###### Abstract
Weld seam feature extraction is a critical step for achieving welding guidance [14]. Noise is smoothed using a combination of amplitude-limited and Gaussian filtering, followed by initial feature point location using a second-order derivative algorithm and precise positioning through linear fitting. [PERSON] et al. [15] proposed an algorithm for stripe-like image center extraction, employing least-squares-method-based slope analysis for feature detection and extracting weld seam groove features. [PERSON] et al. [16] presented a laser stripe feature point extraction method based on the adaptive Hough transform. [PERSON] et al. [17] introduced a variable gap weld seam feature extraction method using column gray difference operators, employing an optimized Random Sample Consensus (RANSAC) algorithm to address interference from strong reflections. [PERSON] et al. [18] utilized methods such as gray value histogram analysis, adaptive thresholding, internal progressing center and boundary extraction (IPCBE), coordinate analysis, and stripe endpoint center detection for feature extraction. Another adaptive weld seam feature extraction method was proposed in [19], which first determines the joint type through classification methods and then applies appropriate feature extraction methods for different joint types. [PERSON] et al. [20] proposed a visual attention feature-based method to extract the weld seam profile (WSP) from the strong arc background using clustering results, which shows its practical value in robotic automated welding with thick steel plates. However, the method heavily relies on empirical parameters. [PERSON] et al. [21] introduced a convolutional neural network algorithm to develop a deep learning model for accurately locating weld seams in V-groove thick plate welding. However, the method requires substantial computational resources and lacks an optimized design for the control system. The aforementioned weld seam extraction algorithms only obtain feature point coordinates and do not involve weld seam trajectory planning. To achieve weld seam tracking, it is necessary to plan and control the welding path to determine the position and pose of the welding torch relative to the weld seam. [PERSON] et al. [22] used 3D point clouds to obtain path information and establishes a model for the position and pose of the weld seam for trajectory planning. [PERSON] et al. [23] proposed an intelligent guided path planning method that uses point cloud data with high adaptability for welding robots. However, point-cloud-based methods are limited by the field of view of the depth camera and cannot obtain a complete point cloud for large welding parts. They are also significantly affected by machining and assembly accuracy. In terms of trajectory planning algorithms, [PERSON] et al. [24] proposed an S-shaped feed rate scheduling method for Non-Uniform Rational B-Spline (NURBS) curve interpolators, ensuring the continuity of acceleration and reducing the impact of jitter. To improve computational accuracy, [PERSON] et al. [25] proposed an interpolation point parameter-solving algorithm based on the second-order Taylor expansion; however, this algorithm has a large computational load and poor real-time performance. [PERSON] et al. [26] proposed an optimization algorithm to improve the accuracy of NURBS curves by optimizing the parameter \(\mathfrak{u}\) and calculating block displacement through an iterative process. However, the repeated calculation of the exact value of \(\mathfrak{u}\) makes online interpolation difficult to execute. To meet the high-precision requirements for thick plate detection and tracking, this paper presents the design and development of a weld seam detection and tracking system based on a constant-focus line laser vision sensor, ensuring consistent accuracy. This study addresses the limitations of traditional line laser vision sensors, particularly their constrained depth of field, which restricts their application in thick plate welding.