A Reactive Walking Pattern Generator Based on Nonlinear Model Predictive Control
Maximilien Naveau, Manuel Kudruss, Olivier Stasse, Christian Kirches, Katja D. Mombaur, Philippe Souères
Abstract
The contribution of this work is to show that real-time nonlinear model predictive control (NMPC) can be implemented on position controlled humanoid robots. Following the idea of “walking without thinking,” we propose a walking pattern generator that takes into account simultaneously the position and orientation of the feet. A requirement for an application in real-world scenarios is the avoidance of obstacles. Therefore, this letter shows an extension of the pattern generator that directly considers the avoidance of convex obstacles. The algorithm uses the whole-body dynamics to correct the center of mass trajectory of the underlying simplified model. The pattern generator runs in real-time on the embedded hardware of the humanoid robot HRP2 and experiments demonstrate the increase in performance with the correction.
BibTeX
@inproceedings{ral2017_areactivewalking,
title = {A Reactive Walking Pattern Generator Based on Nonlinear Model Predictive Control},
author = {Maximilien Naveau and Manuel Kudruss and Olivier Stasse and Christian Kirches and Katja D. Mombaur and Philippe Souères},
booktitle = {RA-L 2017},
year = {2017}
}