Artificial Intelligence in Robotic Manipulators: Exploring Object Detection and Grasping Innovations
The importance of deep learning has heralded transforming changes across different technological domains, not least in the enhancement of robotic arm functionalities of object detection’s and grasping. This paper is aimed to review recent and past studies to give a comprehensive insight to focus in...
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| Format: | Article |
| Language: | English |
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Faculty of Engineering, University of Kufa
2025-02-01
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| Series: | Mağallaẗ Al-kūfaẗ Al-handasiyyaẗ |
| Subjects: | |
| Online Access: | https://journal.uokufa.edu.iq/index.php/kje/article/view/16241 |
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| _version_ | 1832087199658541056 |
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| author | Montassar Aidi Sharif Hanan Hameed Ismael Muamar Almani Jasim Farah Zuhair Jasim |
| author_facet | Montassar Aidi Sharif Hanan Hameed Ismael Muamar Almani Jasim Farah Zuhair Jasim |
| author_sort | Montassar Aidi Sharif |
| collection | DOAJ |
| description | The importance of deep learning has heralded transforming changes across different technological domains, not least in the enhancement of robotic arm functionalities of object detection’s and grasping. This paper is aimed to review recent and past studies to give a comprehensive insight to focus in exploring cutting-edge deep learning methodologies to surmount the persistent challenges of object detection and precise manipulation by robotic arms. By integrating the iterations of the You Only Look Once (YOLO) algorithm with deep learning models, our study not only advances the innovations in robotic perception but also significantly improves the accuracy of robotic grasping in dynamic environments. Through a comprehensive exploration of various deep learning techniques, we introduce many approaches that enable robotic arms to identify and grasp objects with unprecedented precision, thereby bridging a critical gap in robotic automation. Our findings demonstrate a marked enhancement in the robotic arm’s ability to adapt to and interact with its surroundings, opening new avenues for automation in industrial, medical, and domestic applications. The impact of this research extends lays the groundwork for future developments in robotic autonomy, offering insights into the integration of deep learning algorithms with robotic systems. This also serves as a beacon for future research aimed at fully unleashing the potential of robots as autonomous agents in complex, real-world settings.
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| format | Article |
| id | doaj-art-1089867bfb184fa4baeb199d1c431de6 |
| institution | Kabale University |
| issn | 2071-5528 2523-0018 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Faculty of Engineering, University of Kufa |
| record_format | Article |
| series | Mağallaẗ Al-kūfaẗ Al-handasiyyaẗ |
| spelling | doaj-art-1089867bfb184fa4baeb199d1c431de62025-02-06T07:05:42ZengFaculty of Engineering, University of KufaMağallaẗ Al-kūfaẗ Al-handasiyyaẗ2071-55282523-00182025-02-011601136159https://doi.org/10.30572/2018/KJE/160109Artificial Intelligence in Robotic Manipulators: Exploring Object Detection and Grasping InnovationsMontassar Aidi Sharif0https://orcid.org/0000-0002-9879-0631Hanan Hameed Ismael1Muamar Almani Jasim2Farah Zuhair Jasim3Electronic and Control Engineering Department, Technical Engineering College –Kirkuk, Northern Technical University, IrElectronic and Control Engineering Department, Technical Engineering College –Kirkuk, Northern Technical University, IraqComputer Engineering Department, Technical Engineering College –Kirkuk, Northern Technical University, IraqElectronic and Control Engineering Department, Technical Engineering College –Kirkuk, Northern Technical University, IraqThe importance of deep learning has heralded transforming changes across different technological domains, not least in the enhancement of robotic arm functionalities of object detection’s and grasping. This paper is aimed to review recent and past studies to give a comprehensive insight to focus in exploring cutting-edge deep learning methodologies to surmount the persistent challenges of object detection and precise manipulation by robotic arms. By integrating the iterations of the You Only Look Once (YOLO) algorithm with deep learning models, our study not only advances the innovations in robotic perception but also significantly improves the accuracy of robotic grasping in dynamic environments. Through a comprehensive exploration of various deep learning techniques, we introduce many approaches that enable robotic arms to identify and grasp objects with unprecedented precision, thereby bridging a critical gap in robotic automation. Our findings demonstrate a marked enhancement in the robotic arm’s ability to adapt to and interact with its surroundings, opening new avenues for automation in industrial, medical, and domestic applications. The impact of this research extends lays the groundwork for future developments in robotic autonomy, offering insights into the integration of deep learning algorithms with robotic systems. This also serves as a beacon for future research aimed at fully unleashing the potential of robots as autonomous agents in complex, real-world settings. https://journal.uokufa.edu.iq/index.php/kje/article/view/16241robotics manipulatorobject detectionrobot graspingartificial intelligenceyolo |
| spellingShingle | Montassar Aidi Sharif Hanan Hameed Ismael Muamar Almani Jasim Farah Zuhair Jasim Artificial Intelligence in Robotic Manipulators: Exploring Object Detection and Grasping Innovations Mağallaẗ Al-kūfaẗ Al-handasiyyaẗ robotics manipulator object detection robot grasping artificial intelligence yolo |
| title | Artificial Intelligence in Robotic Manipulators: Exploring Object Detection and Grasping Innovations |
| title_full | Artificial Intelligence in Robotic Manipulators: Exploring Object Detection and Grasping Innovations |
| title_fullStr | Artificial Intelligence in Robotic Manipulators: Exploring Object Detection and Grasping Innovations |
| title_full_unstemmed | Artificial Intelligence in Robotic Manipulators: Exploring Object Detection and Grasping Innovations |
| title_short | Artificial Intelligence in Robotic Manipulators: Exploring Object Detection and Grasping Innovations |
| title_sort | artificial intelligence in robotic manipulators exploring object detection and grasping innovations |
| topic | robotics manipulator object detection robot grasping artificial intelligence yolo |
| url | https://journal.uokufa.edu.iq/index.php/kje/article/view/16241 |
| work_keys_str_mv | AT montassaraidisharif artificialintelligenceinroboticmanipulatorsexploringobjectdetectionandgraspinginnovations AT hananhameedismael artificialintelligenceinroboticmanipulatorsexploringobjectdetectionandgraspinginnovations AT muamaralmanijasim artificialintelligenceinroboticmanipulatorsexploringobjectdetectionandgraspinginnovations AT farahzuhairjasim artificialintelligenceinroboticmanipulatorsexploringobjectdetectionandgraspinginnovations |