Search Results (79)

Underwater vehicles (UVs) are pivotal for ocean exploration, yet their effectiveness is fundamentally constrained by acoustic performance in noisy and dynamic seas. Self-noise, non-stationary interference, and extreme conditions not only degrade sensing, navigation, and stealth but also cascade into losses in propulsion efficiency, actuation reliability, and control precision. This review provides a system-performance-oriented synthesis of advances across four key areas: bioinspired and intelligent noise reduction materials/structures, active noise control and adaptive signal processing, noise-robust navigation and collaborative localization, and deep learning-enhanced acoustic perception. Key findings indicate that bioinspired surfaces reduce flow noise by ≈5 dB, adaptive filtering improves $\mathrm{SNR}$ by up to 20 dB, and distributed robust filtering ensures multi-AUV consistency under uncertainty. These developments collectively establish acoustic performance not as a parallel metric, but as a fundamental enabler and critical bottleneck for the integrated propulsion-actuation-control stack of next-generation UVs. Consequently, this review outlines viable pathways toward high-performance acoustic–mechanical integration. Full article

This paper presents the H-CoRE (Heterogeneous Cooperative Multi-Robot Execution) framework designed to enable autonomous multi-robot operations in GNSS-denied environments. Built on an ROS 2-based architecture, H-CoRE enables collaborative, structured task execution through standardized software stacks. Each robot’s stack combines a high-level executive system with an agent-specific motion layer and leverages multi-sensor fusion for localization and mapping. The framework is inherently reconfigurable, allowing individual agents to operate autonomously or as part of a multi-robot team for collaborative missions. In the considered scenario, the system integrates aerial and ground vehicles, a fixed pan–tilt–zoom camera, and a human supervisory interface within a unified, modular infrastructure. The proposed system has been deployed in indoor, GNSS-denied environments, demonstrating autonomous navigation, cooperative area coverage, and real-time information sharing across multiple agents. Experimental results confirm the effectiveness of H-CoRE in maintaining general awareness and mission continuity, paving the way for future applications in search-and-rescue, inspection, and exploration tasks. Full article

For Autonomous Vehicles (AVs), recognizing traffic lights and signs is critical for safety because perception errors directly affect navigation decisions. Real-world disturbances such as glare, rain, dirt, and graffiti, as well as digital adversarial attacks, can lead to dangerous misclassifications. Current research lacks (i) temporal continuity (stable detection across consecutive frames to prevent flickering misclassifications), (ii) multi-field-of-view (FoV) sensing, and (iii) integrated defenses against both digital and natural degradation. This paper presents two principal contributions: (1) a three-layer defense framework integrating feature squeezing, inference-time temperature scaling (softmax $\tau$ = 3 without distillation training), and entropy-based anomaly detection with sequence-level temporal voting; (2) a 500 sequence dual-FoV benchmark (30k base frames, 150k with perturbations) from aiMotive, Waymo, Udacity, and Texas sources across four operational design domains. The unified defense stack achieves 79.8% mAP on a 100-sequence test set (6k base frames, 30k with perturbations), reducing attack success rate from 37.4% to 18.2% (51% reduction) and high-risk misclassifications by 32%. Cross-FoV validation and temporal voting enhance stability under lighting changes (+3.5% mAP) and occlusions (+2.7% mAP). Defense improvements (+9.5–9.6% mAP) remain consistent across native 3D (aiMotive, Waymo) and projected 2D (Udacity, Texas) annotations. Preliminary recapture experiments (n = 15 scenarios) show 2.5% synthetic–physical ASR gap (p = 0.18), though larger validation is needed. Code, models, and dataset reconstruction tools are publicly available. Full article

(1) Background and introduction: High-demand lumbosacral fusions are often supplemented with sacral-alar-iliac (SAI) screws. The idealized SAI trajectory was estimated to traverse 35 mm of sacrum before crossing the sacroiliac (SI) joint. However, there is debate on how much osseous purchase SAI screws achieve. The goal of this study was to determine the amount of osseous engagement achieved using a porous fusion–fixation screw (PFFS) when placed in a stacked SAI configuration. (2) Materials and methods: We retrospectively reviewed 40 consecutive patients who underwent sacropelvic fixation with stacked PFFS at our institution from 1 June 2022 to 30 June 2023, using intraoperative computed tomography (CT)-based computer navigation. A snapshot of each screw was taken and the length of purchase within the sacrum and ilium was measured on the axial image along the anterior and posterior aspect of each screw. Nineteen patients did not have adequate images available for review and were excluded. (3) Results: The overall mean anterior sacral engagement was 38.6 mm (±8.2 mm), which was found to be statistically significantly greater than the hypothesized threshold of 35 mm (p < 0.001), while posterior sacral engagement was 28.1 mm (±8.6 mm), which was not found to be statistically significantly greater than the hypothesized threshold of 35 mm (p = 1). The mean difference in sacral engagement between the anatomical location for the cephalad screws was 10.3 mm (p < 0.001) and 10.6 mm (p < 0.001) for the caudal screws. The total sacral surface area available for bone ingrowth for bilateral stacked PFFS was calculated to be 3338.3 mm², while the total iliac surface area available for bone ingrowth was 4364.8 mm². A mean difference in surface area availability between anatomical locations was −689.5 mm² (p < 0.001) for the sacrum and 689.5 mm² (p < 0.001) for the ilium. (4) Discussion and conclusions: The SAI trajectory screws in this cohort of patients achieved approximately 39 mm of sacrum engagement anteriorly and 28 mm posteriorly. This is consistent with prior estimates based on the idealized SAI pathway through the sacrum. PFFSs allow for simultaneous sacropelvic fixation and SI joint fusion, which may reduce the incidence of de novo SI joint pain in patients with long fusion constructs. Full article

This study benchmarks three ROS 2 Navigation2 local controllers—Dynamic Window Approach Based (DWB), Regulated Pure Pursuit (RPP), and Model Predictive Path Integral (MPPI)—under three complementary operational stressors in simulation: (i) a structured corridor with a transient dynamic obstacle, (ii) a sloped environment where terrain inclination biases a planar 2D LiDAR costmap through spurious occupancy projections, and (iii) a narrow corridor that amplifies inflation effects. A reproducible rosbag2-based protocol records five key performance indicators per trial: time-to-goal, lateral tracking RMSE, stopped time, heading oscillations, and control effort. With 15 independent repetitions per cell (scene × controller × direction), the design yields 270 trials. The results expose complementary value profiles: RPP minimizes mission time, DWB produces the fewest heading oscillations through critic-based shaping, and MPPI achieves the lowest control effort via smooth trajectory generation. In the sloped scene, the tracking RMSE differences compress across all controllers—a signature of a perception-limited regime in which costmap bias overshadows controller logic. These findings translate into an actionable controller-selection guide and a reproducible baseline for quantifying gains from upstream perception and cost-representation improvements. In concrete terms, we contribute (i) a controlled benchmark with fixed planning, localization, and costmaps, (ii) full configuration disclosure (controller parameters, costmap settings, and software versions with package pinning), and (iii) a scene-specific costmap distortion index that links slope-induced local cost bias to measurable performance shifts, underpinning a decision matrix for controller selection in semi-structured environments. Full article

The successful transfer of autonomous driving stacks (ADS) from simulation to the real world faces two main challenges: the Reality Gap (RG)—mismatches between simulated and real behaviors—and the Performance Gap (PG)—differences between expected and achieved performance across domains. We propose a Methodology for Closing Reality and Performance Gaps (MCRPG), a structured and iterative approach that jointly reduces RG and PG through parameter tuning, cross-domain metrics, and staged validation. MCRPG comprises three stages—Digital Twin, Parallel Execution, and Real-World—to progressively align ADS behavior and performance. To ground and validate the method, we present an open-source, cost-effective Development and Validation Platform (DVP) that integrates an ROS-based modular ADS with the CARLA simulator and a custom autonomous electric vehicle. We also introduce a two-level metric suite: (i) Reality Alignment via Maximum Normalized Cross-Correlation (MNCC) over multi-modal signals (e.g., ego kinematics, detections), and (ii) Ego-Vehicle Performance covering safety, comfort, and driving efficiency. Experiments in an urban scenario show convergence between simulated and real behavior and increasingly consistent performance across stages. Overall, MCRPG and DVP provide a replicable framework for robust, scalable, and accessible Sim2Real research in autonomous navigation techniques. Full article

Monitoring urban subsidence over ultra-long periods using time-series Interferometric synthetic aperture radar (InSAR) technology is critically important. Conventional approaches, however, face two main limitations: significant atmospheric phase residuals in complex urban settings, and discontinuous temporal time-series with short temporal coverage due to single-platform data constraints. To address these limitations, this study presents a new method for estimating ultra-long-term subsidence time series in urban areas, which combines Interferometric Subset Stacking (ISS) with multi-platform data fusion (DF). The methodology firstly processes TerraSAR-X and Sentinel-1A datasets through differential interferometry and applies ISS for atmospheric phase suppression. Next, bilinear interpolation unifies the spatial resolution and aligns the spatial reference frames of the two datasets. Subsequently, joint modeling derives subsidence velocities. Finally, temporal integration via linear interpolation and moving averaging produces a unified spatio-temporal deformation sequence. Applied to the Beijing region, China, this approach generated a 12-year ultra-long-term subsidence time series result (2012–2024), revealing maximum cumulative subsidence of 1100 mm spatially correlated with groundwater extraction patterns. Validation against Global Navigation Satellite System (GNSS) data showed strong agreement (correlation coefficient: 0.94, Root Mean Square Error (RMSE): 6.3 mm). The method achieved substantial atmospheric reduction—67.7% for Sentinel-1A and 24.1% for TerraSAR-X—representing approximately 15–20% accuracy improvement over conventional Generic Atmospheric Correction Online Service (GACOS) for InSAR. By effectively utilizing multi-platform data, this approach makes fuller use of the available phase information and compensates for the temporal gaps inherent in single-satellite datasets. It thus offers a valuable framework for long-term urban deformation monitoring. Full article

Autonomous navigation in unknown environments demands policies that can jointly perceive semantic context and geometric safety. Existing Transformer-enabled deep reinforcement learning (DRL) frameworks, such as the Goal-guided Transformer Soft Actor–Critic (GoT-SAC), rely on temporal stacking of multiple RGB frames, which encodes short-term motion cues but lacks explicit spatial understanding. This study introduces a geometry-aware RGB-D early fusion modality that replaces temporal redundancy with cross-modal alignment between appearance and depth. Within the GoT-SAC framework, we integrate a pixel-aligned RGB-D input into the Transformer encoder, enabling the attention mechanism to simultaneously capture semantic textures and obstacle geometry. A comprehensive systematic ablation study was conducted across five modality variants (4RGB, RGB-D, G-D, 4G-D, and 4RGB-D) and three fusion strategies (early, parallel, and late) under identical hyperparameter settings in a controlled simulation environment. The proposed RGB-D early fusion achieved a 40.0% success rate and +94.1 average reward, surpassing the canonical 4RGB baseline (28.0% success, +35.2 reward), while a tuned configuration further improved performance to 54.0% success and +146.8 reward. These results establish early pixel-level multimodal fusion (RGB-D) as a principled and efficient successor to temporal stacking, yielding higher stability, sample efficiency, and geometry-aware decision-making. This work provides the first controlled evidence that spatially aligned multimodal fusion within Transformer-based DRL significantly enhances mapless navigation performance and offers a reproducible foundation for sim-to-real transfer in autonomous mobile robots. Full article

A growing challenge in mobile robotics is the reliance on complex graphical interfaces and rigid control pipelines, which limit accessibility for non-expert users. This work introduces a latency-aware benchmarking framework that enables natural-language robot navigation by integrating multiple Large Language Models (LLMs) with the Robot Operating System 2 (ROS 2) Navigation 2 (Nav2) stack. The system allows robots to interpret and act upon free-form text instructions, replacing traditional Human–Machine Interfaces (HMIs) with conversational interaction. Using a simulated TurtleBot4 platform in Gazebo Fortress, we benchmarked a diverse set of contemporary LLMs, including GPT-3.5, GPT-4, GPT-5, Claude 3.7, Gemini 2.5, Mistral-7B Instruct, DeepSeek-R1, and LLaMA-3.3-70B, across three local planners, namely Dynamic Window Approach (DWB), Timed Elastic Band (TEB), and Regulated Pure Pursuit (RPP). The framework measures end-to-end response latency, instruction-parsing accuracy, path quality, and task success rate in standardised indoor scenarios. The results show that there are clear trade-offs between latency and accuracy, where smaller models respond quickly but have less spatial reasoning, while larger models have more consistent navigation intent but take longer to respond. The proposed framework is the first reproducible multi-LLM system with multi-planner evaluations within ROS 2, supporting the development of intuitive and latency-efficient natural-language interfaces for robot navigation. Full article

Autonomous mobile robots (AMRs) require robust, efficient path planning to operate safely in complex, often dynamic environments (e.g., logistics, transportation, and healthcare). This systematic review focuses on advanced metaheuristic and learning- and reasoning-based (cognitive) techniques for AMR path planning. Drawing on approximately 230 articles published between 2018 and 2025, we organize the literature into two prominent families, metaheuristic optimization and AI-based navigation, and introduce and apply a unified taxonomy (planning scope, output type, and constraint awareness) to guide the comparative analysis and practitioner-oriented synthesis. We synthesize representative approaches, including swarm- and evolutionary-based planners (e.g., PSO, GA, ACO, GWO), fuzzy and neuro-fuzzy systems, neural methods, and RL/DRL-based navigation, highlighting their operating principles, recent enhancements, strengths, and limitations, and typical deployment roles within hierarchical navigation stacks. Comparative tables and a compact trade-off synthesis summarize capabilities across static/dynamic settings, real-world validation, and hybridization trends. Persistent gaps remain in parameter tuning, safety, and interpretability of learning-enabled navigation; sim-to-real transfer; scalability under real-time compute limits; and limited physical experimentation. Finally, we outline research opportunities and open research questions, covering benchmarking and reproducibility, resource-aware planning, multi-robot coordination, 3D navigation, and emerging foundation models (LLMs/VLMs) for high-level semantic navigation. Collectively, this review provides a consolidated reference and practical guidance for future AMR path-planning research. Full article

Electrokinetics has established itself as a central pillar in microfluidic research, offering a powerful, non-mechanical means to manipulate fluids and analytes. Mechanisms such as electroosmotic flow (EOF), electrophoresis (EP), and dielectrophoresis (DEP) re-main central to the field, once more layers of complexity emerge heterogeneous interfaces, viscoelastic liquids, or anisotropic droplets are introduced. Five research directions have become prominent. Field-driven manipulation of droplets and emulsions—most strikingly Janus droplets—demonstrates how asymmetric interfacial structures generate unconventional transport modes. Electrokinetic injection techniques follow as a second focus, because sharply defined sample plugs are essential for high-resolution separations and for maintaining analytical accuracy. Control of EOF is then framed as an integrated design challenge that involves tuning surface chemistry, engineering zeta potential, implementing nanoscale patterning, and navigating non-Newtonian flow behavior. Next, electrokinetic instabilities and electrically driven micromixing are examined through the lens of vortex-mediated perturbations that break diffusion limits in low-Reynolds-number flows. Finally, electrokinetic enrichment strategies—ranging from ion concentration polarization focusing to stacking-based preconcentration—demonstrate how trace analytes can be selectively accumulated to achieve detection sensitivity. Ultimately, electrokinetics is converging towards sophisticated integrated platforms and hybrid powering schemes, promising to expand microfluidic capabilities into previously inaccessible domains for analytical chemistry and diagnostics. Full article

The proliferation of big data applications across various industries has led to a paradigm shift in data architecture, with traditional approaches giving way to more agile and scalable frameworks. The evolution of big data architecture began with the emergence of the Hadoop-based data stack, leveraging technologies like Hadoop Distributed File System (HDFS) and Apache Spark for efficient data processing. However, recent years have seen a shift towards modern data stacks, offering flexibility and diverse toolsets tailored to specific use cases. Concurrently, cloud computing has revolutionized big data management, providing unparalleled scalability and integration capabilities. Despite their benefits, navigating these data stack paradigms can be challenging. While existing literature offers valuable insights into individual data stack paradigms, there remains a dearth of studies that offer practical, in-depth comparisons of these paradigms across the entire big data value chain. To address this gap in the field, this paper examines three main big data stack paradigms: the Hadoop data stack, modern data stack, and cloud-based data stack. Indeed, we conduct in this study an exhaustive architectural comparison of these stacks covering the entire big data value chain from data acquisition to exposition. Moreover, this study extends beyond architectural considerations to include end-to-end use case implementations for a comprehensive evaluation of each stack. Using a large dataset of Amazon reviews, different data stack scenarios are implemented and compared. Furthermore, the paper explores critical factors such as data integration, implementation costs, and ease of deployment to provide researchers and practitioners with a relevant and up-to-date reference for navigating the complex landscape of big data technologies and making informed decisions about data strategies. Full article

Sea surface wind speed is a crucial parameter for studying climate change and ocean dynamics. Accurate, real-time measurements are essential for meteorological and oceanographic observations. Global Navigation Satellite System Reflectometry (GNSS-R) is a key technology for sea surface wind speed retrieval. Existing wind speed retrieval models employ two primary approaches: unified modeling across the entire wind speed range and independent modeling for partitioned wind speed intervals. The former cannot effectively address physical property variations across wind speed ranges. The latter, while mitigating this issue, relies on empirical thresholds for interval partitioning that ignore actual data distribution and struggles to assign new samples to appropriate intervals during prediction. To address these limitations, this study employs the Gradient-Boosted Adaptive Multi-Objective Simulated Annealing (GAMSA) algorithm to construct a multi-objective optimization function and perform data-driven wind speed interval partitioning. Specialized XGBoost sub-models are then constructed for each partitioned interval, and their predictions are integrated through a stacking ensemble learning architecture. The experiments utilize a Cyclone Global Navigation Satellite System (CYGNSS) and ERA5 reanalysis data. The experimental results show that the proposed method reduces the root mean square error (RMSE) from 1.77 m/s to 1.43 m/s and increases the coefficient of determination (R²) from 0.6293 to 0.7770 compared with a global XGBoost model. It also exhibits enhanced accuracy under high wind speeds (>16 m/s) and, when independently validated with buoy data, achieves an RMSE of 1.52 m/s and R² of 0.79. The proposed method improves retrieval accuracy across both overall and individual wind speed intervals, avoids the sample isolation problem inherent in traditional empirical partitioning methods, and resolves the issue of assigning new samples to appropriate sub-models during application. Full article

The growing adoption of laminated fiber-reinforced polymer (FRP) composites in aerospace, automotive, and civil engineering demands advanced design methodologies capable of navigating their complex anisotropic behavior. While traditional design approaches rely heavily on iterative simulations and classical optimization, recent advances in artificial intelligence (AI) offer a transformative alternative. This review systematically examines the expanding role of AI in composite design and optimization—highlighting a critical transition from physics-based modeling to data-driven, intelligent frameworks. This paper emphasizes emerging AI paradigms not yet widely covered in the composite literature, including Explainable AI (XAI) for interpretable decision-making and Large Language Models (LLMs) for automating design synthesis and knowledge retrieval. Key findings demonstrate AI’s capacity to efficiently optimize stacking sequences, ply orientations, and manufacturing parameters while satisfying multi-objective constraints such as weight, stiffness, and damage tolerance. Furthermore, we explore AI’s integration across the composite lifecycle—from surrogate-assisted finite element analysis and uncertainty-aware design allowables to in-service structural health monitoring. By bridging the gap between computational intelligence and industrial practicability, this review underscores AI’s potential not as a supplementary tool, but as a foundational technology poised to redefine next-generation composite engineering. Full article

This research work presents the design and the development of a cost-effective autonomous mobile robot for locating misplaced objects within unstructured indoor environments. The tools integrated into the proposed system for perception and localization are a hardware architecture equipped with LiDAR, an inertial measurement unit (IMU), and wheel encoders. The system also includes an ROS2-based software stack enabling autonomous navigation via the NAV2 framework and Adaptive Monte Carlo Localization (AMCL). For real-time object detection, a lightweight YOLO11n model is developed and implemented on a Raspberry Pi 4 to enable the robot to identify common household items. The robot’s motion control is achieved by a fuzzy logic-enhanced PID controller that dynamically modifies gain values based on navigation conditions. Remote supervision, task management, and real-time status monitoring are provided by a user-friendly Flutter-based mobile application. Simulations and real-world experiments demonstrate the robustness, modularity, and responsiveness of the robot in dynamic environments. This robot achieves a 3 cm localization error and a 95% task execution success rate. Full article