This paper explores the problem of collision-free motion generation for manipulators by formulating it as a global motion optimization problem. We develop a parallel optimization technique to solve this problem and demonstrate its effectiveness on massively parallel GPUs. We show that combining simple optimization techniques with many parallel seeds leads to solving difficult motion generation problems within 50ms on average, 60x faster than state-of-the-art (SOTA) trajectory optimization methods. We achieve SOTA performance by combining L-BFGS step direction estimation with a novel parallel noisy line search scheme and a particle-based optimization solver. To further aid trajectory optimization, we develop a parallel geometric planner that plans within 20ms and also introduce a collision-free IK solver that can solve over 7000 queries/s. We package our contributions into a state of the art GPU accelerated motion generation library, CuRobo and release it to enrich the robotics community. Additional details are available at https://curobo.org

翻译：本文通过将操作臂无碰撞运动生成问题建模为全局运动优化问题展开研究。我们提出了一种并行优化技术来解决该问题，并在大规模并行GPU上验证了其有效性。研究表明：将简单优化技术与大量并行初始解相结合，可在平均50ms内解决困难的运动生成问题，比最先进的轨迹优化方法快60倍。通过结合L-BFGS步长方向估计、新型并行噪声线性搜索方案以及粒子优化求解器，我们实现了最先进的性能。为进一步辅助轨迹优化，我们开发了能在20ms内完成规划的并行几何规划器，并引入了可每秒处理超过7000个查询的无碰撞逆运动学求解器。我们将这些成果整合为先进的GPU加速运动生成库CuRobo并予以开源，以期丰富机器人社区。更多细节请访问https://curobo.org。