Nonlinear Model Predictive Control for Quadrupedal Locomotion Using Second-Order Sensitivity Analysis

ICRA 2022 - 6th Full-Day Workshop on Legged Robots

Abstract

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.

Paper: [ArXiv]Workshop Preprint: [PDF]

Supplementary Video

Presentation

Video presentation for "ICRA 2022 - 6th Full-Day Workshop on Legged Robots".

Demos

Bibtex

@misc{kang2022nonlinear,
  title={Nonlinear Model Predictive Control for Quadrupedal Locomotion Using Second-Order Sensitivity Analysis}, 
  author={Dongho Kang and Flavio De Vincenti and Stelian Coros},
  year={2022},
  eprint={2207.10465},
  archivePrefix={arXiv},
  primaryClass={cs.RO}
}


Acknowledgement

This work has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme.