Industrial Robot Motion Planning with GPUs: Integration of cuRobo for Extended DOF Systems
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866916889651838976 |
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| author | Abuelsamen, Luai Rana, Harsh Lu, Ho-Wei Tang, Wenhan Priyadarshini, Swati Gomes, Gabriel |
| author_facet | Abuelsamen, Luai Rana, Harsh Lu, Ho-Wei Tang, Wenhan Priyadarshini, Swati Gomes, Gabriel |
| contents | Efficient motion planning remains a key challenge in industrial robotics, especially for multi-axis systems operating in complex environments. This paper addresses that challenge by integrating GPU-accelerated motion planning through NVIDIA's cuRobo library into Vention's modular automation platform. By leveraging accurate CAD-based digital twins and real-time parallel optimization, our system enables rapid trajectory generation and dynamic collision avoidance for pick-and-place tasks. We demonstrate this capability on robots equipped with additional degrees of freedom, including a 7th-axis gantry, and benchmark performance across various scenarios. The results show significant improvements in planning speed and robustness, highlighting the potential of GPU-based planning pipelines for scalable, adaptable deployment in modern industrial workflows. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2508_04146 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Industrial Robot Motion Planning with GPUs: Integration of cuRobo for Extended DOF Systems Abuelsamen, Luai Rana, Harsh Lu, Ho-Wei Tang, Wenhan Priyadarshini, Swati Gomes, Gabriel Robotics I.2.9; I.2.10; J.7 Efficient motion planning remains a key challenge in industrial robotics, especially for multi-axis systems operating in complex environments. This paper addresses that challenge by integrating GPU-accelerated motion planning through NVIDIA's cuRobo library into Vention's modular automation platform. By leveraging accurate CAD-based digital twins and real-time parallel optimization, our system enables rapid trajectory generation and dynamic collision avoidance for pick-and-place tasks. We demonstrate this capability on robots equipped with additional degrees of freedom, including a 7th-axis gantry, and benchmark performance across various scenarios. The results show significant improvements in planning speed and robustness, highlighting the potential of GPU-based planning pipelines for scalable, adaptable deployment in modern industrial workflows. |
| title | Industrial Robot Motion Planning with GPUs: Integration of cuRobo for Extended DOF Systems |
| topic | Robotics I.2.9; I.2.10; J.7 |
| url | https://arxiv.org/abs/2508.04146 |