Search Results (2,161)

While infrastructure asset management increasingly relies on high-resolution drone imagery, existing workflows suffer from fragmented information management and dependence on costly local processing infrastructure. This paper addresses these limitations by using a cloud-native spatial intelligence hub that converts raw inspection imagery into an interactive and queryable three-dimensional information layer. The system integrates a timeout-resilient orchestration layer for photogrammetry pipelines, a multi-user three-dimensional environment for collaborative review, and a PostGIS-backed spatial database that stores defects as georeferenced anchors. We further introduce a spatial anchoring workflow mapping three-dimensional interactions to world coordinates, retrieving context-relevant images via frustum-based visibility scoring. Evaluated on real inspection datasets, the serverless architecture achieved end-to-end reconstruction in under one hour with sub-25 ms query latency. Results indicate that acquisition geometry, particularly oblique convergent viewpoints, is a stronger predictor of reconstruction complexity than image count. This work establishes a reproducible reference architecture, enabling a transition from file-centric documentation to traceable, spatially indexed evidence management for infrastructure Digital Twins. Full article

Existing truck–drone collaborative routing models predominantly assume fixed flight speeds, overlooking the non-linear coupling among speed, payload, and energy consumption, which limits urban delivery efficiency. To bridge this gap, this paper proposes the multiple flying sidekick traveling salesman problem with variable drone speed (mFSTSP-VDS). Formulating drone cruising speed as a continuous variable under strict non-linear energy constraints, we design a hybrid algorithm (ALNS-SA-VND) to jointly optimize routing, task allocation, and speed. Empirical analysis of Wuhan’s road network demonstrates the VDS strategy’s robustness. Specifically, VDS reduces the system makespan by up to 17.5% compared to rigid maximum-speed strategies, with consistent stability across varying load scenarios. By adaptively trading permissible battery capacity for temporal synchronization, VDS effectively mitigates unnecessary truck waiting times at rendezvous nodes. This study quantitatively validates the impact of sortie-specific speed adaptation on time efficiency, providing an exploratory theoretical baseline for tactical-level planning in smart logistics networks. Full article

The growing need for sustainable wastewater treatment highlights the importance of low-energy solutions such as horizontal subsurface flow constructed wetlands (HSSF CWs). While effective, these systems often face clogging issues that reduce performance and lifespan. This study investigates clogging dynamics in a Water Treatment Plant (Lleida, Spain) using a multidisciplinary approach. Non-invasive geophysical methods such as Electrical Resistivity Tomography (ERT) and Induced Polarization (IP) were combined with high-resolution drone imagery to characterize surface and subsurface indicators of clogging. Drone data captured surface anomalies, while geophysical measurements revealed subsurface obstructions. The integrated analysis identifies clogged zones and shows a strong spatial correlation between surface features and geophysical anomalies. These results validate the use of drone imagery as a rapid, non-invasive diagnostic tool and demonstrate the effectiveness of combining remote sensing with geophysical techniques for wetland assessment. This approach supports improved monitoring, targeted maintenance, and optimized long-term performance of HSSF CWs. Full article

Anterior cruciate ligament (ACL) injuries remain a leading cause of morbidity in athletic populations, with 70–80% occurring through non-contact mechanisms driven by biomechanical risk factors including knee valgus (>10°), low knee flexion (<30°), tibial internal rotation (>20°), and loading asymmetry (>15°), yet implementation of evidence-based neuromuscular training (which reduces injury risk by 50–70%) remains limited due to barriers in identifying at-risk individuals through accessible field-based screening. This narrative review synthesizes motion analysis technologies spanning laboratory-based optical systems (marker-based), wearable inertial measurement units (IMUs), computer vision and marker-less pose estimation, force plate and pressure-sensitive insole systems, and integrated drone-based field assessment platforms to address this critical gap. We present a three-tier clinical screening framework that progresses from basic anthropometric and single-plane video analysis to multi-modal biomechanical assessment using real-time kinematic feedback. As an illustrative example of emerging field-deployable technology, an integrated drone-based motion capture and smart insole system combining 4K video capture, AI-driven 3D motion reconstruction, and plantar pressure mapping is described to demonstrate how laboratory-quality biomechanical assessment can be achieved in ecologically valid field settings. This evidence-based review addresses current gaps between laboratory research and practical field deployment, with emphasis on cost-effectiveness, accessibility, and clinical utility for ACL injury prevention in diverse sporting environments. Full article

Future planetary exploration missions are expected to employ increasingly sophisticated aerial, ground, and hybrid robotic systems that must interact directly with extraterrestrial regolith during landing, takeoff, mobility, anchoring, sampling, and subsurface investigation activities. Consequently, understanding the mechanical behavior of lunar and Martian regolith is essential for the design and reliable operation of autonomous exploration platforms. This review examines drone–regolith interaction from a system-level perspective by integrating knowledge of regolith mechanical properties with findings from penetration mechanics, anchoring technologies, mobility studies, numerical modelling, and in situ mission observations. Key differences between lunar and Martian regolith are identified, highlighting the predominantly friction-driven behavior of lunar soils and the combined frictional–cohesive response frequently observed in Martian regolith. Lessons learned from planetary missions, particularly the Apollo and Mars InSight programs, demonstrate how system–soil mismatch can significantly affect penetration, stabilization, and surface-operation performance. The review further discusses the implications of regolith mechanics for landing stability, rotor–surface interaction, anchoring efficiency, subsurface access, and future drone-assisted exploration concepts. Finally, current challenges in experimental validation and numerical modelling are assessed, emphasizing the need for integrated approaches that combine soil mechanics, robotic system design, and environmental constraints to enable reliable autonomous operations on the Moon and Mars. Full article

The rapid growth of online shopping has increased demand for home deliveries, leading to sustainability issues and logistical challenges such as labour shortages and congestion. Autonomous delivery vehicles, including drones, street robots, autonomous vans, and mobile vending machines, are emerging as potential solutions. Understanding consumers’ perceptions of these technologies is critical for sustainable implementation. This exploratory study aimed to examine consumer reactions to emerging autonomous delivery services, providing insights into how consumers may respond to autonomous delivery systems encompassing multiple vehicle modes and the resulting policy implications. Eight online focus groups (n = 55) were conducted with a diverse range of participants to examine community attitudes to autonomous delivery services. Participants were shown videos depicting various autonomous delivery methods to foster informed responses. Thematic analysis of the transcripts identified recurring themes relating to participants’ preferences, concerns, and expectations. While participants had some concerns, they were largely receptive to using autonomous delivery services. Positive reactions centred around: (i) convenience, (ii) cost reductions, and (iii) novelty. Identified concerns included: (i) job losses, (ii) practical limitations of the delivery devices, (iii) degradation of urban environments, and (iv) facilitation of unhealthy lifestyles. Overall, the results suggest autonomous delivery systems have the potential to be popular, and proactive government policies are thus likely to be needed to ensure they are implemented in a manner that aligns with community expectations and minimises any negative sustainability outcomes. Full article

Background: Modern warfare has introduced novel mechanisms of injury, particularly drone-induced blast trauma, resulting in complex craniomaxillofacial injuries. These injuries differ substantially from typical ballistic wounds and require adapted surgical strategies. This study was conducted to evaluate the clinical characteristics, management approaches, and long-term outcomes of midfacial blast injuries. Methods: A retrospective analytical study was conducted on 41 patients with drone-induced midfacial blast injuries treated at a tertiary referral center in Armenia following the 2020 Nagorno-Karabakh War. All patients underwent surgical management after initial stabilization and were followed for 5 years. Clinical outcomes, complications, and reconstructive needs were assessed. Results: All patients presented with comminuted midfacial fractures, which were frequently associated with polytrauma (87.8%). Burns were observed in 82.9% of cases. Surgical management included radical debridement and early definitive osteosynthesis using titanium fixation systems. No cases of postoperative osteomyelitis, bone sequestration, or implant failure were observed during the 5-year follow-up period. Patients with extensive soft tissue defects, particularly nasal and lip amputations, required multiple reconstructive procedures. Long-term follow-up revealed progressive soft tissue thinning over titanium meshes, especially in the zygomatico-orbital region, necessitating secondary interventions such as lipofilling. Conclusions: Drone-induced midfacial blast injuries represent a distinct and severe form of trauma. Early definitive reconstruction following adequate debridement was associated with favorable outcomes. However, soft tissue reconstruction remains challenging and often requires staged procedures. Long-term follow-up is essential to manage delayed complications and optimize aesthetic outcomes. Full article

With the development of the low-altitude economy, low-altitude intelligent agents such as delivery robots, courier drones, and outdoor cleaning robots are gradually moving towards widespread application. One of the core challenges faced by such systems is localization and mapping in complex scenarios characterized by satellite signal denial and unknown environmental prior information. To address this requirement, this paper proposes ESKF-g2o-SLAM, a stereo visual-inertial SLAM system that integrates an ESKF (Error-State Kalman Filter)-based visual-inertial odometry front-end with an ORB-feature-based g2o graph optimization back-end in a cascaded, loosely coupled manner. The proposed method was evaluated on 11 sequences of the EuRoC dataset and compared with state-of-the-art approaches including ORB-SLAM2 (stereo), MSCKF-VIO, OKVIS, and VINS-Fusion (stereo). Ablation studies show marginal improvements on selected sequences and suggest potential robustness advantages under more challenging visual conditions. Experimental results show that our method achieves competitive accuracy in terms of both Absolute Trajectory Error (ATE) and Relative Pose Error (RPE), exhibiting good robustness and stability. Full article

Real-time video object detection on unmanned aerial vehicles (UAVs) is essential for urban inspection and autonomous perception, yet its deployment on edge devices is severely constrained by the high computational cost of accurate detectors, the quantization sensitivity of hybrid convolution-attention networks, and the system-level latency of full video processing pipelines. To address these challenges, we present DUST-YOLO, a deployment-oriented algorithm-hardware co-design framework, where structured pruning and mixed-precision quantization-aware training (QAT) are jointly optimized with TensorRT–DeepStream for efficient UAV small-object detection on edge platforms. First, we introduce a multi-dimensional structured pruning strategy that applies asymmetric channel pruning to convolutional and feature-fusion modules while compressing the Swin Transformer prediction heads and bottleneck stacks, thereby reducing parameters and computation with limited impact on multi-scale representation capability. Second, we develop a hardware-aware mixed-precision QAT scheme that maps computation-intensive backbone layers to INT8 while preserving the Transformer-related modules in FP16, improving inference efficiency while mitigating the accuracy loss caused by uniform low-bit quantization. Third, we compile the optimized network with TensorRT and integrate the resulting inference engine into a DeepStream-based asynchronous video pipeline on the edge platform, enabling end-to-end acceleration by reducing decoding, preprocessing, and memory-transfer overheads. Experimental results on the VisDrone2019-DET dataset and the NVIDIA Jetson Orin NX demonstrate that DUST-YOLO achieves 43.7% mAP@0.5 accuracy with an end-to-end latency of 36.3 ms and a throughput of 27.5 FPS. Compared with the state of the art, DUST-YOLO reduces end-to-end latency by 56.9% and improves end-to-end video throughput by $2.31\times$. Full article

Global Navigation Satellite Systems (GNSS)-denied environments pose significant challenges for autonomous drone navigation, requiring robust visual localization systems capable of real-time performance. Existing approaches either sacrifice accuracy for speed or fail to adapt to varying flight altitudes and orientations, limiting their practical deployment. We present Real-Time Elevation and Orientation-Aware Localization Architecture (REOLA), a visual localization system that combines similarity-driven autonomous window sizing, element-wise correlation-based orientation detection, and reinforcement learning with human feedback (RLHF) enhancement for publicly available satellite imagery. On desktop hardware (i7-10700K + RTX 3070), the REOLA achieved approximately 59 FPS performance with sub-5-m accuracy across diverse flight conditions through intelligent similarity-based matching, combined with efficient MobileNet-V3 embeddings and FAISS similarity search. For embedded deployment on NVIDIA Jetson Orin Nano, the system achieved 22.5 FPS, meeting real-time requirements for autonomous drone localization. The system autonomously selects optimal window sizes corresponding to the current elevation and determines drone orientation through element-wise correlation scoring across discrete rotation angles. Enhanced through RLHF, the REOLA achieved a $97.1\%$ success rate (sub-5-m localization) while processing frames in 17 milliseconds on desktop hardware (44.4 ms on embedded hardware), providing a substantial margin over real-time requirements. The approach demonstrates particular superiority over traditional keypoint-based methods in challenging environments with repetitive patterns such as agricultural fields, rocky mountains, dense forests, and grasslands, where conventional keypoint detection struggles. We explicitly identify featureless sand dune deserts and open-sea or coastal water flights as out of scope, since the reference satellite imagery in those regimes does not contain stable landmarks. Full article

Long-horizon trajectory prediction for unmanned aerial vehicles (UAVs) operating in constrained environments remains challenging because of strongly nonlinear dynamics, hidden control effects, and evolving destination-oriented behavior. This challenge is particularly pronounced for highly maneuverable cross-domain unmanned aerial vehicles (CDUAVs), whose glide trajectories are strongly coupled with control and environmental constraints. To address this problem, this paper proposes an intent-aware CNN–Informer framework for accurate long-horizon trajectory prediction. First, a control-affine reformulation of the vehicle dynamics is used to construct physically interpretable DBL control parameters, which reduce the learning difficulty associated with hidden control effects. Second, three continuous intent features—tangential no-fly zone avoidance distance, heading error angle, and relative closing velocity—are introduced to encode destination tendency and avoidance requirements. These features are fused with historical trajectory states and fed into a hybrid CNN–Informer network, where the CNN extracts local maneuver patterns and the Informer captures long-range temporal dependencies. Experiments on a constrained trajectory dataset demonstrate that the proposed method achieves the best performance among all compared models, including SSD-LSTM, Transformer, iTransformer, DLinear, and Informer. Compared with Informer, the proposed approach reduces the average prediction error by 17.2% and significantly improves terminal and maximum prediction errors. These results indicate that the proposed framework provides an effective and physically interpretable solution for long-horizon UAV trajectory prediction in constrained flight scenarios, with potential extensions to behavior-aware forecasting and guidance support in autonomous aerial systems. Full article

The rapid growth of the low-altitude economy and urban air mobility (UAM) is reshaping urban transport and infrastructure systems. However, current planning practices still tend to treat green spaces, stormwater facilities, and drone infrastructure as separate subsystems. This paper proposes a Garden Hydrology UAV collaborative infrastructure framework for resilient urban low-altitude logistics and inspection. Pocket parks and sponge city facilities (rain gardens, detention basins) are redesigned as multi-functional UAV bases that integrate take-off/landing and charging with stormwater retention and recreation. A SWMM-based hydrological model provides time-varying inundation and storage states, which are mapped into dynamic node availability constraints for UAV operations, using EPA SWMM 5.2. A multi-objective optimization model is formulated to minimize logistics operation cost, hydrological risk exposure and noise impact on sensitive receptors, while respecting airspace and battery constraints. A stylized 4 km² high-density district is used to evaluate three scenarios: depot-only operations, garden–UAV integration without hydrological coupling, and the full collaborative framework with SWMM-based node availability and high-precision navigation. Simulation results show that the integrated design reduces makespan by up to 19.7%, energy use by 22.3%, and hydrological risk exposure by 63.4%, while lowering noise exposure by 21.3%, relative to the baseline. The study suggests that garden and sponge city infrastructures can become key physical supports of smart low-altitude networks under the low-altitude economy. Full article

The transition toward smart grids and Industry 4.0 demands a fundamental shift in maintenance strategies, as manual inspection methods are increasingly being supplanted by automated monitoring systems. Among the advanced technologies for smart inspection, infrared imaging has advantages including non-contact operation, intuitive visualization, and predictive capabilities, which has become a cornerstone for autonomous inspection of critical power infrastructure. This review provides recent advancements in infrared imaging, with a specific focus on automated power system inspection. The discussion starts with an overview of the fundamental principles and system architectures, emphasizing the pivotal role of infrared detectors. A detailed analysis traces the technological evolution from traditional photon detectors to current uncooled microbolometers, and critically assesses emerging low-dimensional materials. The analysis highlights inherent performance trade-offs among sensitivity, operating temperature, and fabrication cost. Subsequently, the review explores advanced signal processing algorithms, such as real-time non-uniformity correction and adaptive noise suppression, which are typically implemented on FPGA platforms. Advanced optical configurations—encompassing computational imaging, lensless designs, and scattering suppression methods—are also discussed, demonstrating how their convergence enhances image fidelity and operational reliability in complex field environments. Representative application paradigms are surveyed, including drone-based transmission line inspections, patrol robots in substations, and fault diagnosis in photovoltaic plants; for each, operational efficacy and economic benefits are assessed. Despite considerable progress, several challenges persist, notably the performance–stability–cost trilemma in novel detector development, the substantial computational demands of end-to-end optimized systems, and a lack of standardization. Finally, the review outlines future research directions, such as high-performance uncooled arrays, AI-driven co-design of optics and algorithms, and the development of standardized, low-cost, intelligent inspection platforms. Full article

Small surface defects on large structures such as wind turbine blades, bridges, and pipelines pose significant safety risks if left undetected. Therefore, a laser triangulation system is designed for contactless surface geometry inspection from a flying drone at a working distance of 2 m. To enable single-shot triangulation measurements in dynamic aerial environments, a single-shot-capable approach is realized by means of a laser and a diffractive optical element for creating a dot-matrix illumination pattern and a camera for image recording. The setup, with 101 × 101 measurement points, is calibrated by using an interferometer as a reference, which shows a sub-pixel resolution capability. As a result, the depth resolution capability for each point amounts to 126 µm, while the lateral resolution capability is determined by the laser spots’ size of 0.6 mm and the spots’ interspacing of 1.75 mm. With the present configuration, unambiguous depth detection is possible for local surface gradients of up to 2.3 times the interspot distance between adjacent measurement points, and the field of view is 17.56 cm × 17.56 cm. Finally, surface defects with lateral sizes on the order of 1 cm and 0.5 cm are currently detectable, as is demonstrated by experimental results from in-flight measurements. Thus, the potential and challenges of single-shot laser triangulation for drone-based inspection in real-world scenarios are presented. Full article

In edge-intelligent systems, efficient resource management and task scheduling are critical but challenging due to the dynamic and heterogeneous nature of edge nodes (e.g., IoT devices, drones). We model this dynamic resource allocation challenge as an online sleeping Restless Multi-Armed Bandits (RMAB) problem, where each edge node (arm) operates as a Markov decision process. Unlike prior RMAB frameworks assuming perpetual availability, our setting captures the stochastic availability of edge nodes across rounds. The system controller (learner) is unaware of the transition functions, reward distributions, and node availability a priori. The goal is to maximize expected cumulative rewards through adaptive node selection. To explore this target problem, we first derive an asymptotically optimal sleeping-index policy (SIP) as the oracle based on the fluid process transformation. Then we propose OSILA (Online Sleeping Index-aware Learning Algorithm), featuring a Minimum Exploration Guarantee (MEG) mechanism for efficient exploration. This is coupled with a modified Linear Programming-based exploitation mechanism to construct an online sleeping index, effectively handling dynamic node availability. To the best of our knowledge, this work is the first to provide the theoretical analysis (which achieves $\tilde{\mathcal{O}}\left(K{T}^{2/3}\sqrt{logT}\right)$ regret where K is the number of arms and T is the time horizon) to the online sleeping RMAB problem. Empirical results validate both theoretical guarantees and practical effectiveness in dynamic edge computing environments. Full article