In the integrated processing of large components in the aerospace field, for the end servo industrial robot positioning accuracy needs, often using binocular vision positioning method. This method can accurately measure the end position but is limited by the restricted field of view and small depth of field. So, a monocular camera is needed for servo-guiding the processing end of the robot arm. Therefore, a monocular depth estimation method based on improved Yolov8 and CNN fusion for mobile machining with large field-of-view is proposed in this paper. Firstly, a dataset construction method based on virtual-real fusion is proposed to solve the problem that the depth information corresponding to the training set images is difficult to measure; secondly, the proposed Yolov8sim-CNN cascade neural network can realize the measurement of fast localization and depth prediction of the target machining workpiece and realize the servo-guidance of the robot arm end. The experimental results show that the proposed Yolov8sim-CNN network can ensure high detection accuracy, and the detection accuracy is substantially improved compared with the method of CNN-only, which indicates that the proposed method has better fitting ability and higher accuracy.
Transmitted torque of permanent magnet couplers (PMCs) is the core index to evaluate its transmission performance. A semi-analytical calculation method for transmitted torque of the PMC is established to solve those problems that the analytic method is difficult to fully consider the specific details of the three-dimensional (3D) PMC, the calculation accuracy is insufficient, and the finite element model (FEM) has numerical oscillation and the calculation accuracy is float. The analytical model and the FEM are combined in this method, can effectively calculate and predict the transmission performance of the permanent magnet coupler. First, according to geometric structure, a magnetic equivalent circuit model is established to solve internal magnetic induction intensity. Based on the relationship between the eddy current density and the magnetic induction intensity, and considering the 3D end effect, an analytical model of transmitted torque is constructed. Second, a 3D FEM of the unipolar PMC is established, and the transmission performance by the finite element calculation is obtained. On this basis, using the entropy method to combine the magnetic circuit equivalent model with the FEM, a semi-analytical calculation method for the transmitted torque is presented. Finally, an experimental platform is built to verify the method. The results show that under normal working conditions, the proposed method for calculating the transmitted torque has a good consistency, and the relative error is within 6.5%. This method can provide a theoretical basis for the analysis, design, and optimization of PMCs.
Binocular stereo vision technology plays an essential role in the intelligent manufacturing system due to the advantages of high accuracy and non-contact. However, for the measurement of components with highly reflective surfaces such as metal and ceramic, the specular reflections affected by the complex light field lead to the failure of feature matching and the decrease of measurement accuracy. This paper proposes an imaging strategy for binocular vision and a high dynamic image processing method to suppress the effect of specular reflection for stereo matching. Firstly, the mechanism of highlight generation in the image is analyzed by combining the illumination reflection theory of the BRDF model. Then, a binocular vision system with a parallel optical axis is built to capture images under different illumination conditions. The image processing algorithm of high dynamic range image fusion is studied, and an algorithm based on tone mapping and weight fusion is implemented to remove the high-lights. Finally, an experiment was performed to verify the effectiveness of the proposed method by a robust and fast matching algorithm. An image evaluation method based on BRISQUE further demonstrates the effectiveness of the method. Compared to the original images, the quality score of the HDR images is lower, which means that the processed images are of better quality. Moreover, the method provides an increase of 23.33% in image matching accuracy, which verifies the availability applied in the measurement of highly reflective surface components.
KEYWORDS: Clouds, Denoising, Digital filtering, Signal to noise ratio, Image segmentation, Optical filters, Visualization, Data acquisition, Principal component analysis
The assembly gap between components is very vital for the evaluation of assembly quality of aircrafts. Due to the limits of gap size and operation space, the assembly gap needs to be indirectly calculated by the measurements of surface of components instead of plug gauge test. However, the surface constituted of point cloud is usually mixed with different types of noise ,which severely affects the evaluation of assembly gap. To remove these different types of noise simultaneously with high efficiency, a classified denoising method combining with an improved bilateral filtering and median filtering was proposed. Firstly, based on the principal component analysis, a new coordinate system was established to achieve the homogeneity of coordinates of point cloud. Then, an improved median filtering method on the basis of region segmentation (RSMF) was used to remove large-scale noise. Next, the fast bilateral filtering method based on threshold segmentation (TSBF) was proposed to remove small-scale noise. Finally, a measurement experiment of aircraft component was performed to verify the effectiveness of the proposed method. Experimental results showed that the proposed method could not only reduce measurement error including RMSE (Root Mean Square Error), but also improve SNR (Signal Noise Ratio) and PSNR (Peak Signal to Noise Ratio) of point cloud data.
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