In this section
Download a PDF with the full list of our publications: Robot-Intelligence-Lab-Publications-2021.pdf
A comprehensive list can also be found at Google Scholar, or by searching for the publications of author Kormushev, Petar.
@article{Wang:2022:10.3389/frobt.2022.812258,
author = {Wang, K and Fei, H and Kormushev, P},
doi = {10.3389/frobt.2022.812258},
journal = {Frontiers in Robotics and AI},
pages = {1--11},
title = {Fast online optimization for terrain-blind bipedal robot walking with a decoupled actuated SLIP model},
url = {http://dx.doi.org/10.3389/frobt.2022.812258},
volume = {9},
year = {2022}
}
TY - JOUR
AB - We present an online optimization algorithm which enables bipedal robots to blindly walk overvarious kinds of uneven terrains while resisting pushes. The proposed optimization algorithmperforms high level motion planning of footstep locations and center-of-mass height variationsusing the decoupled actuated Spring Loaded Inverted Pendulum (aSLIP) model. The decoupledaSLIP model simplifies the original aSLIP with Linear Inverted Pendulum (LIP) dynamics inhorizontal states and spring dynamics in the vertical state. The motion planning can beformulated as a discrete-time Model Predictive Control (MPC) problem and solved at a frequencyof 1 kHz. The output of the motion planner is fed into an inverse-dynamics based whole bodycontroller for execution on the robot. A key result of this controller is that the feet of the robot arecompliant, which further extends the robot’s ability to be robust to unobserved terrain variations.We evaluate our method in simulation with the bipedal robot SLIDER. Results show the robotcan blindly walk over various uneven terrains including slopes, wave fields and stairs. It can alsoresist pushes of up to 40 N for a duration of 0.1 s while walking on uneven terrain.
AU - Wang,K
AU - Fei,H
AU - Kormushev,P
DO - 10.3389/frobt.2022.812258
EP - 11
PY - 2022///
SN - 2296-9144
SP - 1
TI - Fast online optimization for terrain-blind bipedal robot walking with a decoupled actuated SLIP model
T2 - Frontiers in Robotics and AI
UR - http://dx.doi.org/10.3389/frobt.2022.812258
UR - https://www.frontiersin.org/articles/10.3389/frobt.2022.812258/full
UR - http://hdl.handle.net/10044/1/93756
VL - 9
ER -
