Autonomous Robotic Manipulation: Real-Time, Deep-Learning Approach for Grasping of Unknown Objects
Recent advancement in vision-based robotics and deep-learning techniques has enabled the use of intelligent systems in a wider range of applications requiring object manipulation. Finding a robust solution for object grasping and autonomous manipulation became the focus of many engineers and is stil...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Robotics |
| Online Access: | http://dx.doi.org/10.1155/2022/2585656 |
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| author | Malak H. Sayour Sharbel E. Kozhaya Samer S. Saab |
| author_facet | Malak H. Sayour Sharbel E. Kozhaya Samer S. Saab |
| author_sort | Malak H. Sayour |
| collection | DOAJ |
| description | Recent advancement in vision-based robotics and deep-learning techniques has enabled the use of intelligent systems in a wider range of applications requiring object manipulation. Finding a robust solution for object grasping and autonomous manipulation became the focus of many engineers and is still one of the most demanding problems in modern robotics. This paper presents a full grasping pipeline proposing a real-time data-driven deep-learning approach for robotic grasping of unknown objects using MATLAB and convolutional neural networks. The proposed approach employs RGB-D image data acquired from an eye-in-hand camera centering the object of interest in the field of view using visual servoing. Our approach aims at reducing propagation errors and eliminating the need for complex hand tracking algorithm, image segmentation, or 3D reconstruction. The proposed approach is able to efficiently generate reliable multi-view object grasps regardless of the geometric complexity and physical properties of the object in question. The proposed system architecture enables simple and effective path generation and a real-time tracking control. In addition, our system is modular, reliable, and accurate in both end effector path generation and control. We experimentally justify the efficacy and effectiveness of our overall system on the Barrett Whole Arm Manipulator. |
| format | Article |
| id | doaj-art-2468f8c117b94a768130a34296006dbf |
| institution | Kabale University |
| issn | 1687-9619 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Robotics |
| spelling | doaj-art-2468f8c117b94a768130a34296006dbf2025-02-03T01:20:08ZengWileyJournal of Robotics1687-96192022-01-01202210.1155/2022/2585656Autonomous Robotic Manipulation: Real-Time, Deep-Learning Approach for Grasping of Unknown ObjectsMalak H. Sayour0Sharbel E. Kozhaya1Samer S. Saab2Department of Electrical Mechatronics and Computer EngineeringHenry Samueli School of EngineeringDepartment of Electrical Mechatronics and Computer EngineeringRecent advancement in vision-based robotics and deep-learning techniques has enabled the use of intelligent systems in a wider range of applications requiring object manipulation. Finding a robust solution for object grasping and autonomous manipulation became the focus of many engineers and is still one of the most demanding problems in modern robotics. This paper presents a full grasping pipeline proposing a real-time data-driven deep-learning approach for robotic grasping of unknown objects using MATLAB and convolutional neural networks. The proposed approach employs RGB-D image data acquired from an eye-in-hand camera centering the object of interest in the field of view using visual servoing. Our approach aims at reducing propagation errors and eliminating the need for complex hand tracking algorithm, image segmentation, or 3D reconstruction. The proposed approach is able to efficiently generate reliable multi-view object grasps regardless of the geometric complexity and physical properties of the object in question. The proposed system architecture enables simple and effective path generation and a real-time tracking control. In addition, our system is modular, reliable, and accurate in both end effector path generation and control. We experimentally justify the efficacy and effectiveness of our overall system on the Barrett Whole Arm Manipulator.http://dx.doi.org/10.1155/2022/2585656 |
| spellingShingle | Malak H. Sayour Sharbel E. Kozhaya Samer S. Saab Autonomous Robotic Manipulation: Real-Time, Deep-Learning Approach for Grasping of Unknown Objects Journal of Robotics |
| title | Autonomous Robotic Manipulation: Real-Time, Deep-Learning Approach for Grasping of Unknown Objects |
| title_full | Autonomous Robotic Manipulation: Real-Time, Deep-Learning Approach for Grasping of Unknown Objects |
| title_fullStr | Autonomous Robotic Manipulation: Real-Time, Deep-Learning Approach for Grasping of Unknown Objects |
| title_full_unstemmed | Autonomous Robotic Manipulation: Real-Time, Deep-Learning Approach for Grasping of Unknown Objects |
| title_short | Autonomous Robotic Manipulation: Real-Time, Deep-Learning Approach for Grasping of Unknown Objects |
| title_sort | autonomous robotic manipulation real time deep learning approach for grasping of unknown objects |
| url | http://dx.doi.org/10.1155/2022/2585656 |
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