随机神经扫描体积用于实时机会约束轨迹优化
Stochastic Neural Signed Swept Volume for Real-time Chance-Constrained Trajectory Optimization
- Purdue University（普渡大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出将扫描体积的符号距离函数建模为概率场，以量化不确定性并纳入感知噪声，从而集成到机会约束轨迹优化中，在仿真和真实硬件上验证了高维操作问题的有效性。
AI中文摘要：
无碰撞运动规划需要来自感知环境的可靠碰撞模型，并验证连续轨迹上各状态的安全性。为使这一问题易于处理，大多数规划器在连续轨迹的离散状态处，针对环境的单一确定性模型进行碰撞检查，从而在安全性与计算效率之间引入权衡。虽然存在近似机器人扫描体积的连续碰撞检查方法，但这些方法计算成本高昂或过于保守。数据驱动方法可以学习扫描体积；然而，这些神经模型容易受到近似误差的影响，因此通常仅限于作为下游碰撞检查器的粗过滤器。在这项工作中，我们提出将扫描体积的符号距离函数学习为概率场，从而能够量化认知不确定性、纳入感知噪声，并最终集成到机会约束轨迹优化框架中。我们在具有显著传感器噪声的挑战性高维操作问题上展示了我们的方法，既在仿真中也在真实硬件上进行了验证。
英文摘要：
Collision-free motion planning requires reliable collision models from sensed environments and validation of states along a continuous trajectory. To make this tractable, most planners check for collision at discrete states along continuous trajectories against a single determinized model of the environment, introducing a trade-off between safety and computational efficiency. While continuous collision checking approaches that approximate the swept volume of the robot exist, they are computationally expensive or overly conservative. Data-driven approaches can learn the swept volume; however, these neural models are susceptible to approximation errors and are therefore often limited to serving as coarse filters for downstream collision checkers. In this work, we propose to learn a signed distance function of the swept volume as a probabilistic field, enabling quantification of epistemic uncertainty, incorporation of perception noise, and eventual integration into a chance-constrained trajectory optimization framework. We demonstrate our approach on challenging high-dimensional manipulation problems with significant sensor noise, both in simulation and on real hardware.
