Observability index selection for robot calibration

Sun, Yu; Hollerbach, John M.

doi:10.1109/robot.2008.4543308

Cited by 119 publications

(72 citation statements)

References 16 publications

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“…In the paper of Sun and Hollerbach (2008a) the choice of the observability index for the pose calculation and in Sun and Hollerbach (2008b) and Zhuang (1994) selection algorithm for the computation of optimal poses are presented. We used the algorithm presented in Sun and Hollerbach (2008b) where the minimum singular value of the covariance matrix = T is maximized.…”

Section: Calculation Of Optimal Poses For the Calibrationmentioning

confidence: 99%

A two-stage calibration method for industrial robots with joint and drive flexibilities

Neubauer

Gattringer

Müller³

et al. 2015

Mech. Sci.

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Abstract. Dealing with robot calibration the neglection of joint and drive flexibilities limit the achievable positioning accuracy significantly. This problem is addressed in this paper. A two stage procedure is presented where elastic deflections are considered for the calculation of the geometric parameters. In the first stage, the unknown stiffness and damping parameters are identified. To this end the model based transfer functions of the linearized system are fitted to captured frequency responses of the real robot. The real frequency responses are determined by exciting the system with periodic multisine signals in the motor torques. In the second stage, the identified elasticity parameters in combination with the measurements of the motor positions are used to compute the real robot pose. On the basis of the estimated pose the geometric calibration is performed and the error between the estimated end-effector position and the real position measured with an external sensor (laser-tracker) is minimized. In the geometric model, joint offsets, axes misalignment, length errors and gear backlash are considered and identified. Experimental results are presented, where a maximum end-effector error (accuracy) of 0.32 mm and for 90 % of the poses a maximum error of 0.23 mm was determined (Stäubli TX90L).

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Section: Calculation Of Optimal Poses For the Calibrationmentioning

confidence: 99%

A two-stage calibration method for industrial robots with joint and drive flexibilities

Neubauer

Gattringer

Müller³

et al. 2015

Mech. Sci.

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“…Maximization of 1 indicates more effect of kinematic parameter errors exerted on the estimate pose error. This is also true for the index 5 , proposed by Sun and Hollerbach [5] and based on the sum of the reciprocals of the nonzero singular values mentioned above. The second index proposed by Driels and Pathre [3], here termed 2 , aims to maximizing the inverse of the condition number of the identification matrix, which is equivalent to the smallest nonzero singular value divided by the largest one.…”

Section: Introductionmentioning

confidence: 73%

A Novel Optimal Design of Measurement Configurations in Robot Calibration

Jia

Wang

Chen

et al. 2018

Mathematical Problems in Engineering

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Robot calibration highly depends on redundant measurement configurations to collect enough sample data for higher accuracy, but excessive measurements seem to be uneconomical and time-consuming. Thus lots of observability indexes to evaluate the goodness of the measurement configurations have been proposed. However, in some circumstances, it is of critical importance to obtain accurate kinematic parameters and estimate the end-effector pose precisely at the same time. Obviously one single observability index can hardly meet this need yet. Accordingly, after analyzing the essential constrains of robot calibration and the influence of measurement configurations on the observability indexes, an optimization model with two observability indexes to be taken into consideration is proposed in this paper, and then the existing DETMAX algorithm is modified to seek optimal design of measurement configurations, by adopting a set-constructing method and a set-shrinking method. Much better results have been obtained by simulation study, which implies that the proposed model and the modified DETMAX algorithm perform well in both kinematic parameter identification and pose estimation of the end-effector of the robot.

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“…As proved in [19], if the goal of the calibration is to achieve minimum variance of estimated parameters, the best observability index is…”

Section: Alternative Criterionmentioning

confidence: 99%

“…The Jacobian matrix is calculated with the joints angles and the nominal parameters. Column-scaling is not used since the determinant-based observability index is invariant to column scaling [19].…”

Section: A Kinematic Modelmentioning

confidence: 99%

Active robot calibration algorithm

Sun

Hollerbach

2008

2008 IEEE International Conference on Robotics and Automation

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Abstract-This paper presents a new updating algorithm to reduce the complexity of computing an observability index for kinematic calibration of robots. An active calibration algorithm is developed to include an updating algorithm in the pose selection process. Simulations on a 6-DOF PUMA robot with 27 unknown parameters shows that the proposed algorithm performs more than 50,000 times better than exhaustive search based on randomly generated designs.

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Observability index selection for robot calibration

Cited by 119 publications

References 16 publications

A two-stage calibration method for industrial robots with joint and drive flexibilities

A two-stage calibration method for industrial robots with joint and drive flexibilities

A Novel Optimal Design of Measurement Configurations in Robot Calibration

Active robot calibration algorithm

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