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

TL;DR

This paper presents an integration of GPU-accelerated motion planning using cuRobo into industrial robotics, enabling rapid, collision-free trajectory generation for multi-DOF systems with improved speed and robustness.

Contribution

It introduces a GPU-based motion planning system for complex industrial robots, leveraging digital twins and real-time optimization for enhanced performance.

Findings

Significant speed improvements in trajectory planning

Enhanced robustness in collision avoidance

Effective handling of robots with extended degrees of freedom

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.