Abstract
Advanced robotic systems will encounter a rapid breakthrough opportunity and become increasingly important, especially with the aid of the accelerated development of artificial intelligence technology. Nowadays, advanced robotic systems are widely used in various fields. However, the development of artificial intelligence-based robot systems for structural health monitoring of tunnels needs to be further investigated, especially for data modeling and intelligent processing for noises. This research focuses on integrated B-spline approximation with a nonparametric rank method and reveals its advantages of high efficiency and noise resistance for the automatic health monitoring of tunnel structures. Furthermore, the root-mean-square error and time consumption of the rank-based and Huber’s M-estimator methods are compared based on various profiles. The results imply that the rank-based method to model point cloud data has a comparative advantage in the monitoring of tunnel, as well as the large-area structures, which requires high degrees of efficiency and robustness.
Highlights
Intelligent robotic systems will achieve significant development utilizing the rapid breakthrough of artificial intelligence (AI) technology and it will become increasingly important in various fields
Autonomous fusion of vision and laser based on convolutional neural network (CNN) was applied for vehicle environment[1]; a hardware platform was employed for an intelligent vehicle based on a driving brain[2]; multi-view clustering was studied based on graph regularized nonnegative matrix factorization for object recognition[3]; and a framework was investigated for road traffic risk assessment with a prediction model.[4]
This article proposed an rank-based method (RBM) B-spline surface modeling method which could reconstruct an automatic and robust surface model based on the laser scanning point cloud data to improve the quality of 3D parametric asbuilt modeling and the efficiency of detecting the structures’ deformation
Summary
Nowadays, advanced robotic systems and AI-based approaches are being investigated in many fields, constantly integrating and changing human lives profoundly, especially in the field of intelligent transportation. It is noteworthy that vision-based robotic systems are gaining increasing attention for health monitoring of large-scale structures like tunnels and rails, where one important issue is to detect automatically deformations and damages of the structures monitored. This requires the recognition and Faculty of Civil Engineering and Geodetic Science, Leibniz University Hannover, Hannover, Germany. Arastounia[14] refined tunnel models by residual analysis and Baarda’s data snooping method to eliminate outliers These filtering methods need extra time consumption and human labor as well as expertise. The issue of intelligent and robust point cloud modeling for the structural health monitoring of tunnels is still challenging
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