Nonlinear optimization of Step Duration and Step Location
Jiatao Ding, Xiaohui Xiao, Nikos Tsagarakis
Abstract
The modulation of step location and duration plays an important role in realizing robust bipedal walking. This paper formulates it as a nonlinear programming problem (NLP) and proposes a novel optimization approach to adjust step location and duration in real time. Based on state feedback, the Linear Inverted Pendulum dynamics is exploited to determine the optimal step parameters. Different from previous works, this work presents three main characteristics: i) the hyperbolic functions of step duration rather than the step duration itself are chosen to be optimization variables; ii) the approach can be switched from baseline two-steps-prediction optimization to one-step-prediction optimization through merely adding several equality constraints in problem formulation; iii) the approach can deal with relative step location tracking (velocity tracking) or absolute step location tracking (position tracking) via changing the reference step parameters. As a result, the first characteristic enables the NLP to be solved in a computational-efficient manner and the latter two endow the approach with versatility under different control modes. The effectiveness has been demonstrated by simulation experiments.
BibTeX
@inproceedings{iros2019_nonlinearoptimiz,
title = {Nonlinear optimization of Step Duration and Step Location},
author = {Jiatao Ding and Xiaohui Xiao and Nikos Tsagarakis},
booktitle = {IROS 2019},
year = {2019}
}