Semi-Infinite Programming with Complementarity Constraints for Pose Optimization with Pervasive Contact
Semi-Infinite Programming with Complementarity Constraints for Pose Optimization with Pervasive Contact
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DOI：
10.1109/icra48506.2021.9561609
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发表时间：
2021-05
期刊：
影响因子：
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通讯作者：
Mengchao Zhang;Kris K. Hauser
中科院分区：
文献类型：
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作者：
Mengchao Zhang;Kris K. Hauser
本文提出了一种新的计算模型来解决接触是一个涉及连续相互作用区域的无限现象的问题。将该问题转化为具有互补约束的半无穷规划（SIPCC）。我们不是将接触面预先离散成有限数量的接触点，而是使用半无限规划（SIP）技术，该技术在底层连续几何上运行，但动态地确定与解决问题最相关的有限数量的约束。然后，我们求解了一系列收敛于包含原SIPCC真最优解的问题。我们将该模型应用于夹持器和仿人机器人的抓取姿态优化问题，该模型使机器人能够在保证力和力矩平衡的情况下找到抓取（非）凸物体的可行姿态。
This paper presents a novel computational model to address the problem that contact is an infinite phenomena involving continuous regions of interaction. The problem is cast as a semi-infinite program with complementarity constraints (SIPCC). Rather than pre-discretize contacting surfaces into a finite number of contact points, we use semi-infinite programming (SIP) techniques that operate on the underlying continuous geometry, but dynamically determine a finite number of constraints that are most relevant to solving the problem. Then we solve the series of problems whose solutions converge toward one that contains a true optimum of the original SIPCC. We apply the model to a grasping pose optimization problem for a gripper and a humanoid robot, and our model enables the robots to find a feasible pose to hold (non-)convex objects while ensuring force and torque balance.