Application of Measurement Robots based on Deep Learning in Building Tilt Stability Monitoring

Abstract

In order to better understand the application of measuring robots in monitoring the stability of building inclination, this paper proposes a deep learning based algorithm for analyzing the trend of building inclination using measuring robots. The author first proposes a method of using measuring robots to monitor the tilting stability of buildings. Based on the principle of laser ranging, construct a robot structure and laser ranging model to achieve information exchange between different units. Secondly, based on simulation analysis, the position relationship between the laser and the building was marked with triangular coordinates, and the inclination of the building was marked with robot benchmarks. The simulation process was designed. By using the forward crossing method to evaluate the accuracy of tilt monitoring and comprehensively monitoring the three-dimensional angle of free station setting, the problem of low monitoring accuracy in traditional methods has been effectively solved. Finally, the experimental results show that using this method, the accuracy of building tilt monitoring can reach 99%, which is 7% higher than traditional measurement methods. Due to the low efficiency, high cost, and low accuracy of traditional manual monitoring work, it can no longer meet the requirements of modern engineering measurement. Therefore, high-precision measurement robots are used for tilt stability monitoring. Compared with traditional monitoring, high-precision measurement robots can achieve high-precision, high-efficiency, and low-cost monitoring with faster speed, higher efficiency, and strong automation capabilities.