TY - GEN
T1 - On the Use of Torque Measurement in Centroidal State Estimation
AU - Khorshidi, Shahram
AU - Gazar, Ahmad
AU - Rotella, Nicholas
AU - Naveau, Maximilien
AU - Righetti, Ludovic
AU - Bennewitz, Maren
AU - Khadiv, Majid
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - State-of-the-art legged robots are either capable of measuring torque at the output of their drive systems, or have transparent drive systems which enable the computation of joint torques from motor currents. In either case, this sensor modality is seldom used in state estimation. In this paper, we propose to use joint torque measurements to estimate the centroidal states of legged robots. To do so, we project the whole-body dynamics of a legged robot into the nullspace of the contact constraints, allowing expression of the dynamics independent of the contact forces. Using the constrained dynamics and the centroidal momentum matrix, we are able to directly relate joint torques and centroidal states dynamics. Using the resulting model as the process model of an Extended Kalman Filter (EKF), we fuse the torque measurement in the centroidal state estimation problem. Through real-world experiments on a quadruped robot executing different gaits, we demonstrate that the estimated centroidal states from our torque-based EKF drastically improve the recovery of these quantities compared to direct computation.
AB - State-of-the-art legged robots are either capable of measuring torque at the output of their drive systems, or have transparent drive systems which enable the computation of joint torques from motor currents. In either case, this sensor modality is seldom used in state estimation. In this paper, we propose to use joint torque measurements to estimate the centroidal states of legged robots. To do so, we project the whole-body dynamics of a legged robot into the nullspace of the contact constraints, allowing expression of the dynamics independent of the contact forces. Using the constrained dynamics and the centroidal momentum matrix, we are able to directly relate joint torques and centroidal states dynamics. Using the resulting model as the process model of an Extended Kalman Filter (EKF), we fuse the torque measurement in the centroidal state estimation problem. Through real-world experiments on a quadruped robot executing different gaits, we demonstrate that the estimated centroidal states from our torque-based EKF drastically improve the recovery of these quantities compared to direct computation.
UR - http://www.scopus.com/inward/record.url?scp=85168670651&partnerID=8YFLogxK
U2 - 10.1109/ICRA48891.2023.10160823
DO - 10.1109/ICRA48891.2023.10160823
M3 - Conference contribution
AN - SCOPUS:85168670651
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 9931
EP - 9937
BT - Proceedings - ICRA 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE International Conference on Robotics and Automation, ICRA 2023
Y2 - 29 May 2023 through 2 June 2023
ER -