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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.22034 |
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| _version_ | 1866915361450885120 |
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| author | Chen, Kejia Dettmering, Celina Pachler, Florian Liu, Zhuo Zhang, Yue Cheng, Tailai Dirr, Jonas Bing, Zhenshan Knoll, Alois Daub, Rüdiger |
| author_facet | Chen, Kejia Dettmering, Celina Pachler, Florian Liu, Zhuo Zhang, Yue Cheng, Tailai Dirr, Jonas Bing, Zhenshan Knoll, Alois Daub, Rüdiger |
| contents | Industrial assembly of deformable linear objects (DLOs) such as cables offers great potential for many industries. However, DLOs pose several challenges for robot-based automation due to the inherent complexity of deformation and, consequentially, the difficulties in anticipating the behavior of DLOs in dynamic situations. Although existing studies have addressed isolated subproblems like shape tracking, grasping, and shape control, there has been limited exploration of integrated workflows that combine these individual processes. To address this gap, we propose an object-centric perception and planning framework to achieve a comprehensive DLO assembly process throughout the industrial value chain. The framework utilizes visual and tactile information to track the DLO's shape as well as contact state across different stages, which facilitates effective planning of robot actions. Our approach encompasses robot-based bin picking of DLOs from cluttered environments, followed by a coordinated handover to two additional robots that mount the DLOs onto designated fixtures. Real-world experiments employing a setup with multiple robots demonstrate the effectiveness of the approach and its relevance to industrial scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_22034 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multi-Robot Assembly of Deformable Linear Objects Using Multi-Modal Perception Chen, Kejia Dettmering, Celina Pachler, Florian Liu, Zhuo Zhang, Yue Cheng, Tailai Dirr, Jonas Bing, Zhenshan Knoll, Alois Daub, Rüdiger Robotics Industrial assembly of deformable linear objects (DLOs) such as cables offers great potential for many industries. However, DLOs pose several challenges for robot-based automation due to the inherent complexity of deformation and, consequentially, the difficulties in anticipating the behavior of DLOs in dynamic situations. Although existing studies have addressed isolated subproblems like shape tracking, grasping, and shape control, there has been limited exploration of integrated workflows that combine these individual processes. To address this gap, we propose an object-centric perception and planning framework to achieve a comprehensive DLO assembly process throughout the industrial value chain. The framework utilizes visual and tactile information to track the DLO's shape as well as contact state across different stages, which facilitates effective planning of robot actions. Our approach encompasses robot-based bin picking of DLOs from cluttered environments, followed by a coordinated handover to two additional robots that mount the DLOs onto designated fixtures. Real-world experiments employing a setup with multiple robots demonstrate the effectiveness of the approach and its relevance to industrial scenarios. |
| title | Multi-Robot Assembly of Deformable Linear Objects Using Multi-Modal Perception |
| topic | Robotics |
| url | https://arxiv.org/abs/2506.22034 |