A Gradient-Based Recurrent Neural Network for Visual Servoing of Robot Manipulators with Acceleration Command
Recent decades have witnessed the rapid evolution of robotic applications and their expansion into a variety of spheres with remarkable achievements. This article researches a crucial technique of robot manipulators referred to as visual servoing, which relies on the visual feedback to respond to th...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/2305459 |
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| _version_ | 1849405168512139264 |
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| author | Zhiguan Huang Zhengtai Xie Long Jin Yuhe Li |
| author_facet | Zhiguan Huang Zhengtai Xie Long Jin Yuhe Li |
| author_sort | Zhiguan Huang |
| collection | DOAJ |
| description | Recent decades have witnessed the rapid evolution of robotic applications and their expansion into a variety of spheres with remarkable achievements. This article researches a crucial technique of robot manipulators referred to as visual servoing, which relies on the visual feedback to respond to the external information. In this regard, the visual servoing issue is tactfully transformed into a quadratic programming problem with equality and inequality constraints. Differing from the traditional methods, a gradient-based recurrent neural network (GRNN) for solving the visual servoing issue is newly proposed in this article in the light of the gradient descent method. Then, the stability proof is presented in theory with the pixel error convergent exponentially to zero. Specifically speaking, the proposed method is able to impel the manipulator to approach the desired static point while maintaining physical constraints considered. After that, the feasibility and superiority of the proposed GRNN are verified by simulative experiments. Significantly, the proposed visual servo method can be leveraged to medical robots and rehabilitation robots to further assist doctors in treating patients remotely. |
| format | Article |
| id | doaj-art-44c1cc75ff8f4a8faf62754d736a04cb |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-44c1cc75ff8f4a8faf62754d736a04cb2025-08-20T03:36:45ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/23054592305459A Gradient-Based Recurrent Neural Network for Visual Servoing of Robot Manipulators with Acceleration CommandZhiguan Huang0Zhengtai Xie1Long Jin2Yuhe Li3Guangdong Provincial Engineering Technology Research Center for Sports Assistive Devices, Guangzhou Sport University, Guangzhou, ChinaSchool of Information Science and Engineering, Lanzhou University, Lanzhou, ChinaGuangdong Provincial Engineering Technology Research Center for Sports Assistive Devices, Guangzhou Sport University, Guangzhou, ChinaGuangdong Provincial Engineering Technology Research Center for Sports Assistive Devices, Guangzhou Sport University, Guangzhou, ChinaRecent decades have witnessed the rapid evolution of robotic applications and their expansion into a variety of spheres with remarkable achievements. This article researches a crucial technique of robot manipulators referred to as visual servoing, which relies on the visual feedback to respond to the external information. In this regard, the visual servoing issue is tactfully transformed into a quadratic programming problem with equality and inequality constraints. Differing from the traditional methods, a gradient-based recurrent neural network (GRNN) for solving the visual servoing issue is newly proposed in this article in the light of the gradient descent method. Then, the stability proof is presented in theory with the pixel error convergent exponentially to zero. Specifically speaking, the proposed method is able to impel the manipulator to approach the desired static point while maintaining physical constraints considered. After that, the feasibility and superiority of the proposed GRNN are verified by simulative experiments. Significantly, the proposed visual servo method can be leveraged to medical robots and rehabilitation robots to further assist doctors in treating patients remotely.http://dx.doi.org/10.1155/2020/2305459 |
| spellingShingle | Zhiguan Huang Zhengtai Xie Long Jin Yuhe Li A Gradient-Based Recurrent Neural Network for Visual Servoing of Robot Manipulators with Acceleration Command Complexity |
| title | A Gradient-Based Recurrent Neural Network for Visual Servoing of Robot Manipulators with Acceleration Command |
| title_full | A Gradient-Based Recurrent Neural Network for Visual Servoing of Robot Manipulators with Acceleration Command |
| title_fullStr | A Gradient-Based Recurrent Neural Network for Visual Servoing of Robot Manipulators with Acceleration Command |
| title_full_unstemmed | A Gradient-Based Recurrent Neural Network for Visual Servoing of Robot Manipulators with Acceleration Command |
| title_short | A Gradient-Based Recurrent Neural Network for Visual Servoing of Robot Manipulators with Acceleration Command |
| title_sort | gradient based recurrent neural network for visual servoing of robot manipulators with acceleration command |
| url | http://dx.doi.org/10.1155/2020/2305459 |
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