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 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.

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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 Vp-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 Vp-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.