论文标题
在线非线性质心MPC用于人形机器人运动，并调整步骤调整
Online Non-linear Centroidal MPC for Humanoid Robot Locomotion with Step Adjustment
论文作者
论文摘要
本文介绍了具有在线步骤调整功能的人形机器人运动的非线性模型预测控制器。所提出的控制器认为系统的质心动力学来计算所需的接触力和扭矩和接触位置。与基于简化模型的两足步行体系结构不同，提出的方法考虑了减少的质心模型，从而使机器人能够执行高度动态的运动，同时保持控制问题仍然可以在线处理。我们表明，所提出的控制器可以在单个和双支持阶段自动调整联系人位置。然后，通过对单腿和两腿系统进行跳跃和运行任务的模拟进行测试。我们最终在位置控制的人形机器人ICUB上验证了所提出的控制器。结果表明，拟议的策略可防止机器人在步行时掉落，并以高达40牛顿的外力推动在机器人臂上施加了1秒钟。
This paper presents a Non-Linear Model Predictive Controller for humanoid robot locomotion with online step adjustment capabilities. The proposed controller considers the Centroidal Dynamics of the system to compute the desired contact forces and torques and contact locations. Differently from bipedal walking architectures based on simplified models, the presented approach considers the reduced centroidal model, thus allowing the robot to perform highly dynamic movements while keeping the control problem still treatable online. We show that the proposed controller can automatically adjust the contact location both in single and double support phases. The overall approach is then tested with a simulation of one-leg and two-leg systems performing jumping and running tasks, respectively. We finally validate the proposed controller on the position-controlled Humanoid Robot iCub. Results show that the proposed strategy prevents the robot from falling while walking and pushed with external forces up to 40 Newton for 1 second applied at the robot arm.
