面向平面物体集合的平行夹爪与抓取协同优化
Parallel-Jaw Gripper and Grasp Co-Optimization for Sets of Planar Objects
AI总结：
本文提出一种协同优化平行夹爪几何形状与抓取接触位置及位姿的框架，通过增广拉格朗日非线性规划实现稳定抓取平面物体集合，并在多个示例和物理实验中验证其有效性。
摘要：
我们提出一个用于优化平面平行夹爪以适配多个物体的框架。尽管通用夹爪优化和抓取接触位置优化都已被充分研究，但对抓取与夹爪几何形状进行协同优化以执行这些抓取的研究却较少受到关注。因此，我们的框架合成出经过优化、能够稳定抓取多边形物体集合的夹爪。在给定固定接触数量及其到物体面和夹爪爪指的分配后，我们的框架优化沿这些面的接触位置、每次抓取的夹爪位姿以及夹爪形状。我们的关键思路是：在与夹爪爪指固连的坐标系中建立形状和接触约束，并通过增广拉格朗日形式利用抓取稳定性和夹爪形状模型中约束的线性特性。这些共同使得一个可求解的非线性规划实现成为可能。我们将方法应用于多个示例。第一个说明性问题展示了在可能的情况下发现几何上简单解的过程。在另一个示例中，空间受到限制，迫使多个物体彼此由相同的特征接触。最后，一个工具组抓取示例表明我们的框架适用于复杂的真实世界物体。我们提供了工具组抓取的物理实验。
英文摘要：
We propose a framework for optimizing a planar parallel-jaw gripper for use with multiple objects. While optimizing general-purpose grippers and contact locations for grasps are both well studied, co-optimizing grasps and the gripper geometry to execute them receives less attention. As such, our framework synthesizes grippers optimized to stably grasp sets of polygonal objects. Given a fixed number of contacts and their assignments to object faces and gripper jaws, our framework optimizes contact locations along these faces, gripper pose for each grasp, and gripper shape. Our key insights are to pose shape and contact constraints in frames fixed to the gripper jaws, and to leverage the linearity of constraints in our grasp stability and gripper shape models via an augmented Lagrangian formulation. Together, these enable a tractable nonlinear program implementation. We apply our method to several examples. The first illustrative problem shows the discovery of a geometrically simple solution where possible. In another, space is constrained, forcing multiple objects to be contacted by the same features as each other. Finally a toolset-grasping example shows that our framework applies to complex, real-world objects. We provide a physical experiment of the toolset grasps.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
