Model predictive control of legged and humanoid robots: models and algorithms
Model predictive control of legged and humanoid robots: models and algorithms
复制标题
DOI：
10.1080/01691864.2023.2168134
复制
发表时间：
2023-02
影响因子：
2
通讯作者：
S. Katayama;Masaki Murooka;Y. Tazaki
中科院分区：
文献类型：
--
作者：
S. Katayama;Masaki Murooka;Y. Tazaki
模型预测控制（MPC）在过去的十年中一直是一个活跃的研究课题。虽然机器人系统的MPC有着悠久的历史，但随着机器人硬件、计算处理器和算法的巨大进步，它的范式（如问题公式和算法）也发生了变化。本文从以下三个方面综述了近年来在足机器人和类人机器人中MPC的研究进展。首先，我们回顾了MPC公式中使用的机器人系统的各种动力学模型。其次，我们概述了MPC算法，特别关注适合机器人问题的算法。最后，从基于降阶模型的MPC到基于全身模型的MPC的最新进展，介绍了MPC在实际机器人问题中的方法和应用。图形抽象
ABSTRACT Model predictive control (MPC) of legged and humanoid robotic systems has been an active research topic in the past decade. While MPC for robotic systems has a long history, its paradigm such as problem formulations and algorithms has changed along with the recent drastic progress in robot hardware, computational processors, and algorithms. This survey paper reviews recent progress on MPC for legged and humanoid robotics from the following three points of view. First, we review a variety of dynamical models of robotic systems used in the MPC formulation. Second, we give an overview of MPC algorithms, particularly focusing on suitable ones for robotic problems. Finally, we introduce methods and applications of MPC for practical robotic problems from MPC based on reduced-order models to recent progress on MPC based on whole-body models. GRAPHICAL ABSTRACT