Trajectory optimization (TO) aims to find a sequence of valid states while minimizing costs. However, its fine validation process is often costly due to computationally expensive collision searches, otherwise coarse searches lower the safety of the system losing a precise solution. To resolve the issues, we introduce a new collision-distance estimator, GraphDistNet, that can precisely encode the structural information between two geometries by leveraging edge feature-based convolutional operations, and also efficiently predict a batch of collision distances and gradients through 25,000 random environments with a maximum of 20 unforeseen objects. Further, we show the adoption of attention mechanism enables our method to be easily generalized in unforeseen complex geometries toward TO. Our evaluation show GraphDistNet outperforms state-of-the-art baseline methods in both simulated and real world tasks.

翻译：轨迹优化旨在寻找一系列有效状态的同时最小化成本。然而，其精细验证过程常因计算昂贵的碰撞搜索而成本高昂，否则粗糙的搜索会因丢失精确解而降低系统的安全性。为解决这些问题，我们提出了一种新的碰撞距离估计器GraphDistNet，它通过利用基于边缘特征的卷积操作精确编码两个几何体之间的结构信息，并能在包含最多20个未知物体的25,000个随机环境中高效预测一批碰撞距离及其梯度。进一步地，我们展示了注意力机制的采用使我们的方法能够轻松泛化至轨迹优化中未知的复杂几何体。实验评估表明，GraphDistNet在模拟和真实世界任务中均优于现有的基准方法。