A comprehensive review of robot intelligent grasping based on tactile perception
The Advancements in tactile sensors and machine learning techniques open new opportunities for achieving intelligent grasping in robotics. Traditional robot is limited in its ability to perform autonomous grasping in unstructured environments. Although the existing robotic grasping method enhances t...
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| Vydané v: | Robotics and computer-integrated manufacturing Ročník 90; s. 102792 |
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| Hlavní autori: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier Ltd
01.12.2024
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| ISSN: | 0736-5845 |
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| Abstract | The Advancements in tactile sensors and machine learning techniques open new opportunities for achieving intelligent grasping in robotics. Traditional robot is limited in its ability to perform autonomous grasping in unstructured environments. Although the existing robotic grasping method enhances the robot's understanding of its environment by incorporating visual perception, it still lacks the capability for force perception and force adaptation. Therefore, tactile sensors are integrated into robot hands to enhance the robot's adaptive grasping capabilities in various complex scenarios by tactile perception. This paper primarily discusses the adaption of different types of tactile sensors in robotic grasping operations and grasping algorithms based on them. By dividing robotic grasping operations into four stages: grasping generation, robot planning, grasping state discrimination, and grasping destabilization adjustment, a further review of tactile-based and tactile-visual fusion methods is applied in related stages. The characteristics of these methods are comprehensively compared with different dimensions and indicators. Additionally, the challenges encountered in robotic tactile perception is summarized and insights into potential directions for future research are offered. This review is aimed for offering researchers and engineers a comprehensive understanding of the application of tactile perception techniques in robotic grasping operations, as well as facilitating future work to further enhance the intelligence of robotic grasping. |
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| AbstractList | The Advancements in tactile sensors and machine learning techniques open new opportunities for achieving intelligent grasping in robotics. Traditional robot is limited in its ability to perform autonomous grasping in unstructured environments. Although the existing robotic grasping method enhances the robot's understanding of its environment by incorporating visual perception, it still lacks the capability for force perception and force adaptation. Therefore, tactile sensors are integrated into robot hands to enhance the robot's adaptive grasping capabilities in various complex scenarios by tactile perception. This paper primarily discusses the adaption of different types of tactile sensors in robotic grasping operations and grasping algorithms based on them. By dividing robotic grasping operations into four stages: grasping generation, robot planning, grasping state discrimination, and grasping destabilization adjustment, a further review of tactile-based and tactile-visual fusion methods is applied in related stages. The characteristics of these methods are comprehensively compared with different dimensions and indicators. Additionally, the challenges encountered in robotic tactile perception is summarized and insights into potential directions for future research are offered. This review is aimed for offering researchers and engineers a comprehensive understanding of the application of tactile perception techniques in robotic grasping operations, as well as facilitating future work to further enhance the intelligence of robotic grasping. |
| ArticleNumber | 102792 |
| Author | Yan, Yuhang Li, Tong An, Jing Chen, Gang Yu, Chengshun Wang, Yifan |
| Author_xml | – sequence: 1 givenname: Tong orcidid: 0000-0001-7989-3355 surname: Li fullname: Li, Tong email: tli@bupt.edu.cn organization: School of Modern Post (School of Automation), Beijing University of Posts and Telecommunications, Beijing 100876, China – sequence: 2 givenname: Yuhang surname: Yan fullname: Yan, Yuhang organization: School of Modern Post (School of Automation), Beijing University of Posts and Telecommunications, Beijing 100876, China – sequence: 3 givenname: Chengshun surname: Yu fullname: Yu, Chengshun organization: School of Modern Post (School of Automation), Beijing University of Posts and Telecommunications, Beijing 100876, China – sequence: 4 givenname: Jing surname: An fullname: An, Jing organization: School of Modern Post (School of Automation), Beijing University of Posts and Telecommunications, Beijing 100876, China – sequence: 5 givenname: Yifan surname: Wang fullname: Wang, Yifan organization: School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China – sequence: 6 givenname: Gang surname: Chen fullname: Chen, Gang organization: School of Modern Post (School of Automation), Beijing University of Posts and Telecommunications, Beijing 100876, China |
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| Keywords | Robotic grasping Tactile sensor Tactile perception Tactile-visual fusion |
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