通过具有互补性限制的轨迹优化优化进行非传染病平面操纵 (Non-prehensile Planar Manipulation via Trajectory Optimization with Complementarity Constraints)

Contact adaption is an essential capability when manipulating objects. Two key contact modes of non-prehensile manipulation are sticking and sliding. This paper presents a Trajectory Optimization (TO) method formulated as a Mathematical Program with Complementarity Constraints (MPCC), which is able to switch between these two modes. We show that this formulation can be applicable to both planning and Model Predictive Control (MPC) for planar manipulation tasks. We numerically compare: (i) our planner against a mixed integer alternative, showing that the MPCC planer converges faster, scales better with respect to time horizon, and can handle environments with obstacles; (ii) our controller against a state-of-the-art mixed integer approach, showing that the MPCC controller achieves better tracking and more consistent computation times. Additionally, we experimentally validate both our planner and controller with the KUKA LWR robot on a range of planar manipulation tasks.

翻译：调控对象时, 接触适应是一种必不可少的能力。两种非危险操纵的关键接触模式正在粘贴和滑动。本文展示了一种轨迹优化( TO) 方法, 设计成具有互补性限制的数学程序( MPCC), 可以在这两种模式之间转换。我们显示这种配方既适用于规划操作任务的规划和模型预测控制( MPC ) 。我们用数字比较:( 一) 我们的计划者与混合的整数替代方法相比, 显示 MPCC 规划者更快地结合, 比例比时间范围要好, 并且能够用障碍处理环境 ;( 二) 我们的控制者对抗一种最先进的混合整数方法, 显示 MPCC 控制者能够实现更好的跟踪和更加一致的计算时间。此外, 我们实验性地验证我们的规划者和控制者与 KUKA LWW 机器人在一系列规划操作任务上的操作者。