The absolute positioning accuracy of a robot is an important specification that determines its performance, but it is affected by several error sources. Typical calibration methods only consider kinematic errors and neglect complex non-kinematic errors, thus limiting the absolute positioning accuracy. To further improve the absolute positioning accuracy, we propose an artificial neural network optimized by the differential evolution algorithm. Specifically, the structure and parameters of the network are iteratively updated by differential evolution to improve both accuracy and efficiency. Then, the absolute positioning deviation caused by kinematic and non-kinematic errors is compensated using the trained network. To verify the performance of the proposed network, the simulations and experiments are conducted using a six-degree-of-freedom robot and a laser tracker. The robot average positioning accuracy improved from 0.8497 mm before calibration to 0.0490 mm. The results demonstrate the substantial improvement in the absolute positioning accuracy achieved by the proposed network on an industrial robot.

In this article, a method of error source analysis and detection to improve the angle measurement accuracy of rotary encoders in precision measuring instruments is proposed. The angle measurement error caused by the installation eccentricity of the grating disk and the radial error motion of the rotating shaft is analyzed, and the error model is built. The method of measuring the radial error motion is introduced, and the visual system and image processing technology is proposed to detect the eccentricity. The verification experiment by the use of an autocollimator and a polygon is carried out. The residual error after comparison within ±6″ accounts for 9% of the angle measurement error. The proposed error model is verified, and the angle measurement error can be predicted if the installation eccentricity and radial error motion are known.

The measurement accuracy of the precision instruments that contain rotation joints is influenced significantly by the rotary encoders that are installed in the rotation joints. Apart from the imperfect manufacturing and installation of the rotary encoder, the variations of ambient temperature could cause the angle measurement error of the rotary encoder. According to the characteristics of the $2\pi$ periodicity of the angle measurement at the stationary temperature and the complexity of the effects of ambient temperature changes, the method based on the Fourier expansion-back propagation (BP) neural network optimized by genetic algorithm (FE-GABPNN) is proposed to improve the angle measurement accuracy of the rotary encoder. The proposed method, which innovatively integrates the characteristics of Fourier expansion, the BP neural network and genetic algorithm, has good fitting performance. The rotary encoder that is installed in the rotation joint of the articulated coordinate measuring machine (ACMM) is calibrated by using an autocollimator and a regular optical polygon at ambient temperature ranging from 10 to 40 °C. The contrastive analysis is carried out. The experimental results show that the angle measurement errors decrease remarkably, from 110.2″ to 2.7″ after compensation. The mean root mean square error (RMSE) of the residual errors is 0.85″.

In this paper, a new kinematic parameter error calibration method based on the Levenberg–Marquard and the beetle antennae search algorithm is proposed to enhance the positioning accuracy of the industrial robot. Firstly, the Modified Denavit–Hartenberg model is chosen to establish the kinematic model for the industrial robot. Secondly, the kinematic parameter errors are calibrated by Levenberg–Marquard algorithm and then obtain the kinematic parameter errors of the industrial robot. Thirdly, these kinematic parameter errors are taken as the center values for the initial individual of the beetle antennae search algorithm. The kinematic parameter errors are accurately calibrated using the beetle antennae search algorithm to obtain the best value for the kinematic parameter errors. Finally, experimental verification results demonstrate that the positioning error of the industrial robot is decreased from 0.7332 mm to 0.1392 mm by using the proposed Levenberg–Marquard and beetle antennae search algorithm. The results also demonstrate that the proposed Levenberg–Marquard and beetle antennae search algorithm calibrate the kinematic parameter errors of the industrial robot effectively and enhance the positioning accuracy of the industrial robot significantly.

In this paper, a 3D artifact is proposed to calibrate the kinematic parameters of articulated arm coordinate measuring machines (AACMMs). The artifact is composed of 14 reference points with three different heights, which provides 91 different reference lengths, and a method is proposed to calibrate the artifact with laser tracker multi-stations. Therefore, the kinematic parameters of an AACMM can be calibrated in one setup of the proposed artifact, instead of having to adjust the 1D or 2D artifacts to different positions and orientations in the existing methods. As a result, it saves time to calibrate the AACMM with the proposed artifact in comparison with the traditional 1D or 2D artifacts. The performance of the AACMM calibrated with the proposed artifact is verified with a 600.003 mm gauge block. The result shows that the measurement accuracy of the AACMM is improved effectively through calibration with the proposed artifact.

This paper presents a novel impact rotary motor based on a piezoelectric tube actuator with helical interdigitated electrodes which has a compact structure and high resolution. The assembled prototype motor has a maximum diameter of 15 mm and a length of 65 mm and works under a saw-shaped driving voltage. The LuGre friction model is adopted to analyze the rotary motion process of the motor in the dynamic simulations. From the experimental tests, the first torsional resonant frequency of the piezoelectric tube is 59.289 kHz with a free boundary condition. A series of experiments about the stepping characteristics of different driving voltages, duty cycles, and working frequencies are carried out by a laser Doppler vibrometer based on a fabricated prototype motor. The experimental results show that the prototype rotary motor can produce a maximum torsional angle of about 0.03° using a driving voltage of 480 V_p-p (peak-to-peak driving voltage) with a duty ratio of 0% under a small friction force of about 0.1 N. The motor can produce a maximum average angle of about 2.55 rad/s and a stall torque of 0.4 mN∙m at 8 kHz using a driving voltage of 640 V_p-p with a duty ratio of 0% under a large friction force of about 3.6 N. The prototype can be driven in forward and backward motion and is working in stick-slip mode at low frequencies and slip-slip mode at high frequencies.