Verification of Articulated Arm Coordinate Measuring Machines Accuracy Using LaserTracer System as Standard of Length

Articulated arm coordinate measuring machine (AACMM) is a specific robotic structural instrument, which uses D-H method for the purpose of kinematic modeling and error compensation. However, it is difficult for the existing error compensation models to describe various factors, which affects the accuracy of AACMM. In this paper, a modeling and error compensation method for AACMM is proposed based on BP Neural Networks. According to the available measurements, the poses of the AACMM are used as the input, and the coordinates of the probe are used as the output of neural network. To avoid tedious training and improve the training efficiency and prediction accuracy, a data acquisition strategy is developed according to the actual measurement behavior in the joint space. A neural network model is proposed and analyzed by using the data generated via Monte-Carlo method in simulations. The structure and parameter settings of neural network are optimized to improve the prediction accuracy and training speed. Experimental studies have been conducted to verify the proposed algorithm with neural network compensation, which shows that 97% error of the AACMM can be eliminated after compensation. These experimental results have revealed the effectiveness of the proposed modeling and compensation method for AACMM.

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“…We have the conclusion that the prediction error is less than 0.05 mm in the principle of 2 Sigma [5]. By comparing …”

Section: Error Compensation With Bpnn Modelmentioning

confidence: 83%

“…As an openlinkage structure, the linkages of AACMM are connected by joints one after another, which results in error propagation and accumulation [2,3]. Therefore, the accuracy of AACMM is relatively lower than that of normal coordinate measuring machines [4,5].…”

Section: Introductionmentioning

confidence: 99%

Modeling and Error Compensation of Robotic Articulated Arm Coordinate Measuring Machines Using BP Neural Network

et al. 2017

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“…The coordinate system of the rotary table is the same as the coordinate system of the CMM, as shown in Figure 2. The cat eye reflector of the laser tracer [9][10][11] is fixed on the probe of the CMM as the measured point. The cat eye reflector has the same trajectory as the probe of the CMM.…”

Section: Laser Tracer Multi-station Measurement Systemmentioning

confidence: 99%

Calibration Method for Angular Positioning Deviation of a High-Precision Rotary Table Based on the Laser Tracer Multi-Station Measurement System

et al. 2019

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This paper proposes a calibration method for angular positioning deviation of a high-precision rotary table based on the laser tracer multi-station measurement system. The algorithm error of the calibration method for angular positioning deviation of a high-precision rotary table based on the laser tracer multi-station measurement system was mainly discussed. During the experiments, the laser tracer was fixed on the work surface of the rotary table, and the rotary was fixed on the work surface of the coordinate measurement machine (CMM). The rotary table was rotated with the same angular interval. In this case, an optimization method for calculating the coordinates of a laser tracer station by using Levenberg–Marquardt algorithm and singular value decomposition transform was proposed. Then, the angular positioning deviation of the rotary table was calibrated by an established geometric relationship model between the coordinates of laser tracer stations and the rotation angle of the rotary table. The angular positioning deviation of the high-precision rotary table was as low as ±0.9″, and the error of the calibration method was ±0.4″. The experimental results proved the feasibility of the proposed calibration method. The calibration method proposed in this paper is suitable for the case that the rotary table is not linked with the CMM, especially for large high-precision rotary tables.

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“…However, as a multi-joint serial structure like robotic arm [4,5] AACMMs have lower accuracy comparing with the normal coordinate measuring machines. Limited accuracy of AACMMs restricts the applications and development [6,7].…”

Section: Introductionmentioning

confidence: 99%

A Novel Kinematic Parameters Identification Method for Articulated Arm Coordinate Measuring Machines Using Repeatability and Scaling Factor

Cheng

Wang

Weng

et al. 2018

Mathematical Problems in Engineering

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Kinematic parameters identification and compensation are effective ways to improve the accuracy of articulated arm coordinate measuring machines (AACMMs) and robotic arms without increasing the cost of hardware. Generally, kinematic parameters identification methods based on standard references are relatively high in accuracy but time-consuming and not suitable for industrial sites, while kinematic parameters identification methods based on repeatability are flexible and easy to implement but lack reliability in accuracy. A novel kinematic parameters identification method for AACMMs using repeatability and scaling factor is proposed in this paper, which combines the advantages of methods based on both standard references and repeatability. Through theoretical analysis and numerical simulations, we found that the commonly used single-point-repeatability-based identification method has problems in identifying the length parameters, which is due to that high repeatability cannot guarantee the accuracy of the kinematic parameters and the measurement accuracy of the AACMM. Further analysis showed that the error of the length parameters is determined by a scaling factor which can be used to remove the error of length parameters. Therefore, a two-step novel kinematic parameters identification method for the AACMMs using repeatability and scaling factor was proposed to get accurate parameters with convenient operation. Experimental studies showed the effectiveness of the proposed identification method, which indicated that 93% more error in spatial length can be decreased comparing to the traditional method of repeatability-based identification.

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Verification of Articulated Arm Coordinate Measuring Machines Accuracy Using LaserTracer System as Standard of Length

Cited by 20 publications

References 12 publications

Modeling and Error Compensation of Robotic Articulated Arm Coordinate Measuring Machines Using BP Neural Network

Modeling and Error Compensation of Robotic Articulated Arm Coordinate Measuring Machines Using BP Neural Network

Calibration Method for Angular Positioning Deviation of a High-Precision Rotary Table Based on the Laser Tracer Multi-Station Measurement System

A Novel Kinematic Parameters Identification Method for Articulated Arm Coordinate Measuring Machines Using Repeatability and Scaling Factor

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