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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.09623 |
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| _version_ | 1866917841212538880 |
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| author | Adetunji, F. Karukayil, A. Samant, P. Shabana, S. Varghese, F. Upadhyay, U. Yadav, R. A. Partridge, A. Pendleton, E. Plant, R. Petillot, Y. Koskinopoulou, M. |
| author_facet | Adetunji, F. Karukayil, A. Samant, P. Shabana, S. Varghese, F. Upadhyay, U. Yadav, R. A. Partridge, A. Pendleton, E. Plant, R. Petillot, Y. Koskinopoulou, M. |
| contents | This paper addresses the challenges of vision-based manipulation for autonomous cutting and unpacking of transparent plastic bags in industrial setups, aligning with the Industry 4.0 paradigm. Industry 4.0, driven by data, connectivity, analytics, and robotics, promises enhanced accessibility and sustainability throughout the value chain. The integration of autonomous systems, including collaborative robots (cobots), into industrial processes is pivotal for efficiency and safety. The proposed solution employs advanced Machine Learning algorithms, particularly Convolutional Neural Networks (CNNs), to identify transparent plastic bags under varying lighting and background conditions. Tracking algorithms and depth sensing technologies are utilized for 3D spatial awareness during pick and placement. The system addresses challenges in grasping and manipulation, considering optimal points, compliance control with vacuum gripping technology, and real-time automation for safe interaction in dynamic environments. The system's successful testing and validation in the lab with the FRANKA robot arm, showcases its potential for widespread industrial applications, while demonstrating effectiveness in automating the unpacking and cutting of transparent plastic bags for an 8-stack bulk-loader based on specific requirements and rigorous testing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_09623 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Vision-based Manipulation of Transparent Plastic Bags in Industrial Setups Adetunji, F. Karukayil, A. Samant, P. Shabana, S. Varghese, F. Upadhyay, U. Yadav, R. A. Partridge, A. Pendleton, E. Plant, R. Petillot, Y. Koskinopoulou, M. Robotics Artificial Intelligence Computer Vision and Pattern Recognition This paper addresses the challenges of vision-based manipulation for autonomous cutting and unpacking of transparent plastic bags in industrial setups, aligning with the Industry 4.0 paradigm. Industry 4.0, driven by data, connectivity, analytics, and robotics, promises enhanced accessibility and sustainability throughout the value chain. The integration of autonomous systems, including collaborative robots (cobots), into industrial processes is pivotal for efficiency and safety. The proposed solution employs advanced Machine Learning algorithms, particularly Convolutional Neural Networks (CNNs), to identify transparent plastic bags under varying lighting and background conditions. Tracking algorithms and depth sensing technologies are utilized for 3D spatial awareness during pick and placement. The system addresses challenges in grasping and manipulation, considering optimal points, compliance control with vacuum gripping technology, and real-time automation for safe interaction in dynamic environments. The system's successful testing and validation in the lab with the FRANKA robot arm, showcases its potential for widespread industrial applications, while demonstrating effectiveness in automating the unpacking and cutting of transparent plastic bags for an 8-stack bulk-loader based on specific requirements and rigorous testing. |
| title | Vision-based Manipulation of Transparent Plastic Bags in Industrial Setups |
| topic | Robotics Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2411.09623 |