constrained A-Star Algorithm and DWA Fusion Algorithm
Path-planning research has been the key to mobile-robot-navigation technology. However, traditional path-planning algorithms have some shortcomings. To solve these problems, this paper proposes a fusion algorithm that combines the kinematical constrained A* algorithm with the Dynamic Window Approach...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/21/4552 |
| _version_ | 1797631594333208576 |
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| author | Yanjie Liu Chao Wang Heng Wu Yanlong Wei |
| author_facet | Yanjie Liu Chao Wang Heng Wu Yanlong Wei |
| author_sort | Yanjie Liu |
| collection | DOAJ |
| description | Path-planning research has been the key to mobile-robot-navigation technology. However, traditional path-planning algorithms have some shortcomings. To solve these problems, this paper proposes a fusion algorithm that combines the kinematical constrained A* algorithm with the Dynamic Window Approach (DWA) algorithm. The kinematical constrained A* algorithm can plan the global path, and then the DWA algorithm can plan the local path under the global path’s guidance. Firstly, combined with robot kinematics, we improve the node-expansion method and heuristic-function model of the A* algorithm, which improves the search efficiency, reduces the number of path bends and lowers the computational cost so that the path generated by the A* algorithm better meets the needs of robot motion. Secondly, we optimize the trajectory-evaluation function of the DWA algorithm so that the local paths planned by the DWA algorithm are smoother and more coherent, which is easier for robot-motion execution. Finally, we extract the key nodes from the global path planned by the A* algorithm to guide the DWA algorithm for local path planning and dynamic-obstacle avoidance and to make the local path closer to the global path. Through simulation and practical experiments, the effectiveness of the fusion algorithm proposed in this paper is verified. |
| first_indexed | 2024-03-11T11:25:40Z |
| format | Article |
| id | doaj.art-be7e0470040c42e5ab1a642f18b87974 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-11T11:25:40Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-be7e0470040c42e5ab1a642f18b879742023-11-10T15:08:16ZengMDPI AGMathematics2227-73902023-11-011121455210.3390/math11214552Mobile Robot Path Planning Based on Kinematically Constrained A-Star Algorithm and DWA Fusion AlgorithmYanjie Liu0Chao Wang1Heng Wu2Yanlong Wei3State Key Laboratory of Robotics and System, Department of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, ChinaState Key Laboratory of Robotics and System, Department of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, ChinaState Key Laboratory of Robotics and System, Department of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, ChinaState Key Laboratory of Robotics and System, Department of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, ChinaPath-planning research has been the key to mobile-robot-navigation technology. However, traditional path-planning algorithms have some shortcomings. To solve these problems, this paper proposes a fusion algorithm that combines the kinematical constrained A* algorithm with the Dynamic Window Approach (DWA) algorithm. The kinematical constrained A* algorithm can plan the global path, and then the DWA algorithm can plan the local path under the global path’s guidance. Firstly, combined with robot kinematics, we improve the node-expansion method and heuristic-function model of the A* algorithm, which improves the search efficiency, reduces the number of path bends and lowers the computational cost so that the path generated by the A* algorithm better meets the needs of robot motion. Secondly, we optimize the trajectory-evaluation function of the DWA algorithm so that the local paths planned by the DWA algorithm are smoother and more coherent, which is easier for robot-motion execution. Finally, we extract the key nodes from the global path planned by the A* algorithm to guide the DWA algorithm for local path planning and dynamic-obstacle avoidance and to make the local path closer to the global path. Through simulation and practical experiments, the effectiveness of the fusion algorithm proposed in this paper is verified.https://www.mdpi.com/2227-7390/11/21/4552path planningmobile robotA-starDWApath generation |
| spellingShingle | Yanjie Liu Chao Wang Heng Wu Yanlong Wei Mobile Robot Path Planning Based on Kinematically Constrained A-Star Algorithm and DWA Fusion Algorithm Mathematics path planning mobile robot A-star DWA path generation |
| title | Mobile Robot Path Planning Based on Kinematically Constrained A-Star Algorithm and DWA Fusion Algorithm |
| title_full | Mobile Robot Path Planning Based on Kinematically Constrained A-Star Algorithm and DWA Fusion Algorithm |
| title_fullStr | Mobile Robot Path Planning Based on Kinematically Constrained A-Star Algorithm and DWA Fusion Algorithm |
| title_full_unstemmed | Mobile Robot Path Planning Based on Kinematically Constrained A-Star Algorithm and DWA Fusion Algorithm |
| title_short | Mobile Robot Path Planning Based on Kinematically Constrained A-Star Algorithm and DWA Fusion Algorithm |
| title_sort | mobile robot path planning based on kinematically constrained a star algorithm and dwa fusion algorithm |
| topic | path planning mobile robot A-star DWA path generation |
| url | https://www.mdpi.com/2227-7390/11/21/4552 |
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