B-spline Curve Fitting Research Articles

Adaptive B-spline curve fitting with minimal control points using an improved sparrow search algorithm for geometric modeling of aero-engine blades

Adaptive B-spline curve fitting with minimal control points using an improved sparrow search algorithm for geometric modeling of aero-engine blades

Autonomous ultrasound scanning robotic system based on human posture recognition and image servo control: an application for cardiac imaging.

In traditional cardiac ultrasound diagnostics, the process of planning scanning paths and adjusting the ultrasound window relies solely on the experience and intuition of the physician, a method that not only affects the efficiency and quality of cardiac imaging but also increases the workload for physicians. To overcome these challenges, this study introduces a robotic system designed for autonomous cardiac ultrasound scanning, with the goal of advancing both the degree of automation and the quality of imaging in cardiac ultrasound examinations. The system achieves autonomous functionality through two key stages: initially, in the autonomous path planning stage, it utilizes a camera posture adjustment method based on the human body's central region and its planar normal vectors to achieve automatic adjustment of the camera's positioning angle; precise segmentation of the human body point cloud is accomplished through efficient point cloud processing techniques, and precise localization of the region of interest (ROI) based on keypoints of the human body. Furthermore, by applying isometric path slicing and B-spline curve fitting techniques, it independently plans the scanning path and the initial position of the probe. Subsequently, in the autonomous scanning stage, an innovative servo control strategy based on cardiac image edge correction is introduced to optimize the quality of the cardiac ultrasound window, integrating position compensation through admittance control to enhance the stability of autonomous cardiac ultrasound imaging, thereby obtaining a detailed view of the heart's structure and function. A series of experimental validations on human and cardiac models have assessed the system's effectiveness and precision in the correction of camera pose, planning of scanning paths, and control of cardiac ultrasound imaging quality, demonstrating its significant potential for clinical ultrasound scanning applications.

Three-Dimensional Leaf Edge Reconstruction Combining Two- and Three-Dimensional Approaches

Leaves, crucial for plant physiology, exhibit various morphological traits that meet diverse functional needs. Traditional leaf morphology quantification, largely 2-dimensional (2D), has not fully captured the 3-dimensional (3D) aspects of leaf function. Despite improvements in 3D data acquisition, accurately depicting leaf morphologies, particularly at the edges, is difficult. This study proposes a method for 3D leaf edge reconstruction, combining 2D image segmentation with curve-based 3D reconstruction. Utilizing deep-learning-based instance segmentation for 2D edge detection, structure from motion for estimation of camera positions and orientations, leaf correspondence identification for matching leaves among images, and curve-based 3D reconstruction for estimating 3D curve fragments, the method assembles 3D curve fragments into a leaf edge model through B-spline curve fitting. The method's performances were evaluated on both virtual and actual leaves, and the results indicated that small leaves and high camera noise pose greater challenges to reconstruction. We developed guidelines for setting a reliability threshold for curve fragments, considering factors occlusion, leaf size, the number of images, and camera error; the number of images had a lesser impact on this threshold compared to others. The method was effective for lobed leaves and leaves with fewer than 4 holes. However, challenges still existed when dealing with morphologies exhibiting highly local variations, such as serrations. This nondestructive approach to 3D leaf edge reconstruction marks an advancement in the quantitative analysis of plant morphology. It is a promising way to capture whole-plant architecture by combining 2D and 3D phenotyping approaches adapted to the target anatomical structures.

Demonstration and Analysis of DRN Deep Learning Architecture for Step-by-Step Disassembly of Dahongquan

Abstract The Dahongquan martial arts style is characterized by its intricate and varied maneuvers, posing challenges for traditional video demonstrations to fully satisfy learners’ needs. This study introduces a cutting-edge approach utilizing a visual neural network to deconstruct and map these martial arts movements in a 3D virtual environment. Initially, preprocessing involves extracting data from exercise videos, employing a combination of video annotation and skeleton feature extraction. Subsequently, a deep residual network (DRN) with multi-scale feature extraction and dense residual attention mechanisms is employed, facilitating a systematic breakdown of the martial arts movements. The third phase involves constructing a 3D action mapping and demonstration process, utilizing cubic B-spline curve fitting and cylinder deformation techniques to enhance the representation of movement based on the skeletal model. Remarkably, the DRN network, without the need for extensive training datasets, achieves flawless recognition of various gongfu combinations and records a recognition rate of 92.92% for the most challenging boxing movements. The efficacy of this model is further corroborated by its enhancement of the demonstration process, achieving an overall learner satisfaction rating of 5.26. The disassembly and demonstration strategy proposed in this study has proven effective during experimental sessions, demonstrating significant potential for advancing martial arts training methodologies.

Structural modeling and analysis of three-dimensional cross-linked braided preforms

Abstract Rotary braiding is widely used in the preparation of high-performance fiber composites. However, neither conventional rotary two-dimensional braiding nor rotary three-dimensional braiding can prepare three-dimensional preforms with low porosity and dense surface yarns. This article designs a three-dimensional cross-linked braiding equipment, describes the mechanism of carrier motion of three-dimensional cross-linked braiding, and obtains a numerical model of three-dimensional cross-linked braided preforms by establishing the spatial coordinate system of the braided chassis and using cubic B-spline curve fitting. The reasons for the large difference between the numerical model of preform and the real fabric are analyzed, and an optimization algorithm is proposed to make the fabric compact by gathering the yarn to the center of the fabric. The experimental samples are braided on a three-dimensional cross-linked braiding machine, and the reliability of the optimization algorithm is verified by comparing the yarn coordinates of the experimental samples with those of the optimized numerical model. The accurate establishment of the geometric model of the preform provides a solid foundation for carrying out the prediction of the mechanical properties of composite materials.

Reconstruction of Smooth Skin Surface Based on Arbitrary Distributed Sparse Point Clouds

Adaptively reconstruction of the skin surface covering the skeletons of aircrafts, ships, high-speed trains, buildings with steel frames, etc. based on on-site measured point clouds is an important issue in both industry and construction. However, the skeleton has the characteristics of long and narrow shape and variable structure, resulting in a narrow and sparse distribution of measured point cloud on its surface with variable shape in different areas, which brings great challenges to the reconstruction of skin surface. In this paper, a method for accurately reconstructing the skin surface covering the skeleton structures based on the arbitrary distributed sparse on-site measured points of the skeleton surface is proposed. At first, a robust B-spline curve fitting method based on the least square principle is proposed to construct the boundary curves of the point cloud. Then, a Coons-B-spline surface fitting method based on the generated boundary curves is proposed to generate an initial skin surface. Next, the initial skin surface is considered as a curved thin plate with stiffness, and a method of surface deformation considering the target points of deformation and the tensile and shear stiffness of the surface is proposed to obtain the surface with high accuracy and good smoothness. To show the feasibility of the proposed method, simulations and experiments are carried out. It is proved that the proposed method can achieve better accuracy while maintaining smoothness.

An Efficient and Robust Complex Weld Seam Feature Point Extraction Method for Seam Tracking and Posture Adjustment

To realize high-quality robotic welding, an efficient and robust complex weld seam feature point extraction method based on a deep neural network (Shuffle-YOLO) is proposed for seam tracking and posture adjustment. The Shuffle-YOLO model can accurately extract the feature points of butt joints, lap joints and irregular joints, and the model can also work well despite strong arc radiation and spatters. Based on the nearest neighbor algorithm and cubic B-spline curve fitting algorithm, the position and posture models of the complex spatially curved weld seams are established. The robot welding postures adjustment and high-precision seam tracking of complex spatially curved weld seams are realized. Experiments show that the method proposed in this paper can extract weld seam feature points quickly and robustly, which enables welding robots to accurately track the weld seams and adjust the welding torch postures simultaneously.

Versatile robotic welding system integrating laser positioning, trajectory fitting and real-time tracking

The three welding modes of laser positioning, trajectory fitting and real-time tracking are key links for flexible robotic welding, because they directly affect the adaptability of welding robots for complex structural parts. Nevertheless, there are few studies on the integration of laser positioning, trajectory fitting and real-time tracking welding. Aiming at the drawback of that the robot cannot efficiently match the corresponding welding process between different working conditions such as short seam, spatial curve seam and long seam welding, a control scheme that can integrate the above three welding modes is proposed in this paper. First, the welding requirements of laser positioning, trajectory fitting and real-time tracking are transformed into a unified 3D coordinate recognition task for integrated welding, so the identification of welding points under three different processes can be obtained through an identical inference. The next, given the fast inference speed of YOLOv5, a joint ROI extraction algorithm with YOLOv5 as the core is applied. To compensate for YOLOv5′s inability to directly locate seam centers, iterative centerline unbiased detector (ICUD) and adaptive feature extraction algorithm (AFEA) are proposed without the need for complex image pre-processing and with very strong immunity to strong exposure and strong arc spatter. Finally, teaching trajectory correction model, B-spline curve fitting model, and 3D coordinate real-time tracking model are presented to enhance the adaptability of the welding robot and to flexibly match the appropriate welding mode in various working conditions. Experimental results indicate that the welding trajectory is basically consistent with the seam centerline when laser positioning and trajectory fitting welding. In real-time tracking welding, the real-time deviation is mainly controlled within the range of ± 0.2 mm, and the average error is 0.1160 mm.

Three-dimensional reconstruction of light field based on cubic B-spline curve fitting

Fringe projection profilometry has been widely used in three-dimensional (3D) reconstruction. In this paper, we replace the camera with a self-made light field camera to achieve structured light field 3D reconstruction. Our self-made light field camera boasts higher resolution and lower cost with the ability to adjust spatial and angular resolution as needed. A novel 3D reconstruction method based on cubic B-spline curve fitting is proposed to directly fit the relationship between phase and 3D coordinates, and the look up table method is utilized to accelerate the entire calculation process. The experimental results demonstrate that the proposed method can accomplish 3D reconstruction of the structured light field under various viewing angles. Specifically, it has respectively reduced the computation time for phase and modulation intensity by 64.03% and 41.43%, and the reconstruction time is decreased by 32.99% without compromising the reconstruction accuracy.

Constrained least square progressive and iterative approximation (CLSPIA) for B-spline curve and surface fitting

Constrained least square progressive and iterative approximation (CLSPIA) for B-spline curve and surface fitting

Numerical analysis of composite preform structure based on flat cross-linked braiding

Composite materials are widely used in aerospace, automotive industry and medical equipment, where most of the structural parts are made of fiber laminates. Rotary braiding is one of the most important methods to prepare composite preforms. The ordinary rotary braided preforms have low porosity and dense yarns, but are single-layer structures after molding, which do not meet the needs of most applications. To solve the problem that traditional laminates are prone to delamination, this paper designs a flat cross-linked braiding process for the preparation of multilayer-interlocking composite panels. The carrier trajectory is analyzed, and the spatial coordinate system of the braided chassis is established by combining the effect of the end traction system on the morphology of the preform yarns. The numerical model of the preform is initially obtained by using cubic B-spline curve fitting, and an algorithm for gathering yarns towards the fabric center is proposed to optimize the numerical model. Experimental samples of flat cross-linked preforms are prepared according to the braiding process, and the reliability of the optimization algorithm is verified by comparing the yarn coordinates of the experimental samples with those of the optimized numerical model.

Large-scale stealth trajectory optimization based on hybrid A*-Gauss pseudospectral method

For airborne electronic countermeasures, a practical and feasible stealth trajectory with low observability significantly impacts mission success. However, for long-range and large-scale trajectory optimization problems, the significant increase in the state space size will affect the feasibility and optimality of the problem solution. This paper proposes a hybrid trajectory optimization method to address the above issues. First, the A* (A-star) algorithm and cubic B-spline curve fitting method are used to generate the corresponding waypoints in the predetermined grid map under the radar detection threat to satisfy the trajectory stealth effect at the macro level. Then, the optimal control model (OCP) with the shortest flight time is solved between the waypoints to obtain segmented trajectories. Finally, the above state-control variable sequences are further used to solve the optimal control problem of the micro-level stealth trajectory coupling the radar cross section (RCS) by Gauss pseudospectral method (GPM). The numerical simulation results validate the proposed hybrid optimization method.

Adaptive Multi-ROI Agricultural Robot Navigation Line Extraction Based on Image Semantic Segmentation.

Automated robots are an important part of realizing sustainable food production in smart agriculture. Agricultural robots require a powerful and precise navigation system to be able to perform tasks in the field. Aiming at the problems of complex image background, as well as weed and light interference factors of the visual navigation system in field and greenhouse environments, a Faster-U-net model that retains the advantages of the U-net model feature jump connection is proposed. Based on the U-net model, pruning and optimization were carried out to predict crop ridges. Firstly, a corn dataset was trained to obtain the weight of the corn dataset. Then, the training weight of the obtained corn dataset was used as the pretraining weight for the cucumber, wheat, and tomato datasets, respectively. The three datasets were trained separately. Finally, the navigation line between ridges and the yaw angle of the robot were generated by B-spline curve fitting. The experimental results showed that the parameters of the improved path segmentation model were reduced by 65.86%, and the mPA was 97.39%. The recognition accuracy MIoU of the Faster-U-net model for maize, tomatoes, cucumbers, and wheat was 93.86%, 94.01%, 93.14%, and 89.10%, respectively. The processing speed of the single-core CPU was 22.32 fps/s. The proposed method had strong robustness in predicting rows of different crops. The average angle difference of the navigation line under a ridge environment such as that for corn, tomatoes, cucumbers, or wheat was 0.624°, 0.556°, 0.526°, and 0.999°, respectively. This research can provide technical support and reference for the research and development of intelligent agricultural robot navigation equipment in the field.

A path smoothing scheme for micro-line tools using cubic B-spline fitting with dominant points

To obtain higher order continuous tool path, a cubic B-spline curve fitting method is presented in this paper to process the linear tool path. First, a local cubic polynomial fitting with bidirectional scanning scheme is proposed to determine some initial dominant points. Based on these dominant points, the initial knot vector is generated by average method, and the initial B-spline is constructed. To constrain the fitting error, a Newton’s method based fitting error estimation scheme and a fitting error based dominant point insertion scheme are proposed to modify the B-spline. Simulation results prove that our method can compress data and smooth tool path. Compared with another existing curve fitting method, the number of control point has decreased by 9.43%, and the machining time has decreased by 94.45% compared with traditional point-to-point (P2P) motion.

B-Spline Curve Fitting of Hungry Predation Optimization on Ship Line Design

The ship line often describes by the offset table of discrete data points, which leads to the problems that three view coordinates may not correspond, the fitting error is large and the fitted curve cannot be easily modified. This will seriously affect the subsequent ship performance evaluation and op-timization. To solve this problem, this paper develops a B-spline curve fitting of hunger predation optimization on ship line design (HPA), which contains knot guidance technology, hungry preda-tion optimization technology and adaptive adjustment of algorithm input parameters. HPA transforms the discrete ship line into a continuous B-spline curve description, which improves the accuracy and modifiability of the ship line design. Through the real-time feedback of the results of each round of iteration, the knot vector is adaptively adjusted towards a better fitness, and then the optimal control point set that satisfies the error threshold can be obtained. The effectiveness and superiority of HPA are verified by comparing with related research and engineering software.

Collision-free trajectory planning for multi-robot simultaneous motion in preforms weaving

AbstractIn this paper, an automatic obstacle avoidance trajectory planning strategy is proposed for the simultaneous motion of multi-robots, which perform anthropomorphic skill operations in a large curved preformed three-dimensional (3D) weaving environment with multiple obstacles and limited space, to eliminate tedious manual calibration work of robot path in engineering. Firstly, an Adaptive Goal-guided Rapidly-exploring Random Trees (AGG-RRT) algorithm is proposed, combined with the robot obstacle avoidance strategy, to search the discrete position of the collision-free path of the end-effector gradually from the starting point to the ending point. Then the optimization of the path is completed by bidirectional pruning of redundant nodes and cubic non-uniform rational B-spline (NURBS) curve fitting. And finally, the robot trajectory is interpolated based on S-shaped acceleration/deceleration planning to ensure smooth robot joint motion. The simulation results demonstrate the superiority of the AGG-RRT algorithm over the basic RRT algorithm and related improved algorithms in terms of search time and success rate. The simulated experiments also achieve the smooth trajectory planning of multiple robotic arms with the synchronous obstacle avoidance motion, which shows that the AGG-RRT algorithm is successfully applied and the collision-free trajectory planning strategy is effective.

Investigation on the Bending Behavior of Tea Stalks Based on Non-Prismatic Beam with Virtual Internodes

The study aims to fully explicate the bending behavior of tea stalks under the condition of large deflection, which is crucial to improve the working performance of mechanized harvesting equipment. The mechanical model of the stalk was assumed to be a non-prismatic beam with virtual internodes that could differ from actual internodes. With the model, the stalk can be freely divided into multiple virtual internodes, whose flexural rigidities can be determined by solving an optimization problem, and deflection curves can be predicted after determining the positions of virtual nodes under given loads. Moreover, a novel method was proposed to obtain the deflection curve of the stalk based on the techniques of binocular vision and non-uniform rational B-spline (NURBS) curve fitting. The results show that R-squared values of fitted 2nd-degree NURBS curves of bending shape of tea stalks range from 0.9576 to 0.9964, with an average of 0.9797. The results indicate that flexural rigidity decreases from the bottom to the top of the tea stalk, and the deflection curve could be predicted more precisely with the model of piecewise flexural rigidities than that of average flexural rigidity. The study could be applied to the optimization design of the cutter and adaptive adjustment techniques of operational parameters for tea picking machines.

Reconstruction of the initial curve from a two-dimensional shape for the B-spline curve fitting

Reconstruction of the initial curve from a two-dimensional shape for the B-spline curve fitting

Comparison of Numerical Calculation Methods for Stem Diameter Retrieval Using Terrestrial Laser Data

Terrestrial laser scanning (TLS) can be used as a millimeter-level measurement tool for forest inventories. However, the stem diameter retrieval accuracy in sample plot scanning is not yet convincing. The errors in each step of stem diameter retrieval algorithms must be evaluated. In this study, six numerical calculation methods for the numerical calculation step, i.e., cylinder fitting (CYF), circle fitting (CF), convex hull line fitting (CLF), the proposed caliper simulation method (CSM), closure B-spline curve fitting (SP) and closure Bézier curve fitting with global convexity (SPC), were applied to stem diameter retrieval, and the similarities and differences were evaluated. The ovality, completeness and roughness were used to evaluate the stem slice point cloud quality. A total of 165 stem slice point clouds at breast height collected from three Larix kaempferi plots were used. Compared with the field-measured stem diameters at breast height (DBHs), the root mean square errors (RMSEs) of the CYF, CF, CLF, CSM, SP and SPC methods were 0.30 cm, 0.30 cm, 0.51 cm, 0.51 cm, 0.56 cm and 0.54 cm, respectively. Compared with the SPC method results, the RMSE of the CSM results was 0.05 cm. The results illustrated that the CYF and CF methods performed the same, as did the CLF and CSM methods. Most DBHs retrieved by the CYF and CF methods were smaller than the field-measured DBHs, and most DBHs retrieved by the CLF, CSM, SP and SPC methods were larger than the field-measured DBHs. This study demonstrated that the CYF and CF methods perform the best and are the most robust, and the measurements by a diameter tape and a caliper are similar enough for forestry inventories. Evaluating and preprocessing stem slice point clouds is a potential way to improve stem diameter retrieval accuracy.

Rapid Path Extraction and Three‐Dimensional Roaming of the Virtual Endonasal Endoscope

Compared with traditional endonasal endoscope, Virtual endonasal endoscope (VEE) is a computerized alternative solution with the advantages of being non-invasive, affordable, no perforation, low risk of infection, and high degree of sensitivity. A Central path extraction algorithm based on Pre-planning of the roaming area (CPE-PRA) is proposed to extract the central path of the nasal cavity model efficiently. And a B-spline curve fitting algorithm based on Relative center deviation (RCD) is introduced to fit a smooth curve to the extracted path, so that the virtual camera could have both a steady turning speed and a broad vision. A series of comparison tests were conducted to evaluate the effectiveness and efficiency of the proposed algorithms.