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Joint friction estimation for walking bipeds

Published online by Cambridge University Press:  05 November 2014

Iyad Hashlamon and
Kemalettin Erbatur
Iyad Hashlamon*
Affiliation:
Faculty of Engineering and Natural Sciences, Sabanci University, Tuzla, Istanbul, Turkey E-mail: erbatur@sabanciuniv.edu
Kemalettin Erbatur*
Affiliation:
Faculty of Engineering and Natural Sciences, Sabanci University, Tuzla, Istanbul, Turkey E-mail: erbatur@sabanciuniv.edu
*
*Corresponding authors. E-mail: hashlamon@sabanciuniv.edu, erbatur@sabanciuniv.edu
*Corresponding authors. E-mail: hashlamon@sabanciuniv.edu, erbatur@sabanciuniv.edu
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Summary

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This paper proposed a new approach for the joint friction estimation of non-slipping walking biped robots. The proposed approach is based on the combination of a measurement-based strategy and a model-based method. The former is used to estimate the joint friction online when the foot is in contact with the ground, while the latter adopts a friction model to represent the joint friction when the leg is swinging. The measurement-based strategy utilizes the measured ground reaction forces (GRF) and the readings of an inertial measurement unit (IMU) located at the robot body. Based on these measurements, the joint angular accelerations and the body attitude and velocity are estimated. The aforementioned measurements and estimates are used in a reduced dynamical model of the biped. However, when the leg is swinging, this strategy is inapplicable. Therefore, a friction model is adopted. Its parameters are identified adaptively using the estimated online friction whenever the foot is in contact. The estimated joint friction is used in the feedback torque control signal. The proposed approach is validated using the full-dynamics of 12-DOF biped model. By using this approach, the robot center of mass (CoM) position error is reduced by 10% which demonstrates the effectiveness of this approach.

Keywords

Humanoid robotJoint frictionNon-slipping feetCoM statesBiped model

Information

Type
Articles
Information
Robotica , Volume 34 , Issue 7 , July 2016, pp. 1610 - 1629
Copyright
Copyright © Cambridge University Press 2014 

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