Real-Time Constrained Nonlinear Model Predictive Control on SO(3) for Dynamic Legged Locomotion

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020

Seungwoo Hong (1)    Joon-Ha Kim (1)    Hae-Won Park (1)

(1) Korea Advanced Institute of Science and Technology



IROS Best RoboCup Paper Award

---

Abstract

This paper presents a constrained nonlinear model predictive control (NMPC) framework for legged locomotion. The framework assumes a legged robot as a floating base single rigid body with contact forces being applied to the body as external forces. With consideration of orientation dynamics evolving on the rotation manifold SO(3), analytic Jacobians which are necessary for constructing the gradient and the Gauss-Newton Hessian approximation of the objective function are derived. This procedure also includes the reparameterization of the robot orientation on SO(3) to orientation error in the tangent space of that manifold. Obtained gradient and Gauss-Newton Hessian approximation are utilized to solve nonlinear least squares problems formulated from NMPC in a computationally efficient manner. The proposed algorithm is verified on various types of legged robots and gaits in a simulation environment.

Paper: [PDF]

Video

Bibtex

@INPROCEEDINGS{NMPCSO3Hong2020,
  author={Hong, Seungwoo and Kim, Joon-Ha and Park, Hae-Won},
  booktitle={2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  title={Real-Time Constrained Nonlinear Model Predictive Control on SO(3) for Dynamic Legged Locomotion},
  year={2020},
  volume={},
  number={},
  pages={3982-3989},
  doi={10.1109/IROS45743.2020.9341447}
}