Autonomous Robotic Path Planning Based on the Gaussian Mixture Model in Complex Manufacturing Environment
In the automotive manufacturing process, industrial robot path planning, which relies on offline programming by engineers, is usually time-consuming and difficult to migrate. To solve this problem, this paper proposes a path segment directed evolution algorithm (PSDEA) based on the Gaussian mixture...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10794758/ |
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| author | Rui Sun Yuanmin Wang Wenzheng Zhao Yinhua Liu |
| author_facet | Rui Sun Yuanmin Wang Wenzheng Zhao Yinhua Liu |
| author_sort | Rui Sun |
| collection | DOAJ |
| description | In the automotive manufacturing process, industrial robot path planning, which relies on offline programming by engineers, is usually time-consuming and difficult to migrate. To solve this problem, this paper proposes a path segment directed evolution algorithm (PSDEA) based on the Gaussian mixture model and a heuristic optimization algorithm. First, in the narrow and complex manufacturing environment, the Gaussian mixture model based on obstacles is used to calculate the collision probability of the robot arm in different poses. Secondly, the initial path is determined using the path generation configuration library, and a segmented path fitness function is introduced to evaluate the quality of the path quantitatively. Then, a directed evolution strategy is proposed to improve the genetic algorithm and achieve precise guidance of the path evolution direction; finally, the effectiveness of the proposed method is verified through simulation experiments and real scenarios. The results show that compared with the benchmark methods, the proposed method can improve the planning efficiency, reduce the path length, and be more robust. Therefore, the method proposed in this paper can quickly generate high-quality industrial robot terminal motion trajectories in complex manufacturing environments. |
| format | Article |
| id | doaj-art-641d1f03e0184cf3b1ea80b38e78e25d |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-641d1f03e0184cf3b1ea80b38e78e25d2025-08-20T02:49:09ZengIEEEIEEE Access2169-35362024-01-011218839818841010.1109/ACCESS.2024.351605610794758Autonomous Robotic Path Planning Based on the Gaussian Mixture Model in Complex Manufacturing EnvironmentRui Sun0https://orcid.org/0009-0009-1007-3055Yuanmin Wang1Wenzheng Zhao2Yinhua Liu3https://orcid.org/0000-0002-1370-0300School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, ChinaSchool of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, ChinaSchool of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, ChinaSchool of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, ChinaIn the automotive manufacturing process, industrial robot path planning, which relies on offline programming by engineers, is usually time-consuming and difficult to migrate. To solve this problem, this paper proposes a path segment directed evolution algorithm (PSDEA) based on the Gaussian mixture model and a heuristic optimization algorithm. First, in the narrow and complex manufacturing environment, the Gaussian mixture model based on obstacles is used to calculate the collision probability of the robot arm in different poses. Secondly, the initial path is determined using the path generation configuration library, and a segmented path fitness function is introduced to evaluate the quality of the path quantitatively. Then, a directed evolution strategy is proposed to improve the genetic algorithm and achieve precise guidance of the path evolution direction; finally, the effectiveness of the proposed method is verified through simulation experiments and real scenarios. The results show that compared with the benchmark methods, the proposed method can improve the planning efficiency, reduce the path length, and be more robust. Therefore, the method proposed in this paper can quickly generate high-quality industrial robot terminal motion trajectories in complex manufacturing environments.https://ieeexplore.ieee.org/document/10794758/Manipulatorpath planninggenetic algorithmsGaussian mixture modelcollision probability |
| spellingShingle | Rui Sun Yuanmin Wang Wenzheng Zhao Yinhua Liu Autonomous Robotic Path Planning Based on the Gaussian Mixture Model in Complex Manufacturing Environment IEEE Access Manipulator path planning genetic algorithms Gaussian mixture model collision probability |
| title | Autonomous Robotic Path Planning Based on the Gaussian Mixture Model in Complex Manufacturing Environment |
| title_full | Autonomous Robotic Path Planning Based on the Gaussian Mixture Model in Complex Manufacturing Environment |
| title_fullStr | Autonomous Robotic Path Planning Based on the Gaussian Mixture Model in Complex Manufacturing Environment |
| title_full_unstemmed | Autonomous Robotic Path Planning Based on the Gaussian Mixture Model in Complex Manufacturing Environment |
| title_short | Autonomous Robotic Path Planning Based on the Gaussian Mixture Model in Complex Manufacturing Environment |
| title_sort | autonomous robotic path planning based on the gaussian mixture model in complex manufacturing environment |
| topic | Manipulator path planning genetic algorithms Gaussian mixture model collision probability |
| url | https://ieeexplore.ieee.org/document/10794758/ |
| work_keys_str_mv | AT ruisun autonomousroboticpathplanningbasedonthegaussianmixturemodelincomplexmanufacturingenvironment AT yuanminwang autonomousroboticpathplanningbasedonthegaussianmixturemodelincomplexmanufacturingenvironment AT wenzhengzhao autonomousroboticpathplanningbasedonthegaussianmixturemodelincomplexmanufacturingenvironment AT yinhualiu autonomousroboticpathplanningbasedonthegaussianmixturemodelincomplexmanufacturingenvironment |