Whole-body model predictive control with rigid contacts via online switching time optimization

Описание: A novel whole-body model predictive control (MPC) scheme that enables optimizing the trajectory and switching times simultaneously.
Will be presented at the 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022).

Title:
Whole-body model predictive control with rigid contacts via online switching time optimization

Authors:
Sotaro Katayama and Toshiyuki Ohtsuka

Conference:
2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022)

arXiv:
https://arxiv.org/abs/2203.00997

Code:
https://github.com/mayataka/robotoc

Abstruct:
This study presents a whole-body model predictive control (MPC) of robotic systems with rigid contacts, under a given contact sequence using online switching time optimization (STO). We treat robot dynamics with rigid contacts as a switched system and formulate an optimal control problem of switched systems to implement the MPC. We utilize an efficient solution algorithm for the MPC problem that optimizes the switching times and trajectory simultaneously. The present efficient algorithm, unlike inefficient existing methods, enables online optimization as well as switching times. The proposed MPC with online STO is compared over the conventional MPC with fixed switching times, through numerical simulations of dynamic jumping motions of a quadruped robot. In the simulation comparison, the proposed MPC successfully controls the dynamic jumping motions in twice as many cases as the conventional MPC, which indicates that the proposed method extends the ability of the whole-body MPC. We further conduct hardware experiments on the quadrupedal robot Unitree A1 and prove that the proposed method achieves dynamic motions on the real robot.