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Laser point clouds are a 3D reconstruction method with wide range, high accuracy and strong adaptability. Therefore, the purpose is to discover a construction point cloud extraction method that can obtain complete information about the construction of rebar, facilitating construction quality inspection and tunnel data archiving, to reduce the cost and complexity of construction management.

Firstly, this paper analyzes the point cloud data of the tunnel during the construction phase, extracts the main features of the rebar data and proposes an M-E-L recognition method. Secondly, based on the actual conditions of the tunnel and the specifications of Chinese tunnel engineering, a rebar model experiment is designed to obtain experimental data. Finally, the feasibility and accuracy of the M-E-L recognition method are analyzed and tested based on the experimental data from the model.

Based on tunnel morphology characteristics, data preprocessing, Euclidean clustering and PCA shape extraction methods, a M-E-L identification algorithm is proposed for identifying secondary lining rebars in highway tunnel construction stages. The algorithm achieves 100% extraction of the first-layer rebars, allowing for the three-dimensional visualization of the on-site rebar situation. Subsequently, through data processing, rebar dimensions and spacings can be obtained. For the second-layer rebars, 55% extraction is achieved, providing information on the rebar skeleton and partial rebar details at the construction site. These extracted data can be further processed to verify compliance with construction requirements.

This paper introduces a laser point cloud method for double-layer rebar identification in tunnels. Current methods rely heavily on manual detection, lacking objectivity. Objective approaches for automatic rebar identification include image-based and LiDAR-based methods. Image-based methods are constrained by tunnel lighting conditions, while LiDAR focuses on straight rebar skeletons. Our research proposes a 3D point cloud recognition algorithm for tunnel lining rebar. This method can extract double-layer rebars and obtain construction rebar dimensions, enhancing management efficiency.

To overcome the shortcomings of traditional dynamic parameter identification methods in accuracy and efficiency, this paper aims to propose a dynamic parameter identification method based on improved iterative reweighted least squares (IIRLS) algorithm.

First, Newton–Euler method is used to establish the dynamic model of the robot, which is linearized and reorganized. Then, taking the improved Fourier series as the excitation trajectory, the optimization model with objective function is established and optimized. Then, the manipulator runs the optimized trajectory and collects the running state of the joint. Finally, the iterative process of iterative reweighted least squares (IRLS) algorithm is improved by combining clustering algorithm and matrix inversion operation rules, and the dynamic model of robot is identified by using the processed collected data.

Experimental results show that, compared with the IRLS algorithm, the root mean square of the proposed IIRLS algorithm is reduced by 4.18% and the identification time is reduced by 94.92% when the sampling point is 1001. This shows that IIRLS algorithm can identify the dynamic model more accurately and efficiently.

It effectively solves the problem of low accuracy and efficiency of parameter identification in robot dynamic model and can be used as an effective method for parameter estimation of robot dynamic model, which is of great significance to the research of control method based on robot model.