Distance Field Research Articles

A Neural Network-Based State-Constrained Control Strategy for Underactuated Aerial Transportation Systems Within Narrow Workspace

The aerial transportation system belongs to a symmetrical system and has recently garnered increasing attention from researchers due to its broad application range and convenient operation. The control difficulty of the aerial transportation system lies in the fact that the load is not directly actuated, posing a significant challenge for state-constrained control. Taking the motion of an unmanned aerial vehicle (UAV) suspension transportation system within complex pipelines as an example, this paper employs the the swept volume signed distance field (SVSDF) method to search for state boundaries, which is an aspect not considered or elaborated in many state-constrained control approaches. Furthermore, adaptive state-constrained control based on the radial basis function (RBF) neural network is utilized for the case of experiencing unknown air resistance. The convergence of the proposed method for underactuated and actuated state variables is theoretically demonstrated based on the Lyapunov technique. Compared with existing methods, the error integral index demonstrates that the proposed method displays better convergence capability in the simulation section when considering state constraints under disturbance and air resistance.

Citation classics on distance and online learning: a bibliometric analysis

Purpose The purpose of this study is to identify seminal research works on distance and online learning that have had significant impact on the domain. Design/methodology/approach The authors used the SCOPUS database for this study as the data source, and a well-defined search strategy retrieved the items for analysis. First, the authors identified the h-index (n = 207) of the discipline to determine the threshold for listing the top works. The authors critically analysed these classic publications using several bibliometric parameters to present the analysis. To understand the primary focus of the classic research works, the authors also carried out a keyword cluster analysis using VOSviewer. Findings While the USA produced maximum classic research, authors from Canada have maximum research visibility in terms of citations (n = 474.06). Canada also received the highest value of RCI (1.30), followed by Taiwan and Australia. The majority of the classics are published in 67 scientific journals. Of these, Computers and Education published the highest number with a quarter of the total citations (n = 19,403). Although e-learning was the nucleus of the research theme, the authors observed that students, learning systems, online learning, blended learning, learning management systems and computer-aided instructions dominated their influence in the research cluster. Originality/value To the best of the authors’ knowledge, this is the first of its kind work in the field of distance and online learning. Findings of this study would be useful to faculty, researchers and students in the discipline to focus on the seminal works and understand their implications better in the context of the growing significance of the discipline.

Real-Time LiDAR–Inertial Simultaneous Localization and Mesh Reconstruction

In this paper, a novel LiDAR–inertial-based Simultaneous Localization and Mesh Reconstruction (LI-SLAMesh) system is proposed, which can achieve fast and robust pose tracking and online mesh reconstruction in an outdoor environment. The LI-SLAMesh system consists of two components, including LiDAR–inertial odometry and a Truncated Signed Distance Field (TSDF) free online reconstruction module. Firstly, to reduce the odometry drift errors we use scan-to-map matching, and inter-frame inertial information is used to generate prior relative pose estimation for later LiDAR-dominated optimization. Then, based on the motivation that the unevenly distributed residual terms tend to degrade the nonlinear optimizer, a novel residual density-driven Gauss–Newton method is proposed to obtain the optimal pose estimation. Secondly, to achieve fast and accurate 3D reconstruction, compared with TSDF-based mapping mechanism, a more compact map representation is proposed, which only maintains the occupied voxels and computes the vertices’ SDF values of each occupied voxels using an iterative Implicit Moving Least Squares (IMLS) algorithm. Then, marching cube is performed on the voxels and a dense mesh map is generated online. Extensive experiments are conducted on public datasets. The experimental results demonstrate that our method can achieve significant localization and online reconstruction performance improvements. The source code will be made public for the benefit of the robotic community.

Synchronized tracing of primitive-based implicit volumes

Implicit volumes are known for their ability to represent smooth shapes of arbitrary topology thanks to hierarchical combinations of primitives using a structure called a blobtree. We present a new tile-based rendering pipeline well suited for modeling scenarios, i.e., no preprocessing is required when primitive parameters are updated. When using approximate signed distance fields (fields with Lipschitz bound close to 1), we rely on compact, smooth CSG operators - extended from standard bounded operators - to compute a tight augmented bounding volume for all primitives of the blobtree. The pipeline relies on a low-resolution A-buffer storing the primitives of interest of a given screen tile. The A-buffer is then used during ray processing to synchronize threads within a subfrustum. This allows coherent field evaluation within workgroups. We use a sparse bottom-up tree traversal to prune the blobtree on-the-fly which allows us to decorrelate field evaluation complexity from the full blobtree size. The ray processing itself is done using the sphere tracing algorithm. The pipeline scales well to volumes consisting of thousands of primitives.

Research on wireless channel model based on improved ray tracing algorithm that considers multiple diffuse scattering and employs pyramid-shaped ray tubes as the carrier

This paper extensively utilizes fine three dimensional environmental data obtained from laser point clouds. Based on theories such as geometrical optics and effective roughness theory, a deterministic wireless channel model is established, which integrates higher-order diffuse scattering. This model is referred to as the ray tracing fusion with higher-order diffuse scattering model. To expedite the collision calculation between rays and the scene, this paper introduces a combined approach of voxelization and signed distance field, resulting in a remarkable 16-fold improvement in computational speed. Moreover, aiming to balance accuracy and efficiency, the paper systematically analyzes the optimization computation parameters of the model. Finally, the proposed model is validated using measurement data in the frequency range of 1 GHz to 6 GHz in mountainous terrain. The results indicate that the predicted outcomes of the proposed model have an accuracy within 6 dB compared to the measurement results, and are superior to ITU-R P.1546, which is an international standard recommended by the International Telecommunication Union for modeling electromagnetic wave propagation in undulating terrain. This provides necessary technical support for network planning and optimization.

GS‐Octree: Octree‐based 3D Gaussian Splatting for Robust Object‐level 3D Reconstruction Under Strong Lighting

AbstractThe 3D Gaussian Splatting technique has significantly advanced the construction of radiance fields from multi‐view images, enabling real‐time rendering. While point‐based rasterization effectively reduces computational demands for rendering, it often struggles to accurately reconstruct the geometry of the target object, especially under strong lighting conditions. Strong lighting can cause significant color variations on the object's surface when viewed from different directions, complicating the reconstruction process. To address this challenge, we introduce an approach that combines octree‐based implicit surface representations with Gaussian Splatting. Initially, it reconstructs a signed distance field (SDF) and a radiance field through volume rendering, encoding them in a low‐resolution octree. This initial SDF represents the coarse geometry of the target object. Subsequently, it introduces 3D Gaussians as additional degrees of freedom, which are guided by the initial SDF. In the third stage, the optimized Gaussians enhance the accuracy of the SDF, enabling the recovery of finer geometric details compared to the initial SDF. Finally, the refined SDF is used to further optimize the 3D Gaussians via splatting, eliminating those that contribute little to the visual appearance. Experimental results show that our method, which leverages the distribution of 3D Gaussians with SDFs, reconstructs more accurate geometry, particularly in images with specular highlights caused by strong lighting. The source code can be downloaded from https://github.com/LaoChui999/GS-Octree.

Neural Vector Fields for Implicit Surface Representation and Inference

AbstractNeural implicit fields have recently shown increasing success in representing, learning and analysis of 3D shapes. Signed distance fields and occupancy fields are still the preferred choice of implicit representations with well-studied properties, despite their restriction to closed surfaces. With neural networks, unsigned distance fields as well as several other variations and training principles have been proposed with the goal to represent all classes of shapes. In this paper, we develop a novel and yet a fundamental representation considering unit vectors in 3D space and call it Vector Field (VF). At each point in $$\mathbb {R}^3$$ R 3 , VF is directed to the closest point on the surface. We theoretically demonstrate that VF can be easily transformed to surface density by computing the flux density. Unlike other standard representations, VF directly encodes an important physical property of the surface, its normal. We further show the advantages of VF representation, in learning open, closed, or multi-layered surfaces. We show that, thanks to the continuity property of the neural optimization with VF, a separate distance field becomes unnecessary for extracting surfaces from the implicit field via Marching Cubes. We compare our method on several datasets including ShapeNet where the proposed new neural implicit field shows superior accuracy in representing any type of shape, outperforming other standard methods. Codes are available at https://github.com/edomel/ImplicitVF.

Active fault-tolerant control with prescribed performance and reachability judgement for the altitude ground test facility

This research proposes an active fault-tolerant control (AFTC) method based on reachability judgement with asymmetric appointed-time prescribed performance to ensure the safety of altitude ground test facility (AGTF) under potential valve actuator and sensor faults. Firstly, a long short-term memory (LSTM) neural network is employed for fault diagnosis. Given the unique characteristics of AGTF, the equivalent multiplicative fault is introduced to ensure the successful fitting of the fault diagnosis network. Then, a reachability judgement network based on the distance field on grids (DFOG) method is designed to assess the impact of faults on subsequent tasks of the system, thereby ensuring the safety of the AGTF. Subsequently, this study proposes an error transformation function of prescribed performance control (PPC) that ensures error convergence even in cases of asymmetric performance function. This enables the performance boundaries to be further reduced to improve the control performance of the system. Finally, experiments validate the effectiveness of the methods proposed in this manuscript.

Advances in vision-based deep learning methods for interacting hands reconstruction: A survey

Vision-based hand reconstructions have become noteworthy tools in enhancing interactive experiences in various applications such as virtual reality, augmented reality, and autonomous driving, which enable sophisticated interactions by reconstructing complex motions of human hands. Despite significant progress driven by deep-learning methodologies, the quest for high-fidelity interacting hands reconstruction faces challenges such as limited dataset diversity, lack of detailed hand representation, occlusions, and differentiation between similar hand structures. This survey thoroughly reviews deep learning-based methods, diverse datasets, loss functions, and evaluation metrics addressing the complexities of interacting hands reconstruction. Mainstream algorithms of the past five years are systematically classified into two main categories: algorithms that employ explicit representations, such as parametric meshes and 3D Gaussian splatting, and those that utilize implicit representations, including signed distance fields and neural radiance fields. Novel deep-learning models like graph convolutional networks and transformers are applied to solve the aforementioned challenges in hand reconstruction effectively. Beyond summarizing these interaction-aware algorithms, this survey also briefly discusses hand tracking in virtual reality and augmented reality. To the best of our knowledge, this is the first survey specifically focusing on the reconstruction of both hands and their interactions with objects. The survey contains the various facets of hand modeling, deep learning approaches, and datasets, broadening the horizon of hand reconstruction research and future innovation in natural user interactions.

Global BIM-point cloud registration and association for construction progress monitoring

Traditional manual and semi-automatic approaches rely heavily on surveying control points and manually picking equivalent point pairs, which is time-consuming and labor-intensive. This paper proposes an automatic algorithm for automatic global BIM-point registration and association to support construction progress monitoring. A representation using distance fields is proposed to efficiently integrate BIM in registration tasks. By leveraging a coarse-to-fine strategy, a primitive-level coarse algorithm is developed to achieve rough alignment between BIM and point cloud. This approach is then complemented by a point-level fine registration approach, which enables simultaneous pose refinement and BIM-point association. Extensive experiments are conducted on the data from simulation and real-world construction sites. The results demonstrate the promising registration and association performance of the proposed algorithm.

Assessing the lightning performance of sustainable GTL type dielectric liquids versus mineral oils and synthetic ester in a non-uniform electric field

This paper presents for the first time the results of complex comparative tests on the lightning performance of a wide range of sustainable electrical insulating liquids made in GTL (Gas-to-Liquids) technology. Three different GTLs are compared with conventional mineral oils (inhibited and uninhibited) as well as biodegradable synthetic ester. Tests are aimed to determine Lightning Impulse Breakdown Voltage (LIBV) and Acceleration Voltage (Va) at standard lightning impulse voltage of both polarities. Those are performed at 25 mm gap distance of point-sphere electrode setup representing non-uniform electric field distribution. In addition, light emission is studied as well. Results show that for the conditions of experiment LIBV values tend to be similar for all tested dielectric liquids with a slight advantage of mineral oils at negative polarity and GTL-I oil at positive polarity. In turn, in terms of Va, it was found that at negative polarity the GTL oils are, in general, fully comparable with a high grade inhibited mineral oil. The Va of GTLs is in the range 260–275 kV versus 255–260 kV that characterizes mineral oils. When considering positive polarity the GTL-I is slightly inferior to other hydrocarbons with Va equal to 195 kV versus 230–255 kV characterizing other GTLs and mineral oils. For both polarities tested synthetic ester has much lower acceleration voltage in relation to hydrocarbon liquids including GTLs, which constitutes a huge disadvantage of the ester. Although the studies are limited to only one gap distance and non-uniform electric field distribution, it can be stated that the GTLs are worth to be examined as alternative to mineral oil when considering LIBV and Va as the analysed quantities. In addition, a special added value of the performed measurements should be mentioned that the measurements confirmed that Va parameter may be a good dielectric indicator of a dielectric liquid performance, especially when examining new product introduced to the market.

Integration of deep learning and computational fluid dynamics for rapid aerodynamic force prediction of compressor blades

The distribution of flow fields around compressor blades is crucial for the performance and reliability of aircraft engines. To effectively obtain aerodynamic loads, this study combines deep learning with computational fluid dynamics (CFD) to develop an efficient aerodynamic prediction model. Initially, CFD is used to acquire detailed flow field data for the blade surface and its surrounding environment. Subsequently, a distance field parameterization method is applied to process the blade geometry, and deep learning models are used to capture the complex relationship between blade geometry and aerodynamic parameters with high precision. The results indicate that the proposed model can predict aerodynamic loads within seconds with a mean squared error of less than 2%. Compared to traditional parameterization methods and other deep learning approaches, this model exhibits higher accuracy. The findings highlight the effectiveness of integrating deep learning with CFD to enhance aerodynamic predictions and provide a promising approach for future aerodynamic modeling research.

Fuzzy adaptive finite-time formation control of unmanned ground vehicles with performance and feasibility constraints

This paper investigates the problem of fuzzy adaptive finite-time formation tracking control for unmanned ground vehicles (UGVs) systems with two different constraints. By utilizing the distance tracking error between the actual trajectory and the desired trajectory of each UGV, as well as the constraints of bearing angle, to achieve the performance and feasibility constraints, respectively. Furthermore, the fuzzy logic systems (FLSs) are used to approximate unknown nonlinear functions. Due to the limitations of the field of view and communication distance of UGVs, the controller is designed by using finite time stability theory and universal barrier function. Under the proposed control scheme, the stability of the closed-loop system can be ensured, feasibility and performance constraints are also achieved. Finally, the effectiveness of the proposed control design is verified through simulation results.

Improved GAN-based deep learning approach for strain field prediction and failure analysis of precast bridge slab joints

Nonlinear finite element analysis (FEA) is crucial for understanding complex stress-strain behaviors and assessing potential structural failure risks. Building on the recent success of deep learning (DL) methods in physical field predictions, there has been growing interest in forecasting internal force fields of structures beyond the existing data scope. In this study, a data-driven strain field analysis model based on an improved Generative Adversarial Network (GAN) is proposed. To enrich the input data, this novel approach incorporates the Signed Distance Field (SDF) derived from the characteristics of the original Finite Element (FE) models. The generator, based on U-Net, is enhanced with channel attention mechanisms and loss penalty terms, forming the core of the improved GAN. The effectiveness of the proposed method is demonstrated using two parallel datasets comprising 3-Headed bar tension models across two planes. Subsequently, the generated strain field is subjected to feature extraction using the 2D-Principal Component Analysis (2D-PCA) method, followed by structural failure determination through the K-Nearest Neighbor (KNN) algorithm. The results indicate that the proposed improved approach achieves a significant reduction in Root Mean Square Error (RMSE) of 20.71 % and 13.21 % across the two datasets, with the structural failure determination method reaching an F1-score of 0.971. Thus, the intelligent strain field analysis method introduced here effectively predicts strain distributions and promptly identifies failure states, offering valuable insights for further numerical analysis and structural design.

A Toolpath Generator Based on Signed Distance Fields and Clustering Algorithms for Optimized Additive Manufacturing

Additive manufacturing (AM) methods have been gaining momentum because they provide vast design and fabrication possibilities, increasing the accessibility of state-of-the-art hardware through recent developments in user-friendly computer-aided drawing/engineering/manufacturing (CAD/CAE/CAM) tools. However, in comparison to the conventional manufacturing methods, AM processes have some disadvantages, including the machining precision and fabrication process times. The first issue has been mostly resolved through the recent advances in manufacturing hardware, sensors, and controller systems. However, the latter has been widely investigated by researchers with different toolpath planning perspectives. As a contribution to these investigations, the present study proposes a toolpath planning method for AM, which aims to provide highly continuous yet distance-optimized solutions. The approach is based on the utilization of the signed distance field (SDF), clustering, and minimization of toolpath distances among cluster centroids. The method was tested on various geometries with simple closed curves to complex geometries with holes, which provides effective toolpaths, e.g., with relative distance reduction percentages up to 16.5% in comparison to conventional rectilinear infill patterns.

Cone-Traced Supersampling With Subpixel Edge Reconstruction.

While signed distance fields (SDFs) in theory offer infinite level of detail, they are typically rendered using the sphere tracing algorithm at finite resolutions, which causes the common rasterized image synthesis problem of aliasing. Most existing optimized antialiasing solutions rely on polygon mesh representations; SDF-based geometry can only be directly antialiased with the computationally expensive supersampling or with post-processing filters that may produce undesirable blurriness and ghosting. In this work, we present cone-traced supersampling (CTSS), an efficient and robust spatial antialiasing solution that naturally complements the sphere tracing algorithm, does not require casting additional rays per pixel or offline pre-filtering, and can be easily implemented in existing real-time SDF renderers. CTSS performs supersampling along the traced ray near surfaces with partial visibility - object contours - identified by evaluating cone intersections within a pixel's view frustum. We further introduce subpixel edge reconstruction (SER), a technique that extends CTSS to locate and resolve complex pixels with geometric edges in relatively flat regions, which are otherwise undetected by cone intersections. Our combined solution relies on a specialized sampling strategy to minimize the number of shading computations and correlates sample visibility to aggregate the samples. With comparable antialiasing quality at significantly lower computational cost, CTSS is a reliable practical alternative to conventional supersampling.

TNDF-Fusion: Implicit Truncated Neural Distance Field for LiDAR Dense Mapping and Localization in Large Urban Environments

TNDF-Fusion: Implicit Truncated Neural Distance Field for LiDAR Dense Mapping and Localization in Large Urban Environments

RGBTSDF: An Efficient and Simple Method for Color Truncated Signed Distance Field (TSDF) Volume Fusion Based on RGB-D Images

RGB-D image mapping is an important tool in applications such as robotics, 3D reconstruction, autonomous navigation, and augmented reality (AR). Efficient and reliable mapping methods can improve the accuracy, real-time performance, and flexibility of sensors in various fields. However, the currently widely used Truncated Signed Distance Field (TSDF) still suffers from the problem of inefficient memory management, making it difficult to directly use it for large-scale 3D reconstruction. In order to address this problem, this paper proposes a highly efficient and accurate TSDF voxel fusion method, RGBTSDF. First, based on the sparse characteristics of the volume, an improved grid octree is used to manage the whole scene, and a hard coding method is proposed for indexing. Second, during the depth map fusion process, the depth map is interpolated to achieve a more accurate voxel fusion effect. Finally, a mesh extraction method with texture constraints is proposed to overcome the effects of noise and holes and improve the smoothness and refinement of the extracted surface. We comprehensively evaluate RGBTSDF and similar methods through experiments on public datasets and the datasets collected by commercial scanning devices. Experimental results show that RGBTSDF requires less memory and can achieve real-time performance experience using only the CPU. It also improves fusion accuracy and achieves finer grid details.

Path planning for Multi-USV target coverage in complex environments

This research introduces a path planning methodology aimed at coordinating multiple unmanned surface vehicles (USVs) to accomplish target coverage task in obstacle-rich environments. Leveraging the locking sweeping method (LSM), our proposed method constructs task target point distance fields that integrates environmental constraints, alongside a task target point distance matrix. During the task allocation phase, we design a cost assessment function to assess the generated allocation solutions. And a greedy allocation strategy is employed to determine the optimal number of USVs required for mission completion, ensuring evenly distribution of target task points among them. Subsequently, we improve the ant colony optimization (ACO) method by redesigning the heuristic function and pheromone update rules, considering environmental constraints and task execution sequence constraints. This refinement facilitates the generation of optimized task execution sequences and safe navigation paths in obstacle environments. The effectiveness of the proposed method is validated through multiple sets of simulation experiments and compared with existing methods. The results demonstrate the practicality and efficacy of the method in addressing the challenges of USVs target coverage task in obstacle environments.

粮食干燥机自吸式热风变温装置设计与试验验证

During the grain drying process, in order to adjust the temperature, it is necessary to match the proportion of hot and cold air. This is the problem that the two components in the mixed gas cannot fully mix with each other in a short period of time, resulting in the problem that it takes a long time for the gas flow temperature to reach a stable value. Based on the process and technical requirements of the dryer, a self-priming hot air temperature changing device suitable for the dryer was designed. The two gas components used to improve the variable temperature ratio of the dryer are fully mixed with each other in a short time and a short distance, thereby reducing the loss caused by the food not reaching safe moisture. Based on Bernoulli's principle and the basic theory of airflow mixing in fluid dynamics, a mathematical model of airflow mixing in the constriction section of the main pipeline was established. Fluent was used to numerically simulate the distribution of uniform mixing distance and temperature field in the necking section. The results show that the mixing uniformity in the necking section reaches 75%-80%, which effectively improves the mixing efficiency. A self-priming hot air temperature change device test bench developed independently was used, and the quadratic orthogonal rotation combined test method was used for parameter optimization. Design-Expert.V8.0.6.1 was used for analysis and testing, and the regression equation and response surface were obtained to analyze the effects. Interaction between factors to determine the best combination of optimization parameters: when the number of air inlet pipes is 3.16, the incision axial angle is 27.6°, the temperature difference is 43.5°C, and the pipe diameter is 23.8mm, the post-mixing temperature is 50.93°C, and the mixing distance is uniform is 39.33mm. The test results are consistent with the optimization results. The self-priming hot air temperature changing device of the dryer has certain practical application value.