Structural Parameter Identification of Articulated Arm Coordinate Measuring Machines

Gao, Guanbin; Zhang, Huaishan; Guo, Yu

doi:10.1155/2016/4063046

Cited by 16 publications

(8 citation statements)

References 22 publications

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“…Due to the existence of linear correlation parameters [22], the Jacobian matrix in the formula ( 8) is not a column full rank matrix. Therefore, the columns of the Jacobian matrix corresponding to the linear correlation parameters are eliminated, and the Jacobian matrix corresponding to linear independent parameters is retained.…”

Section: Kinematic Error Modelmentioning

confidence: 99%

Multi-point calibration method for articulated arm coordinate measuring machine based on an observability index

Wang¹,

Wu²,

Wei³

et al. 2021

Meas. Sci. Technol.

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Kinematic calibration is an effective way to improve the accuracy of articulated arm coordinate measuring machine (AACMM). In this paper, a multi-point calibration method for AACMM based on observation index is proposed. First, the kinematic model is established according to the D-H method, and the measurement error model is established on the basis of the D-H model. Then, the feasibility of using the observation index to evaluate the posture of AACMM is analyzed, and simulations are carried out to verify the correlation between the level of the observation index and the quality of the calibration results. In the simulation, the influence of the number of measurement positions and the number of measurement points on the calibration results are studied in combination with the observability index, and on this basis, a multi-point calibration method is proposed. Finally, experiments are carried out to verify the superiority of the multi-point calibration method. The results show that, compared with the single point calibration method, the multi-point calibration method can improve the measurement accuracy by 31.58%, and the average measurement error of the AACMM after the multi-point calibration method is 0.0091 mm.

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Section: Kinematic Error Modelmentioning

confidence: 99%

Multi-point calibration method for articulated arm coordinate measuring machine based on an observability index

Wang¹,

Wu²,

Wei³

et al. 2021

Meas. Sci. Technol.

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show abstract

“…Gao et al also confirmed the results of [8,25], stating that the calibration depends on the location of the artefact in the workspace. Gao et al [26] proposed a calibration method for AACMMs, based on an iterative algorithm, that optimizes the solution of the kinematic model by comparing three probe coordinates measured by the program with the reference coordinates (considered as real) in each loop. They proved with experiments that this method improved the calibration.…”

Section: Related Workmentioning

confidence: 99%

Rapid and robust on-site evaluation of articulated arm coordinate measuring machine performance

Asmai

Hennebelle

Coorevits

et al. 2018

Meas. Sci. Technol.

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This paper proposes a quick and efficient methodology of uncertainty estimation on measurement results provided by an articulated arm coordinate measuring machine (AACMM). The methodology is highly adapted to industrial applications—a frequent use of these devices—but can also be performed in laboratories. The study stems from an actual need among coordinate measuring arm users to estimate the uncertainty associated with their equipment in specific conditions on the shop floor. It is important to mention that no simple and quick test exists currently to estimate the measurement uncertainty of results obtained with an AACMM in the conditions of the measurement site. In fact, the guidelines and studies addressing measuring machines propose protocols to verify performance or to evaluate uncertainties associated with measurement results, but these protocols are laboratory-type tests (carried out after purchase, re-calibration, etc) and usually take hours to complete.

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“…In the definition of the control law, the dynamic characteristics of the controller at the operating point are mainly concerned, but the influence of load torque is seldom considered. In fact, the load torque can cause significant disturbance to the operation of the servo system, thus changing the operating behavior of the servo system [ 8 – 12 ]. To avoid this situation, the identification of load torque can achieve satisfactory results.…”

Section: Introductionmentioning

confidence: 99%

RBFNN-Enabled Adaptive Parameters Identification for Robot Servo System Based on Improved Sliding Mode Observer

Wang

et al. 2022

Computational Intelligence and Neuroscience

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Effective and accurate parameter identification, especially the identification of load torque, is one of the key factors to improve the control performance of the robot servo system. Sliding mode observer (SMO) has always been a common method for identifying load torque due to its advantages of simple implementation, strong robustness, and fast response. However, due to the discontinuity of the SMO switching function, the system will generate high-frequency chattering, which will reduce the accuracy of load torque identification and affect system performance. In this paper, an adaptive parameter identification method based on an improved sliding mode observer is proposed. A continuous deformation mode of saturation function based on boundary variation is proposed as the switching function to alleviate the chattering phenomenon. Meanwhile, the relationship between the sliding mode gain and the feedback gain of proposed SMO is defined so that it can be selected properly to improve the accuracy of identification, and the radial basis function neural network (RBFNN) is used to adaptively tune the boundary layer gain according to the speed change. Moreover, considering that the identification result of the load torque is related to the moment of inertia and the mismatch of the inertia will cause identification errors, the variable period integration method is proposed to identify the inertia and redefine the calculation period of the load torque and inertia. The effectiveness and superiority of the proposed method are verified by simulation experiments. Experimental results demonstrate that the improved SMO combines observer gain coefficient tuning and inertia matching can smoothly and accurately estimate the value of load torque, which is an adaptive identification method worthy of reference for robot servo system.

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Structural Parameter Identification of Articulated Arm Coordinate Measuring Machines

Cited by 16 publications

References 22 publications

Multi-point calibration method for articulated arm coordinate measuring machine based on an observability index

Multi-point calibration method for articulated arm coordinate measuring machine based on an observability index

Rapid and robust on-site evaluation of articulated arm coordinate measuring machine performance

RBFNN-Enabled Adaptive Parameters Identification for Robot Servo System Based on Improved Sliding Mode Observer

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