This paper introduces an autonomous robot designed for in-pipe structural health monitoring of oil/gas pipelines. This system employs a 3D Optical Laser Scanning Technical Vision System (TVS) to continuously scan the internal surface of the pipeline. This paper elaborates on the mathematical methodology of 3D laser surface scanning based on dynamic triangulation. This paper presents the mathematical framework governing the combined kinematics of the Mobile Robot (MR) and TVS. It discusses the custom design of the MR, adjusting it to use of robustized mathematics, and incorporating a laser scanner produced using a 3D printer. Both experimental and theoretical approaches are utilized to illustrate the formation of point clouds during surface scanning. This paper details the application of the simple and robust mathematical algorithm RANSAC for the preliminary processing of the measured point clouds. Furthermore, it contributes two distinct and simplified criteria for detecting defects in pipelines, specifically tailored for computer processing. In conclusion, this paper assesses the effectiveness of the proposed mathematical and physical method through experimental tests conducted under varying light conditions.

This paper presents a novel approach to modeling and analyzing chaotic mechanical vibrations in laser scanning systems. The model explicitly incorporates nonlinear friction using the LuGre friction model. Experimental validation demonstrates chaotic behavior manifested in irregular velocity fluctuations. Dominant frequencies and maximum vibration amplitudes were identified under various operational conditions. A maximum disturbance amplitude of 1.65~rad/s\textsuperscript{2} was observed under the most demanding conditions (25~RPS, $\pm$15° inclination). The proposed model was validated experimentally, providing insights into the interplay of chaotic vibrations and nonlinear friction. A robust control strategy was introduced to mitigate these effects. This strategy is supported by Lyapunov stability analysis and computational implementation. The results demonstrate the effectiveness of the control strategy in reducing the negative effects of friction and chaotic vibrations. The findings could benefit precision engineering, nonlinear dynamics research, and machine vision.

This paper proposes an advancement in the application of a Technical Vision System (TVS), which integrates a laser scanning mechanism with a single light sensor to measure 3D spatial coordinates. In this application, the system is used to scan and digitalize objects using a rotating table to explore the potential of the system for 3D scanning at reduced resolutions. The experiments undertaken searched for optimal scanning windows and used statistical data filtering techniques and regression models to find a method to generate a 3D scan that was still recognizable with the least amount of 3D points, balancing the number of points scanned and time, while at the same time reducing effects caused by the particularities of the TVS, such as noise and entropy in the form of natural distortion in the resulting scans. The evaluation of the experimentation results uses 3D point registration methods, joining multiple faces from the original volume scanned by the TVS and aligning it to the ground truth model point clouds, which are based on a commercial 3D camera to verify that the reconstructed 3D model retains substantial detail from the original object. This research finds it is possible to reconstruct sufficiently detailed 3D models obtained from the TVS, which contain coarsely scanned data or scans that initially lack high definition or are too noisy.