We present a versatile nonlinear model predictive control (NMPC) formulation for quadrupedal locomotion. Our formulation jointly optimizes a base trajectory and a set of footholds over a finite time horizon based on simplified dynamics models. We leverage second-order sensitivity analysis and a sparse Gauss-Newton (SGN) method to solve the resulting optimal control problems. We further describe our ongoing effort to verify our approach through simulation and hardware experiments. Finally, we extend our locomotion framework to deal with challenging tasks that comprise gap crossing, movement on stepping stones, and multi-robot control.
@article{arxiv.2207.10465,
title = {Nonlinear Model Predictive Control for Quadrupedal Locomotion Using Second-Order Sensitivity Analysis},
author = {Dongho Kang and Flavio De Vincenti and Stelian Coros},
journal= {arXiv preprint arXiv:2207.10465},
year = {2022}
}
Comments
5 pages. 5 figures. Presented in ICRA 2022: 6th Full-Day Workshop on Legged Robots. The first two authors contributed equally to this work. The supplementary video is available in https://youtu.be/BrJSRlAJaX4