Realizing accurate positioning with the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) closed-loop system depends on accurate high-precision calibration of the visual measurement system, which has a great impact on collision avoidance and accurate positioning. We designed fiducial fibers for the calibration of the LAMOST closed-loop system to provide accurate fiducial positions for visual measurement. The benchmark position accuracy of the fiducial fibers is a key factor affecting the accuracy of the visual measurement system; the more accurate the fiducial fiber positions are, the higher the visual measurement correction accuracy. In this study, three measurement methods were used to obtain the fiducial fiber positions, namely, measuring the hole positions using a coordinate measuring machine, imaging the fiducial fibers using a calibrated photographic system, and directly measuring the fiducial fiber spatial positions using a laser tracker. By evaluating the fiber positions obtained via the three methods, we can obtain a stable and reliable fiducial fiber position benchmark. A fiducial fiber positions evaluation method based on an optimal residual criterion is proposed, and the optimal residual solution for a small calibration target (SCT) is used to evaluate the optimal fiducial fiber measurement method. Specifically, the fiducial positions obtained via each of the three methods are used to invert the camera calibration parameters, which are then used to calculate the physical position of an SCT. Finally, the residual value between the calculated and theoretical positions is taken as the standard for evaluating the fiducial fiber measurement benchmark performance. The results show that the fiducial fiber positions measured using the laser tracker can be applied to effectively calibrate the photographic system, enabling the LAMOST vision measurement system to achieve a positioning accuracy of nearly 10 μm with the camera 20 m from the focal surface, whereas the accuracy is within 20 μm for ∼95 % of the measurement points.
In the process of robot target recognition, depth cameras and LIDAR are often used as extended sensors. The data collected by both have their own advantages and disadvantages, LIDAR can obtain accurate position information but not the morphology information of the object, while depth cameras can obtain abundant image information but cannot obtain accurate three-dimensional position information of objects. To better achieve the robot's recognition of specific targets, we fused the two information sources and obtained point cloud data with RGB information. To solve the problem of inconsistency in the coordinate system of the sensor system, we identify a specific calibration plate and propose an ellipse identification method for fault point clouds. To solve the problem of LIDAR point clouds sparsity, we compare the improved completion algorithm based on computer graphics and the completion algorithm based on deep learning, we figure out that the method of computer graphics meets our expectations better based on the fact that it have a larger number of point clouds. Finally, The accuracy of the method based on computer graphics is measured, and the errors of length and width are only 0.003% and 3.64%, which proves that the proposed method can meet the fusion accuracy in small indoor environment.
The fiber positioning robot is an important part of LAMSOT's acquisition of celestial spectra, and the fiber positioning robot is routinely maintained every summer to ensure that its positioning accuracy meets the observation requirements. At present, in the process of maintaining the fiber positioning robot, the cause of the fault is analyzed by manually disassembling the fiber positioning robot, which takes a long time and is inefficient. In order to quickly locate the cause of the fault of the fiber positioning robot, this paper proposes to obtain the repeated positioning accuracy and actual rotation angle data of the fiber positioning robot through the fiber positioning robot motion accuracy experiment. Taking the experimental data as the robot performance diagnosis index, the fault cause of the fiber positioning robot is analyzed, and the performance diagnosis index and the fault cause are corresponded, and the fault diagnosis method of the fiber positioning robot is obtained. This method has high accuracy in identifying the cause of faults, effectively improves the maintenance efficiency of fiber positioning robots, greatly reduces labor and time costs, and has reference value for the maintenance and design of fiber positioning robots using similar double rotary gear transmission.
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