cuRoboV2-Fast：快速力矩受限无碰撞运动规划
cuRoboV2-Fast: Fast Torque-Limited Collision-Free Motion Planning
Balakumar Sundaralingam, Adithyavairavan Murali, Stan Birchfield
AI 速览：作者提出一种GPU加速规划器，在无碰撞、载荷感知与力矩受限条件下同时降低规划延迟和执行时间。
We present cuRoboV2-Fast, a GPU-accelerated planner that reduces both planning latency and execution time for collision-free, payload-aware, torque-limited motions. B-spline trajectory optimization reduces the required iterations, while accelerated robot computations and differentiable inverse dynamics reduce refinement cost and accommodate changing payloads. TimeOpt optimizes traversal speed along the fixed path under sampled velocity, acceleration, jerk, and torque constraints, exploiting local spline support to solve banded Newton systems efficiently on the GPU. With a 3,kg payload, cuRoboV2-Fast achieves 4.4 ms mean planning time on RTX 5090, 14.7x faster than cuRoboV2. Our method on Jetson AGX Orin plans faster than cuRoboV1 + TOPPRA on RTX 5090. A stereo-camera-based deployment on YAM achieves 5.93 s mean completed pick-and-drop cycles, 34.6% shorter than cuRoboV2. Code (Apache 2.0): www.github.com/NVlabs/curobo , Project Page: www.research.nvidia.com/labs/lpr/curobov2-fast .