TY - JOUR
T1 - A Method to Identify the Nonlinear Stiffness Characteristics of an Elastic Continuum Mechanism
AU - Deutschmann, Bastian
AU - Liu, Tong
AU - Dietrich, Alexander
AU - Ott, Christian
AU - Lee, Dongheui
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2018/7
Y1 - 2018/7
N2 - The humanoid robot David is equipped with a novel robotic neck based on an elastic continuum mechanism (ECM). To realize a model-based motion control, the six dimensional stiffness characteristics needs to be known. This letter presents an approach to experimentally identify the stiffness characteristic using a robot manipulator to deflect the ECM and measure the Cartesian wrenches and Cartesian poses with external sensors. A three-step process is proposed to establish Cartesian wrench and pose pairs experimentally. The process consists of a simulation step, to select a good model, a second step that extracts effective poses from workspace which are sampled experimentally and the third step, the pose sampling procedure in which the robot drives the ECM to these effective poses. A full cubic polynomial regression model is adopted based on simulation data to fit the stiffness characteristics. To extract the poses to be sampled in the experiments, two different approaches are evaluated and compared to ensure a well-posed identification. The identification process on the hardware is performed by using Cartesian impedance and inverse kinematics control in combination to comply with the physical constraints imposed by the ECM.
AB - The humanoid robot David is equipped with a novel robotic neck based on an elastic continuum mechanism (ECM). To realize a model-based motion control, the six dimensional stiffness characteristics needs to be known. This letter presents an approach to experimentally identify the stiffness characteristic using a robot manipulator to deflect the ECM and measure the Cartesian wrenches and Cartesian poses with external sensors. A three-step process is proposed to establish Cartesian wrench and pose pairs experimentally. The process consists of a simulation step, to select a good model, a second step that extracts effective poses from workspace which are sampled experimentally and the third step, the pose sampling procedure in which the robot drives the ECM to these effective poses. A full cubic polynomial regression model is adopted based on simulation data to fit the stiffness characteristics. To extract the poses to be sampled in the experiments, two different approaches are evaluated and compared to ensure a well-posed identification. The identification process on the hardware is performed by using Cartesian impedance and inverse kinematics control in combination to comply with the physical constraints imposed by the ECM.
KW - compliant joint/mechanism
KW - Model learning for control
KW - soft material robotics
UR - http://www.scopus.com/inward/record.url?scp=85054151913&partnerID=8YFLogxK
U2 - 10.1109/LRA.2018.2800098
DO - 10.1109/LRA.2018.2800098
M3 - Article
AN - SCOPUS:85054151913
SN - 2377-3766
VL - 3
SP - 1450
EP - 1457
JO - IEEE Robotics and Automation Letters
JF - IEEE Robotics and Automation Letters
IS - 3
ER -