A Generic Instrumental Variable Approach for Industrial Robot Identification

Janot, Alexandre; Vandanjon, Pierre Olivier; Gautier, Maxime

doi:10.1109/tcst.2013.2246163

Cited by 108 publications

(119 citation statements)

References 28 publications

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“…Before presenting the identification result, it is to be noticed that during the identification process, some small parameters remain poorly identifiable because they have no significant contribution in the joint torques. These parameters can be cancelled in order to keep a set of essential parameters of a simplified dynamic model with a good accuracy [16] [26]. The essential parameters are calculated using an iterative procedure starting from the base parameters estimation.…”

Section: B Identification Resultsmentioning

confidence: 99%

Force calibration of KUKA LWR-like robots including embedded joint torque sensors and robot structure

Gautier

Jubien

2014

2014 IEEE/RSJ International Conference on Intelligent Robots and Systems

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The Kuka LWR is equipped with torque sensors mounted into the actuated joints. Each torque sensor is calibrated separately before it is mounted on the robot. This needs a second calibration at the last stage of the assembling of the robot in order to take into account the effect of the robot structure through it's jacobian matrix. This final calibration is necessary to improve the accuracy of the estimation of the interaction wrench of the robot with its environment. However, the proposed calibration techniques are usually complicated, time-consuming, and must be carried out before assembling the sensors on the robot. In this paper, a simple and fast method for calibrating the sensors once they are assembled on the robot is presented. The method is based on the least squares solution of an over-determined linear system obtained with the robot inverse dynamic identification model in which are included the sensor gains. This model is calculated with available sensor measurement and joint position sampled data while the robot is tracking some reference trajectories without load on the robot and some trajectories with a known payload fixed on the robot. The method is experimentally validated on the Kuka LWR4+ but can be applied to any similar kind of robot equipped with joint torque sensors. 978-1-4799-6934-0/14/$31.00 ©2014 IEEE 416

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Section: B Identification Resultsmentioning

confidence: 99%

Force calibration of KUKA LWR-like robots including embedded joint torque sensors and robot structure

Gautier

Jubien

2014

2014 IEEE/RSJ International Conference on Intelligent Robots and Systems

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show abstract

“…Before presenting the identification result, it is to be noticed that during identification process, some small parameters remain poorly identifiable because they have no significant contribution in the joint torques. These parameters can be cancelled in order to keep a set of essential parameters of a simplified dynamic model with a good accuracy [16] [27]. The essential parameters are calculated using an iterative procedure starting from the base parameters estimation.…”

Section: B Identification Resultsmentioning

confidence: 99%

In situ calibration of joint torque sensors of the KUKA LightWeight robot using only internal controller data

Briot¹,

Gautier

Jubien³

2014

2014 IEEE/ASME International Conference on Advanced Intelligent Mechatronics

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International audienceThe Kuka LWR is equipped with torque sensors mounted on the link side of the actuated joints. Each torque sensor is calibrated separately before it is mounted on the robot. This needs a second calibration at the last stage of the assembling of the robot in order to take into account the effect of the robot structure. This final in situ calibration is necessary to improve the accuracy of the estimation of the interaction wrench of the robot end-effector with its environment. However, the proposed calibration techniques are usually complicated, time-consuming, and must be carried out before assembling the sensors on the robot. In this paper, a simple and fast method for calibrating the sensors once they are assembled on the robot is presented. The method is based on the least squares solution of an over-determined linear system obtained with the robot inverse dynamic identification model in which are included the sensor gains. This model is calculated with sensor measurement and joint position sampled data available in the controller while the robot is tracking some reference trajectories without load on the robot and some trajectories with a known payload fixed on the robot. The method is experimentally validated on the Kuka LWR4+ but can be applied to any similar kind of robot equipped with joint torque sensors

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“…Regarding the first issue, the development of advanced techniques has made accurate robot dynamic models available [16,34] so that they can be safely exploited for the considered problem, as shown in the previous subsection. Regarding the second issue, it is necessary to discuss better the role of the observer, which is essential in order to overcome the lack of an additional temperature sensor.…”

Section: Discussionmentioning

confidence: 99%

“…It is important to note at this point that, in general, the measured torque τ is the sum between the friction term τ f and the term τ d = F(θ,θ,θ) composed by the inertial, Coriolis and gravitational torques, which can be estimated from experimental tests [16,34]. Thus, the friction torque can be computed as τ f = τ − τ d .…”

Section: Identificationmentioning

confidence: 99%

Modelling the temperature in joint friction of industrial manipulators

Simoni¹,

Beschi²,

Legnani³

et al. 2017

Robotica

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In this paper, a new model for joint dynamic friction of industrial robot manipulators is presented. In particular, the effects of the temperature in the joints are considered. A polynomial-based model is proposed and the parameter estimation is performed without the need of a joint temperature sensor. The use of an observer is then proposed to compensate for the uncertainty in the initial estimation of the temperature value. A large experimental campaign show that the model, in spite of the simplifying assumptions made, is effective in estimating the joint temperature and therefore the friction torque during the robot operations, even for values of velocities that have not been previously employed.

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A Generic Instrumental Variable Approach for Industrial Robot Identification

Cited by 108 publications

References 28 publications

Force calibration of KUKA LWR-like robots including embedded joint torque sensors and robot structure

Force calibration of KUKA LWR-like robots including embedded joint torque sensors and robot structure

In situ calibration of joint torque sensors of the KUKA LightWeight robot using only internal controller data

Modelling the temperature in joint friction of industrial manipulators

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