Bipedal Walking Control using Variable Horizon MPC

Bipedal Walking Control using Variable Horizon MPC
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使用可变水平 MPC 的双足行走控制

DOI:
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发表时间:
2020
期刊:
arXiv.org
影响因子:
--
通讯作者:
L. Righetti
L. Righetti
中科院分区:
--
文献类型:
--
作者:
E. Daneshmand;M. Khadiv;F. Grimminger;L. Righetti

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本文提出了一种新的两级可变水平模型预测控制(VH-MPC)框架。在这一框架中,上层利用质心状态的反馈来计算摆动脚的着陆位置和时间(视界长度),以稳定质心的不稳定部分动力学。较低的水平考虑到摆动脚的动力学,并产生动态一致的轨迹,以便在期望的时间尽可能接近期望的位置着陆。为了做到这一点,我们使用了一个简化的机器人动力学模型,该模型考虑了关节扭矩约束以及站立脚的摩擦锥约束。我们通过在我们的力矩控制和开源双足机器人Bolt上实现鲁棒行走模式来证明我们提出的控制框架的有效性。我们报告了大量的模拟和真实的机器人实验在各种干扰和不确定性的存在。
In this paper, we present a novel two-level variable Horizon Model Predictive Control (VH-MPC) framework for bipedal locomotion. In this framework, the higher level computes the landing location and timing (horizon length) of the swing foot to stabilize the unstable part of the center of mass (CoM) dynamics, using feedback from the CoM state. The lower level takes into account the swing foot dynamics and generates dynamically consistent trajectories for landing at the desired time as close as possible to the desired location. To do that, we use a simplified model of the robot dynamics projected in swing foot space that takes into account joint torque constraints as well as the friction cone constraints of the stance foot. We show the effectiveness of our proposed control framework by implementing robust walking patterns on our torque-controlled and open-source biped robot, Bolt. We report extensive simulations and real robot experiments in the presence of various disturbances and uncertainties.
使用学习质心动力学预测进行高效的人形接触规划
DOI: 10.1109/icra.2019.8794032
发表时间: 2019
期刊: 2019 IEEE International Conference on Robotics and Automation (ICRA
影响因子: --
作者:
Lin, Yu-Chi;Ponton, Brahayam;Righetti, Ludovic;Berenson, Dmitry
通讯作者: Berenson, Dmitry
DOI: 10.1109/lra.2020.2976639
发表时间: 2020-04-01
影响因子: 5.2
作者:
Grimminger, Felix;Meduri, Avadesh;Righetti, Ludovic
通讯作者: Righetti, Ludovic