{"ID":2885600,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.04146","arxiv_id":"2508.04146","title":"Industrial Robot Motion Planning with GPUs: Integration of cuRobo for Extended DOF Systems","abstract":"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.","short_abstract":"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 accu...","url_abs":"https://arxiv.org/abs/2508.04146","url_pdf":"https://arxiv.org/pdf/2508.04146v2","authors":"[\"Luai Abuelsamen\",\"Harsh Rana\",\"Ho-Wei Lu\",\"Wenhan Tang\",\"Swati Priyadarshini\",\"Gabriel Gomes\"]","published":"2025-08-06T07:18:33Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false}