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基于实时足迹规划和模型预测控制的稳定双足步行方法。

Real-Time Footprint Planning and Model Predictive Control Based Method for Stable Biped Walking.

机构信息

School of Computer Science, Harbin Institute of Technology, Harbin, China.

Leju Robotics, Shenzhen, China.

出版信息

Comput Intell Neurosci. 2022 Apr 1;2022:4781747. doi: 10.1155/2022/4781747. eCollection 2022.

DOI:10.1155/2022/4781747
PMID:35401727
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8993559/
Abstract

In order to walk in a physical environment, the biped will encounter various external disturbances, and walking under persistent conditions is still challenging. This paper tries to improve the push recovery performance based on capture point (CP) and model predictive control. The trajectory of zero moment point (ZMP) and center of mass are solved and predicted in a limited time horizon. Online footprint generator is combined with MPC walking pattern generation, which can keep biped stable in the next few steps, and projection of ZMP is used to calculate the next footprint and reach the target CP in an incremental way. Verification of the proposed stable biped walking method is conducted by simulation and experiments.

摘要

为了在物理环境中行走，双足机器人将遇到各种外部干扰，在持续条件下行走仍然具有挑战性。本文试图基于捕获点（CP）和模型预测控制来提高推回恢复性能。在有限的时间范围内求解和预测零力矩点（ZMP）和质心的轨迹。在线足迹生成器与 MPC 行走模式生成相结合，可以使双足机器人在下几步保持稳定，并使用 ZMP 的投影以递增的方式计算下一个足迹并到达目标 CP。通过仿真和实验验证了所提出的稳定双足行走方法。

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