Inertial parameters identification and joint torques estimation with proximal force/torque sensing

Silvio Traversaro 1 Andrea Del Prete 2 Serena Ivaldi 3, 4 Francesco Nori 1
2 LAAS-GEPETTO - Équipe Mouvement des Systèmes Anthropomorphes
LAAS - Laboratoire d'analyse et d'architecture des systèmes [Toulouse]
4 LARSEN - Lifelong Autonomy and interaction skills for Robots in a Sensing ENvironment
Inria Nancy - Grand Est, LORIA - AIS - Department of Complex Systems, Artificial Intelligence & Robotics
Abstract : Classically robot force control passes through joint torques measurement or estimation. Within this context, classical torque sensing technologies rely on current sensing on motor windings and on torsion sensing on motor shaft. An alternative approach was recently proposed in [1] and combines whole-body distributed 6-axis force/torque (F/T) sensors, gyroscopes, accelerometers and tactile sensors (i.e. artificial skin). A further advantage of this method is that it simultaneously estimates (internal) joint torques and (external) contact forces with no need of joint redesign. As a drawback, the method relies on a model of the robot dynamics, as it consists on reordering the classical recursive Newton-Euler algorithm (RNEA). In this paper we consider the problem of the parametric identification of the robot dynamic model from embedded F/T sensors. We extend recent results on parametric identification [2] by considering an arbitrary reordering of the classical RNEA. The theoretical framework is validated on the iCub humanoid, which is equipped with both 6-axis F/T sensors and joint torque sensors. We estimated the system inertial parameters using only one F/T sensor. We used the obtained parameters to estimate the joint torques (as proposed in [1]) and compared the results with direct joint torque measurements, used in this context only as a ground truth.
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Communication dans un congrès
2015 IEEE International Conference on Robotics and Automation (ICRA 2015), May 2015, Seattle, WA, United States
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Dernière modification le : mardi 11 septembre 2018 - 15:19:07
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  • HAL Id : hal-01131610, version 1

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Silvio Traversaro, Andrea Del Prete, Serena Ivaldi, Francesco Nori. Inertial parameters identification and joint torques estimation with proximal force/torque sensing. 2015 IEEE International Conference on Robotics and Automation (ICRA 2015), May 2015, Seattle, WA, United States. 〈hal-01131610〉

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