TY - GEN
T1 - Second-order Kinematics for Floating-base Robots using the Redundant Acceleration Feedback of an Artificial Sensory Skin
AU - Leboutet, Quentin
AU - Guadarrama-Olvera, J. Rogelio
AU - Bergner, Florian
AU - Cheng, Gordon
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/5
Y1 - 2020/5
N2 - In this work, we propose a new estimation method for second-order kinematics for floating-base robots, based on highly redundant distributed inertial feedback. The linear acceleration of each robot link is measured at multiple points using a multimodal, self-configuring and self-calibrating artificial skin. The proposed algorithm is two-fold: i) the skin acceleration data is fused at the link level for state dimensionality reduction; ii) the estimated values are then fused limb-wise with data from the joint encoders and the main inertial measurement unit (IMU), using a Sigma-point Kalman filter. In this manner, it is possible to estimate the joint velocities and accelerations while avoiding the lag and noise amplification phenomena associated with conventional numerical derivation approaches. Experiments performed on the right arm and torso of a REEM-C humanoid robot, demonstrate the consistency of the proposed estimation method.
AB - In this work, we propose a new estimation method for second-order kinematics for floating-base robots, based on highly redundant distributed inertial feedback. The linear acceleration of each robot link is measured at multiple points using a multimodal, self-configuring and self-calibrating artificial skin. The proposed algorithm is two-fold: i) the skin acceleration data is fused at the link level for state dimensionality reduction; ii) the estimated values are then fused limb-wise with data from the joint encoders and the main inertial measurement unit (IMU), using a Sigma-point Kalman filter. In this manner, it is possible to estimate the joint velocities and accelerations while avoiding the lag and noise amplification phenomena associated with conventional numerical derivation approaches. Experiments performed on the right arm and torso of a REEM-C humanoid robot, demonstrate the consistency of the proposed estimation method.
KW - Acceleration Feedback
KW - Artificial Robot Skin
KW - Sigma-point Kalman Filter
UR - http://www.scopus.com/inward/record.url?scp=85092695612&partnerID=8YFLogxK
U2 - 10.1109/ICRA40945.2020.9197169
DO - 10.1109/ICRA40945.2020.9197169
M3 - Conference contribution
AN - SCOPUS:85092695612
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 4687
EP - 4694
BT - 2020 IEEE International Conference on Robotics and Automation, ICRA 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE International Conference on Robotics and Automation, ICRA 2020
Y2 - 31 May 2020 through 31 August 2020
ER -