用GPU加速机器人运动规划，提升多轴系统实时避障能力
Industrial Robot Motion Planning with GPUs: Integration of cuRobo for Extended DOF Systems
- 基于CAD数字孪生与GPU并行优化，实现快速轨迹生成
- 在含第7轴的多轴系统上，规划速度显著提升且稳定性增强
- 适合需高动态响应的现代工业自动化场景
高效运动规划仍是工业机器人在复杂环境中运行的关键挑战，尤其针对多轴系统。本文将NVIDIA的cuRobo库引入Vention模块化自动化平台，利用精确的基于CAD的数字孪生和实时并行优化，实现了拾取放置任务中快速轨迹生成与动态碰撞避让。系统在配备额外自由度（包括第7轴龙门架）的机器人上进行了验证，并在多种场景下进行性能基准测试。结果表明，规划速度与鲁棒性均有显著提升，凸显了基于GPU的规划流水线在现代工业工作流中可扩展、可适配部署的巨大潜力。
原文摘要 · Abstract (English)
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.
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