Search Results (165)

To address the operational demands of irregular farmland with fixed obstacles, this study proposes a full-coverage path planning framework that integrates UAV-based 3D perception and angle-adaptive optimization. First, digital orthophoto maps (DOMs) and digital elevation models (DEMs) were reconstructed from low-altitude aerial imagery. The feasible working region was constructed by shrinking field boundaries inward and dilating obstacle boundaries outward. This ensured sufficient safety margins for machinery operation. Next, segmentation angles were scanned from 0° to 180° to minimize the number and irregularity of sub-regions; then a two-level simulation search was performed over 0° to 360° to optimize the working direction for each sub-region. For each sub-region, the optimal working direction was selected based on four criteria: the number of turns, travel distance, coverage redundancy, and planning time. Between sub-regions, a closed-loop interconnection path was generated using eight-directional A* search combined with polyline simplification, arc fitting, Chaikin subdivision, and B-spline smoothing. Simulation results showed that a 78° segmentation yielded four regular sub-regions, achieving 99.97% coverage while reducing the number of turns, travel distance, and planning time by up to 70.42%, 23.17%, and 85.6%. This framework accounts for field heterogeneity and turning radius constraints, effectively mitigating path redundancy in conventional fixed-angle methods. This framework enables general deployment in agricultural field operations and facilitates extensions toward collaborative and energy-optimized task planning. Full article

Accurate environmental sensing is pivotal for advancing automation in construction, particularly in autonomous excavation. Precise 3D representations of complex and dynamic site geometries is essential for obstacle detection, progress monitoring, and safe operation. However, existing sensing techniques often struggle with capturing irregular surfaces and incomplete data in real-time, leading to significant challenges in practical deployment. To address these gaps, we present a novel framework integrating curve approximation, surface reconstruction, and marching cubes algorithm to transform raw sensor data into a detailed and computationally efficient soil surface representation. Our approach improves site modeling accuracy, paving the way for reliable and efficient construction automation. This paper enhances sensory data quality using surface reconstruction techniques, followed by the marching cubes algorithm to generate an accurate and flexible 3D soil model. This model facilitates rapid estimation of soil volume, weight, and shape, offering an efficient approach for environmental analysis and decision-making in automated systems. Experimental validation demonstrated the effectiveness of the proposed method, achieving relative errors of 4.92% and 1.42% across two excavation cycles. Additionally, the marching cubes algorithm completed volume estimation in just 0.05 s, confirming the approach’s accuracy and suitability for real-time applications in dynamic construction environments. Full article

This paper proposes a novel navigation approach for Unmanned Tracked Vehicles (UTVs) using prior-based ensemble deep reinforcement learning, which fuses the policy of the ensemble Deep Reinforcement Learning (DRL) and Dynamic Window Approach (DWA) to enhance both exploration efficiency and deployment safety in unstructured off-road environments. First, by integrating kinematic analysis, we introduce a novel state and an action space that account for rugged terrain features and track–ground interactions. Local elevation information and vehicle pose changes over consecutive time steps are used as inputs to the DRL model, enabling the UTVs to implicitly learn policies for safe navigation in complex terrains while minimizing the impact of slipping disturbances. Then, we introduce an ensemble Soft Actor–Critic (SAC) learning framework, which introduces the DWA as a behavioral prior, referred to as the SAC-based Hybrid Policy (SAC-HP). Ensemble SAC uses multiple policy networks to effectively reduce the variance of DRL outputs. We combine the DRL actions with the DWA method by reconstructing the hybrid Gaussian distribution of both. Experimental results indicate that the proposed SAC-HP converges faster than traditional SAC models, which enables efficient large-scale navigation tasks. Additionally, a penalty term in the reward function about energy optimization is proposed to reduce velocity oscillations, ensuring fast convergence and smooth robot movement. Scenarios with obstacles and rugged terrain have been considered to prove the SAC-HP’s efficiency, robustness, and smoothness when compared with the state of the art. Full article

The oxygen evolution reaction (OER) is pivotal in hydrogen production via water electrolysis, yet its sluggish kinetics, stemming from the four-electron transfer process, remain a major obstacle, with overpotential reduction being critical for enhancing efficiency. This work addresses this challenge by developing a novel approach to stabilize and activate non-precious metal catalysts for OER. Specifically, we synthesized a three-dimensional flake NiFe-LDH/ZIF-L composite catalyst on a flexible nickel foam (NF) substrate through a room temperature soaking and hydrothermal method, leveraging the mesoporous structure of ZIF-L to increase the specific surface area and optimizing electron transfer pathways via interfacial regulation. Continuous linear sweep voltammetry (LSV) scanning induced structural self-reconstruction, forming highly active NiOOH species enriched with oxygen vacancies, which significantly boosted catalytic performance. Experimental results demonstrate an overpotential of only 221 mV at 10 mA cm⁻² and a Tafel slope of 56.3 mV dec⁻¹, alongside remarkable stability, attributed to the catalyst’s hierarchical nanostructure that accelerates mass diffusion and charge transfer. The innovation lies in the synergistic effect of the mesoporous ZIF-L structure and interfacial regulation, which collectively enhance the catalyst’s activity and durability, offering a promising strategy for advancing large-scale water electrolysis hydrogen production technology. Full article

Background and Objectives: Breast reconstruction following mastectomy improves quality of life and psychosocial outcomes, yet it is not consistently performed despite multiple federal mandates. Current data shows decreased reconstruction in minority races, those with a low socioeconomic status, and those holding public health insurance. Many barriers remain misunderstood or unstudied. This study examines barriers to post-mastectomy breast reconstruction to promote a supportive clinical climate by addressing multifactorial obstacles to equitable access to care. Materials and Methods: The California Cancer Registry Data Surveillance, Epidemiology, and End Results (SEER) database and California Health and Human Services Agency Cancer Surgeries Database (2013–2021 and 2000–2021, respectively) were used in this retrospective observational study on mastectomy with immediate breast reconstruction (IBR), delayed breast reconstruction (DBR), or mastectomy only (MO) rates. Data were collected on age, sex, race, insurance type, hospital type, socioeconomic status, and residence. Pearson’s chi-square analysis was performed. Results: We found that 168,494 mastectomy and reconstruction surgeries were performed (82.36% MO, 7% IBR, 10.6% DBR). The 40–49 age group received significantly less MO (38.1%) compared to the 70–74 age group (94.8%, (p = <0.001). Significantly more reconstruction was carried out in patients with private, HMO, or PPO insurance (IBR 75.86%, DBR 75.32%, p = <0.001). Almost all breast surgeries were in urban areas as opposed to rural/isolated rural areas (96.02% vs. 1.55%, p = <0.001). There was no significant difference between races. Of all surgeries, 7.46% were completed in a cancer center with significantly higher rates of IBR. LA County, San Luis Obispo/Ventura County, and Northern CA had significantly more MO than other regions (p = <0.001). Conclusions: Reconstruction rates after mastectomy are low, with only 17.64% of patients undergoing reconstruction. Nationally, 70.5% of patients received MO, with 29.6% undergoing reconstruction. Significant factors positively contributing to reconstruction were private insurance, high SES, cancer center care, and urban residency. Identified barriers include public health insurance enrollment, rural or non-urban residence, older age, low SES, and non-white race/ethnicity, indicating potential monetary influences on care. Full article

To study the effects of different microbial agents on the microbial community structure of continuously cropped strawberry soil after soil fumigation, seven treatments were applied: T1 (Trichoderma harzianum + Bacillus subtilis + actinomycetes), T2 (Trichoderma harzianum + Bacillus subtilis), T3 (Trichoderma harzianum + actinomycetes), T4 (CK) (water control), T5 (Bacillus subtilis), T6 (actinomycetes) and T7 (Trichoderma harzianum). A high-throughput sequencing platform (Illumina HiSeq 2500) was used to analyze the soil bacterial and fungal communities and their compositions. Compared with the T4 (CK) treatment, the application of microbial agents increased the richness and diversity of soil bacteria and fungi, and the effects of single microbial agents and compound microbial agents differed. The richness, diversity indices and population sizes of bacteria and fungi in the T6 treatment were the highest. The Chao1, observed species and Shannon indices of bacteria were 22.51%, 23.56% and 5.61% greater, respectively, than those of T4 (CK). The Chao1, observed species, Shannon and Simpson indices of fungi were 41.28%, 41.83%, 128.02% and 88.65% higher, respectively, than those of T4 (CK). At the genus level, the bacterial community compositions of T2 and T6 were the most similar, and the fungal community compositions of T1 and T5 were the most similar. Analysis of the genera in the dominant communities revealed that the application of microbial agents after dazomet fumigation increased the numbers and recovery rates of soil bacteria and fungi, especially the beneficial fungal genera, Lecanicillium, Cladosporium, Saccharomyces and Aspergillus. An investigation of strawberry growth and yield-related indicators revealed that the T6 treatment resulted in the lowest seedling mortality and the highest yield. In summary, adding microbial agents to soil with continuous cropping of strawberry after fumigation with dazomet is a scientifically sound and effective method for reconstructing the balance of the soil microbial flora and overcoming the obstacles associated with continuous cropping. In this study, the T6 (actinomycetes) treatment presented the best performance. Full article

In view of the low efficiency and slow development of fruit and vegetable picking in China, the picking sequence and obstacle avoidance of clustered crabapples were studied with them as the picking target. The multi-objective picking sequence of crabapples was planned, and the adaptive pheromone factor, heuristic function, and volatile factor were used to improve the ant colony (ACO) algorithm, so as to improve the convergence speed, adaptability, and global search ability of the algorithm. In order to avoid the collision between the robotic arm and the branches of the fruit tree, the three-dimensional reconstruction of the fruit tree was carried out, the shape and position information of the obstacle branch was determined, the artificial potential field was fused with the RRT, the search orientation of the RRT algorithm was enhanced, the inflection point was reduced, and the convergence speed was improved. The results showed that the average success rate of picking was 89.58%, and the robotic arm did not collide with the branches according to the planned picking sequence during the picking process, so as to achieve the picking purpose and picking effect. Full article

Autonomous underwater vehicle inspection in 3D environments presents significant challenges in spatial mapping for obstacle avoidance and motion control. Current solutions rely on either 2D forward-looking sonar or expensive 3D sonar systems. To address these limitations, this study proposes a cost-effective 3D reconstruction method using an oscillatory forward-looking sonar with a pan-tilt mechanism that extends perception from a 2D plane to a 75-degree spatial range. Additionally, a polar coordinate-based frontier extraction method for sequential sonar images is introduced that captures more complete contour frontiers. Through bridge pier scanning validation, the system shows a maximum measurement error of 0.203 m. Furthermore, the method is integrated with the Ego-Planner path planning algorithm and nonlinear Model Predictive Control (MPC) algorithm, creating a comprehensive underwater 3D perception, planning, and control system. Gazebo simulations confirm that generated 3D point clouds effectively support the Ego-Planner method. Under localisation errors of 0 m, 0.25 m, and 0.5 m, obstacle avoidance success rates are 100%, 60%, and 30%, respectively, demonstrating the method’s potential for autonomous operations in complex underwater environments. Full article

Purpose—The reconstruction of worn-out urban fabrics poses a significant challenge in sustainable urban development, as such places, due to their decay and infrastructural inefficiencies, diminish residents’ quality of life and generate many environmental, social, and economic issues. Meanwhile, green building techniques have emerged as a novel option because they focus on environmental sustainability and resource efficiency. Nonetheless, effectively executing these strategies in worn-out urban fabrics necessitates a thorough feasibility evaluation to identify the associated obstacles and implementation prerequisites. The current study aimed to identify critical indicators for the feasibility of employing contemporary green building techniques in the repair of worn-out urban fabrics in Iran. The revitalization of worn-out urban fabrics is essential to enhancing the quality of life of urban inhabitants. Regarding this matter, the concept of green buildings, which emphasizes environmental sustainability, deserves significant attention. Meanwhile, feasibility assessments can help to successfully implement these changes in worn-out urban fabrics. Accordingly, the current study seeks to determine the essential indicators for the feasibility assessment of using initiative green building methods in the revitalization of worn-out urban fabric. Design/methodology/approach—In this vein, two rounds of the Delphi survey technique were carried out to identify and consolidate the indicators for the feasibility assessment of using initiative green building methods in the revitalization of the worn-out urban fabric in Iran. A research questionnaire was developed after reviewing the literature. It consists of four main dimensions (i.e., environmental, cultural–social, management–legal, and technical–technological) containing a total of 26 distinct indicators. The questionnaire was distributed among 123 experienced specialists. Eventually, the collected data were analyzed using the SPSS and Smart PLS programs. Findings—The results revealed that identified dimensions and indicators can be considered significant and essential indices in evaluating the use of initiative green building methods in the revitalization of worn-out urban fabric. Furthermore, the sequence of importance of the dimensions was environmental, followed by technical and technological, cultural and social, and managerial and legal. The environment, with an average rating of 3.33, ranked first; technical–technology, with an average rating of 2.45, ranked second; cultural–social, with an average rating of 2.15, ranked third; and management–legal, with an average rating of 2.07, ranked fourth. Furthermore, among the ranked indicators, the utilization of natural plants as a source of inspiration for living design in communal areas, aimed at toxin absorption and gas mitigation while achieving thermal equilibrium, received the highest average rating of 18.22, securing the first position. Conversely, the indicator assessing residents’ financial capacity, and the establishment of executive assurances and governmental support for the revitalization of the neighborhoods’ fabric garnered the lowest average rating of 10.98, placing it 26th and final. Originality/value—This research’s findings can significantly influence public policy and urban planning initiatives, aiding in the sustainable repair of worn-out urban fabrics in Iran by offering a systematic framework for evaluating the viability of innovative green building techniques. Full article

Onboard millimeter-wave radar is one way to improve helicopter flight safety at low altitudes in difficult meteorological conditions. Low-altitude flight is associated with the risk of collision with low-visibility obstacles such as power lines. Since power lines have the property of polarization anisotropy, it is necessary to use radar polarimetry methods to increase the radar visibility of such obstacles for detection. This paper investigates the possibility of identifying low-visibility polarization-anisotropic objects, such as power lines, in polarimetric images obtained by onboard helicopter radars to warn the pilot about the danger of a collision with a low-visibility object. Consequently, the use of a polarimetric radar with a polarization modulation of the emitted signal, the two-channel polarization synchronous reception of reflected signals and Eigenvalue signal processing in real time was proposed, aiming to eliminate the dependence of reflected signals on the spatial orientation of power transmission line wires. Additionally, an optimal algorithm for adaptive polarization selection, obtained by the maximum likelihood method, was proposed to detect such objects against the background of the underlying surface. The effectiveness of the proposed algorithm was tested using computer modeling methods. Moreover, a W-band polarimetric radar system was designed for experimental studies of this concept. The developed radar system provides digital real-time signal processing and the reconstruction of composite polarimetric images. It displays information about the polarization characteristics of the objects in the scanned area and the results of their polarization selection. Therefore, the system informs the pilot about dangerous objects along the helicopter’s route. Radar experimental tests in actual environmental conditions have confirmed the proposed concept’s correctness and the proposed method and algorithm’s effectiveness. Full article

Prosthetics, a rapidly advancing field in dentistry, aims to improve patient comfort and aesthetics by addressing the challenge of replacing missing teeth. A critical obstacle in dental implantation is the condition of the jawbone, which often necessitates reconstruction prior to implant placement. Guided bone regeneration (GBR) and guided tissue regeneration (GTR) techniques utilize membranes that act as scaffolds for bone and tissue growth while serving as barriers against rapidly proliferating cells and pathogens. Commonly used membranes, such as poly(tetrafluoroethylene) (PTFE) and collagen, have significant limitations—PTFE is non-bioresorbable and requires secondary removal, while collagen lacks adequate mechanical strength and exhibits unpredictable degradation rates. To overcome these challenges, nanofiber membranes produced via electrospinning using polylactic acid (PLA) were developed. The novel composites were functionalized with bioactive additives, including periclase (MgO) nanoparticles and polydopamine (PDA), to enhance osteoblast adhesion, antibacterial properties, and tissue regeneration. This study comprehensively evaluated the biological, mechanical, and physicochemical properties of the prepared nanofibrous scaffolds. Experimental results revealed controlled degradation rates and improved hydrophilicity due to surface modifications with PDA and MgO. Moreover, the nanofibers exhibited enhanced swelling behavior, which promoted nutrient exchange while maintaining structural integrity over prolonged periods. The incorporation of bioactive additives contributed to superior osteoblast proliferation, antibacterial activity, and growth factor immobilization, supporting bone tissue regeneration. These findings suggest that the developed nanofibrous composites are a promising candidate for GBR and GTR applications, offering a balanced combination of biological activity, mechanical performance, and degradation behavior tailored for clinical use. Full article

To tackle the challenge of enhancing moving target detection performance in environments characterized by small sample sizes and non-uniformity, methods rooted in sparse signal reconstruction have been incorporated into Space-Time Adaptive Processing (STAP) algorithms. Given the prominent sparse nature of clutter spectra in the angle-Doppler domain, adopting sparse recovery algorithms has proven to be a feasible approach for accurately estimating high-resolution spatio-temporal two-dimensional clutter spectra. Sparse Bayesian Learning (SBL) is a pivotal tool in sparse signal reconstruction and has been previously utilized, yet it has demonstrated limited success in enhancing sparsity, resulting in insufficient robustness in local fitting. To significantly improve sparsity, this paper introduces a hierarchical Bayesian prior framework and derives iterative parameter update formulas through variational inference techniques. However, this algorithm encounters significant computational hurdles during the parameter update process. To overcome this obstacle, the paper proposes an enhanced Variational Bayesian Inference (VBI) method that leverages prior information on the rank of the temporal clutter covariance matrix to refine the parameter update formulas, thereby significantly reducing computational complexity. Furthermore, this method fully exploits the joint sparsity of the Multiple Measurement Vector (MMV) model to achieve greater sparsity without compromising accuracy, and employs a first-order Taylor expansion to eliminate grid mismatch in the dictionary. The research presented in this paper enhances the moving target detection capabilities of STAP algorithms in complex environments and provides new perspectives and methodologies for the application of sparse signal reconstruction in related fields. Full article

Point cloud-based BIM reconstruction is an effective approach to enabling the digital documentation of existing buildings. However, current methods often demand substantial time and expertise for the manual measurement of building dimensions and the drafting of BIMs. This paper proposes an automated approach to BIM modeling of the external surfaces of existing buildings, aiming to streamline the labor-intensive and time-consuming processes of manual measurement and drafting. Initially, multi-angle images of the building are captured using drones, and the building’s point cloud is reconstructed using 3D reconstruction software. Next, a multi-plane segmentation technique based on the RANSAC algorithm is applied, facilitating the efficient extraction of key features of exterior walls and planar roofs. The orthophotos of the building façades are generated by projecting wall point clouds onto a 2D plane. A lightweight convolutional encoder–decoder model is utilized for the semantic segmentation of windows and doors on the façade, enabling the precise extraction of window and door features and the automated generation of AutoCAD elevation drawings. Finally, the extracted features and segmented data are integrated to generate the BIM. The case study results demonstrate that the proposed method exhibits a stable error distribution, with model accuracy exceeding architectural industry requirements, successfully achieving reliable BIM reconstruction. However, this method currently faces limitations in dealing with buildings with complex curved walls and irregular roof structures or dense vegetation obstacles. Full article

This paper presents a 3D vision-based autonomous navigation system for wheeled mobile robots equipped with an RGB-D camera. The system integrates SLAM (simultaneous localization and mapping), motion planning, and obstacle avoidance to operate in both static and dynamic environments. A real-time pipeline is developed to construct sparse and dense maps for precise localization and path planning. Navigation meshes (NavMeshes) derived from 3D reconstructions facilitate efficient A* path planning. Additionally, a dynamic “U-map” generated from depth data identifies obstacles, enabling rapid NavMesh updates for obstacle avoidance. The proposed system achieves real-time performance and robust navigation across diverse terrains, including uneven surfaces and ramps, offering a comprehensive solution for 3D vision-guided robotic navigation. Full article

The aviation industry relies on continuous inspections to ensure infrastructure safety, particularly in confined spaces like aircraft fuel tanks, where human inspections are labor-intensive, risky, and expose workers to hazardous exposures. Robotic systems present a promising alternative to these manual processes but face significant technical and operational challenges, including technological limitations, retraining requirements, and economic constraints. Additionally, existing prototypes often lack open-source documentation, which restricts researchers and developers from replicating setups and building on existing work. This study addresses some of these challenges by proposing a modular, open-source framework for robotic inspection systems that prioritizes simplicity and scalability. The design incorporates a robotic arm and an end-effector equipped with three RGB-D cameras to enhance the inspection process. The primary contribution lies in the development of decentralized software modules that facilitate integration and future advancements, including interfaces for teleoperation and motion planning. Preliminary results indicate that the system offers an intuitive user experience, while also enabling effective 3D reconstruction for visualization. However, improvements in incremental obstacle avoidance and path planning inside the tank interior are still necessary. Nonetheless, the proposed robotic system promises to streamline development efforts, potentially reducing both time and resources for future robotic inspection systems. Full article