Fiber positioning technology is widely used in spectroscopic telescopes, and the accurate identification of the fiber position on the focal plane directly affects the efficiency of the astronomical spectrum. At present, fiber positioning usually uses the “back-illuminate” technique to illuminate one end of the fiber. The other end of the fiber is used for detection. The fiber could be stressed or twisted during locator motion, resulting in a difference between the detected fiber position and the actual fiber core. However, the fiber-optic back-illuminated device in the spectrometer system increases the complexity of the system and the time loss of fiber positioning. This paper attempts to use a new method combining image processing with deep learning to identify the fiber ferrule by the front-illuminated method. We built an experimental platform in the lab and experimented with a CMOS camera and telecentric lens. We tested the repeated errors and displacement measurement errors of the two methods. A series of comparative experimental results show that the final detection accuracy of this method can meet the needs of optical fiber positioning in the laboratory, although it has not yet reached the accuracy of the back-illuminated approach. In the future, if the light source and fiber ferrule were specifically designed for the front-illuminated method, its accuracy could be further improved.
The entire system of the LAMOST ((Large Sky Area Multi-Object Fiber Spectroscopic Telescope) requires high positioning accuracy of the fiber positioning unit. In order to acquire accurately target celestial objects, fiber view metrology system for positioners can efficiently and accurately detect thousands of fiber spots simultaneously in a large scale is required. The traditional method mainly used the "back-illumination method" for detection. With the advent of 8k*6k high-resolution CMOS cameras, fiber position detection based on the "front-illumination method" becomes feasible. This paper mainly studies the fiber position detection based on the "front-end illumination image processing method". The image is preprocessed first, and then the edge detection of a large number of fiber target points in the image is performed. Considering the constant radius of the white ceramic head where the fiber is located, the article proposes a "front-illuminated" image algorithm based on radius-based Hough space conversion and optimal radius error center search. This algorithm improves the speed and accuracy of fiber pixel coordinate detection. At the same time, it can be coordinated and compared with the "back-illuminated method" to further optimize and improve the detection accuracy of the fiber position.
LAMOST, as the astronomical telescope with the highest spectrum acquisition efficiency in the world, requires high positioning accuracy, the maximum allowable positioning error is only 40μm. Due to various aberrations, the general photogrammetry system cannot meet the requirements of detecting high-precision optical fiber positioning errors. In this study, we proposed a double telecentric measurement system to detect accurately the actual position of fiber positioner by taking advantage of ultra-low distortion and ultra-wide depth of field of telecentric lens. In this paper, the main sources of telecentric lens distortion were analyzed, and the calibration method of polynomial calibration model and dot matrix calibration target was adopted, and the validity of the five parameters obtained by calibration is verified by experiments. The experimental results showed that all optical fibers reached the target after two steps of approximation, and the errors met the positioning requirements.
During the LAMOST observation, to accurately align a large number of fibers with the target star positions, we used a closed-loop feedback system based on visual measurement in fiber positioner operation mode. The fiber was illuminated at the end of the spectrometer and the fiber light spots on the other end of the focal plane could be captured by the metrology system for positioning. The system can have a larger field of view and a single measurement can cover thousands of fibers. The metrology accuracy which is based on camera accurate calibration, is critical in the fiber positioning system. In general, calibration of a standard camera requires a reference surface with a known precise position marker and covering the camera's field of view. Theoretically, it is necessary to design a standard target surface that covers the camera's field of view to calibrate the camera's error. However, it is not realistic to manufacture and install a large standard target that meets the accuracy requirement. To ensure that the camera calibration error is within the limited range, and the fiber positioner can obtain higher positioning accuracy, we use the focal plane unit hole to insert a dedicated reference unit to serve as its calibration reference. In this paper, a reference fiber unit structure was designed according to the requirements of closedloop positioning. Through the test experiment on the reference fibers, it was finally verified that the reference fiber unit meets the accuracy requirements of closed-loop control.
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