杠杆(统计)
二次规划
线性互补问题
序列二次规划
先验与后验
互补性(分子生物学)
计算机科学
弹道
轨迹优化
机器人
运动规划
数学优化
二次方程
控制理论(社会学)
接触动力学
最优控制
数学
人工智能
物理
控制(管理)
认识论
天文
哲学
生物
非线性系统
量子力学
遗传学
几何学
作者
Michael Posa,Cecilia Cantu,Russ Tedrake
标识
DOI:10.1177/0278364913506757
摘要
Direct methods for trajectory optimization are widely used for planning locally optimal trajectories of robotic systems. Many critical tasks, such as locomotion and manipulation, often involve impacting the ground or objects in the environment. Most state-of-the-art techniques treat the discontinuous dynamics that result from impacts as discrete modes and restrict the search for a complete path to a specified sequence through these modes. Here we present a novel method for trajectory planning of rigid-body systems that contact their environment through inelastic impacts and Coulomb friction. This method eliminates the requirement for a priori mode ordering. Motivated by the formulation of multi-contact dynamics as a Linear Complementarity Problem for forward simulation, the proposed algorithm poses the optimization problem as a Mathematical Program with Complementarity Constraints. We leverage Sequential Quadratic Programming to naturally resolve contact constraint forces while simultaneously optimizing a trajectory that satisfies the complementarity constraints. The method scales well to high-dimensional systems with large numbers of possible modes. We demonstrate the approach on four increasingly complex systems: rotating a pinned object with a finger, simple grasping and manipulation, planar walking with the Spring Flamingo robot, and high-speed bipedal running on the FastRunner platform.
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