面向具有高保真几何形状的富接触操作的同步轨迹优化与接触选择
Simultaneous Trajectory Optimization and Contact Selection for Contact-rich Manipulation with High-Fidelity Geometry
AI总结：
本文提出STOCS方法，通过在迭代轨迹优化过程中动态识别显著接触点和时间，有效减少MPCC的变量与约束，解决了CITO在处理高保真几何体时计算量过大的问题。
摘要：
接触隐式轨迹优化（CITO）是为包括操作和移动在内的各种富接触系统规划复杂轨迹的有效方法。CITO构建了一个带有互补约束的数学规划（MPCC），强制要求当点未接触时接触力必须为零。然而，MPCC的求解时间随着允许的接触点数量增加而急剧上升，这限制了CITO仅适用于只允许少数简单几何体进行接触的问题。本文引入了同步轨迹优化与接触选择（STOCS），作为CITO的扩展来克服这一局限性。STOCS的创新在于在迭代轨迹优化过程中识别显著的接触点和接触时间。这有效地减少了每次MPCC调用中的变量和约束数量。实例化了关键接触识别子程序的STOCS框架，使得即使是包含数万个顶点的高保真几何体，其操作轨迹的优化在计算上也是可处理的。
英文摘要：
Contact-implicit trajectory optimization (CITO) is an effective method to plan complex trajectories for various contact-rich systems including manipulation and locomotion. CITO formulates a mathematical program with complementarity constraints (MPCC) that enforces that contact forces must be zero when points are not in contact. However, MPCC solve times increase steeply with the number of allowable points of contact, which limits CITO's applicability to problems in which only a few, simple geometries are allowed to make contact. This paper introduces simultaneous trajectory optimization and contact selection (STOCS), as an extension of CITO that overcomes this limitation. The innovation of STOCS is to identify salient contact points and times inside the iterative trajectory optimization process. This effectively reduces the number of variables and constraints in each MPCC invocation. The STOCS framework, instantiated with key contact identification subroutines, renders the optimization of manipulation trajectories computationally tractable even for high-fidelity geometries consisting of tens of thousands of vertices.
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