A Sufficient Condition for Parameter Identifiability in Robotic Calibration

Gayral, Thibault; Daney, David

doi:10.1007/978-94-007-7214-4_15

Cited by 6 publications

(11 citation statements)

References 3 publications

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“…We call deviation between actual value and corrected nominal value of end position/attitude as the calibration residuals δP, which can be expressed as (10).…”

Section: A Evaluate Calibration Performancementioning

confidence: 99%

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Configuration Optimization for Manipulator Kinematic Calibration Based on Comprehensive Quality Index

et al. 2019

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Kinematic calibration performance is heavily dependent on two factors-the ability of calibration configurations mapping kinematic parameter errors, and the un-modeled errors including joint clearance, thermal expansion, and measurement noise. Therefore, this paper deals with the calibration configuration optimization to reduce the impact of the two factors on calibration performance. We pay particular attention to establish an index for evaluating calibration configuration's quality. Different from other works, the proposed comprehensive quality index can simultaneously reflect configurations' observability and globality. Furthermore, the numerical methods are used to analyze the relationships between the comprehensive quality index and configuration number, and the relationships between calibration performance and configuration number. Based on the above relationships, we provide a feasible solution for determining the calibration configuration number of a specific manipulator. Based on the above work, configuration optimization model is established and solved by particle swarm optimization. The simulation of an eight degree-of-freedom manipulator illustrates the advantages of the proposed method. In 100 calibration simulations, optimized configurations perform better than random configurations, with the position accuracy increased by 43.86% and the attitude accuracy increased by 14.29%.

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“…We call deviation between actual value and corrected nominal value of end position/attitude as the calibration residuals δP, which can be expressed as (10).…”

Section: A Evaluate Calibration Performancementioning

confidence: 99%

“…This phenomenon is caused by two factors. One of the factors is the existence of un-modeled errors [7], including joint clearance [8], thermal expansion [9], and measurement noise [10]. Although these un-modeled errors occupy a small proportion…”

Section: Introductionmentioning

confidence: 99%

Configuration Optimization for Manipulator Kinematic Calibration Based on Comprehensive Quality Index

et al. 2019

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“…Starting from an initial estimate x 0 of x, the Non-Linear Least Squares method performs several solving steps to reduce ∆ x. As J u • ∆ u + J m • ∆ m is assumed to be negligible compared to J x • ∆ x, see [4], j-th step consists in solving the following linear system -with ∆ f and J x computed from previous estimation x j -in the unknown variables x j+1 :…”

Section: Regression Analysismentioning

confidence: 99%

“…Iterative process ends when ∆ x is sufficiently small, and is the same order of magnitude of ∆ u and ∆ m -see [4] for the stop condition. At the end of this iterative process, one classically considers that the quality of the final estimationx relies on the numerical quality of the Jacobian matrix J x , denoted by Identification Matrix.…”

Section: Regression Analysismentioning

confidence: 99%

Robust Design of Parameter Identification

Massein

Daney

Papegay

2017

Advances in Robot Kinematics 2016

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Quality of results computed during parameter identification problems relies on the selection of system's states while performing measurements. This choice usually does not take into account the uncertainty of states and of measures. For identifiability, classical methods focus only on the contribution of model errors on the uncertainty of parameters. We present an alternative approach that tackles this drawback: taking into account influence of all uncertainty sources in order to improve parameter identification robustness to uncertainties. A robotic application example that showcases the differences between approaches is developed as well.

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“…The commonly used approaches include the Newton-Gauss method [12], the Levenberg-Marquardt (L-M) algorithm [13], neural networks (NN) [14], genetic algorithms [15], and many others. However, because most geometric source errors are much smaller than their associated dimensions, the most common practice is to use linear least squares for dealing with error parameter identification of robotic mechanisms [16][17][18][19][20]. The procedure involves first formulating a linearized map between the pose error twist and all the possible source errors at the link/joint level.…”

Section: Introductionmentioning

confidence: 99%