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Industrial Robot Motion Planning with GPUs: Integration of cuRobo for Extended DOF Systems

Published: August 6, 2025 | arXiv ID: 2508.04146v2

By: Luai Abuelsamen , Harsh Rana , Ho-Wei Lu and more

BigTech Affiliations: University of California, Berkeley

Potential Business Impact:

Robots move faster and avoid bumping into things.

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.

Country of Origin
🇺🇸 United States

Page Count
6 pages

Category
Computer Science:
Robotics