## 1 Introduction
Although the above researches effectively improved the measurement accuracy of the LTDS for different objects, they did not involve the study of the dynamic response performance of the LTDS. For the dynamic response speed problem, [PERSON] [18] developed a new type of LTDS which could measure the surface of a train with a speed of 64 km/h, but the accuracy was low. [PERSON] [19] proposed a dual-view triangulation structure to reduce the measurement error caused by the change of laser speckle when the measured object moved laterally. However, the structure was too large to be integrated. Thus, the LK-G series LTDS developed by [PERSON] [20, 21] could achieve high-speed and high-precision measurement synchronously (50 KHz sampling frequency and accuracy of 0.05% F.S) through a highly integrated design. However, the LK-G series sensor took 4096 times, on average, to improve the measurement repeatability, and it relied on the sampling speed of customized imaging devices, with the disadvantage of complex steps that resulted in higher production costs. In addition, imaging characteristics influence the accuracy of an optical measurement system. With the development of computer pattern recognition, researchers have carried out recognition and feature extraction for various optical images. [PERSON] [22] realized the detection of a dynamic laser spot by auto-associative multilayer perceptron (AAMLP) but did not extract the detailed information of the spot. [PERSON] [23] adopted a supervised classification algorithm based on a \(t\)-test filter for spectral feature classification in a complex data environment. [PERSON] [24] proposed a method combining deep learning with a physical model to recover the information of the measured object from a single diffraction intensity diagram, but the imaging time of this method was too long. [PERSON] [25] developed an object recognition framework based on a fast convolution neural network, which could further extract more detailed features of the image and improved the speed of the model. Although the above studies had forward-looking significance, they had high requirements for the performance of the hardware system, which was not conducive to integrated development and promotion of LTDS. Therefore, we need a simpler laser imaging recognition method that requires less hardware. Moreover, in the particular application of a dispensing robot, it is necessary to quickly distinguish the dynamic and static imaging and to adjust the imaging to ensure measurement accuracy. In this paper, an LTDS was proposed for a dispensing robot. By using the adjustment algorithm of laser imaging waveform, a method of \"replacing hardware with software\" was adopted to save hardware cost, and high-precision measurements in a dynamic scene were realized to meet the application requirements of a dispensing robot. First, the principle of dispensing robot system and the laser spot location method of the LTDS were introduced. Secondly, from the static and dynamic point of view, we studied the characteristics of an actual laser imaging waveform, in which the measured objects were a white diffuse reflector and a black frosted reflector, respectively. The optimal peak intensity of the imaging waveform was determined by least square fitting. Then, considering the actual working scene of a dispensing robot, a fast waveform adjustment algorithm was proposed. According to the dynamic centroid position shift of the imaging waveform, the static and dynamic working modes of the sensor were distinguished, and the peak intensity of the waveform was adjusted to different intervals by linear iteration. Finally, based on the dispensing robot experimental platform, we verified the repeatability accuracy and the step dynamic response characteristics of the sensor.