Search Results (541)

Commercial automotive technologies are advancing rapidly in areas such as electrification, connectivity, digital transformation, and autonomous systems. As military organizations increasingly incorporate commercial technologies, it is important to understand how commercial trends align with future battlefield requirements. This paper presents a framework for forecasting military ground vehicle requirements through 2040 by integrating commercial automotive technology trends with observations from contemporary warfare. This analysis identified automotive technology trajectories through a synthesis of published bibliometric studies covering major automotive research domains from 2016 to 2026. Military operational requirements were derived from battlefield narratives published by the Institute for the Study of War (ISW) and open-source vehicle loss data reported by Oryxspioenkop during the Russia–Ukraine War. The automotive and military analyses were then aligned to identify areas of convergence between commercial technology development and future battlefield requirements. The results indicate that future battlefields will be characterized by persistent surveillance, precision fires, contested logistics, contested electromagnetic environments, high attrition, and rapid battlefield adaptation. Future military vehicles will increasingly leverage commercial technologies to improve autonomous operations, predictive sustainment, fuel efficiency, resilient communications, and signature management while incorporating military-specific capabilities where required. The resulting framework provides a structured methodology for linking commercial automotive innovation with military vehicle modernization, acquisition planning, and future capability development. Full article

Weeds create a significant challenge in agricultural production by competing with crops for essential resources such as nutrients, sunlight, and water. This competition leads to reduced crop yields and quality, resulting in substantial economic losses. Consequently, there is a critical need for effective weed control strategies to mitigate the impact of unwanted plant growth and ensure sustainable agricultural practices. In precision agriculture, enabling autonomous navigation between crop rows during tasks such as weeding and harvesting presents a significant research challenge, particularly when leveraging cost-effective technological solutions. Effective robot navigation requires adaptive traffic management strategies and robust object recognition capabilities to distinguish between cultivated and uncultivated areas. In fact, the integration of computer vision techniques into these systems is essential for optimizing trafficability in cropping fields and enhancing robots’ dynamics for better working efficiency in agricultural environments. This review addresses the challenge of enhancing inter-row navigation in field crops and delivering reliable guidance for autonomous agricultural robots. A PRISMA-based systematic review methodology was adopted to identify, screen, and analyze 38 relevant studies selected from the Scopus and Web of Science databases. The selected studies are classified according to their target platform (Unmanned Ground Vehicles and Unmanned Aerial Vehicles) and grouped into three methodological categories: conventional computer vision, deep learning architectures, and hybrid approaches. The findings provide practical guidance for selecting appropriate vision-based crop row detection technologies according to the application requirements and highlight key research directions toward more robust, cost-effective, and adaptable autonomous navigation systems. This article presents an outline of artificial-intelligence-based row detection methods used in agricultural fields and a classification of related semantic segmentation approaches. Unlike previous surveys, it provides an overview of the technological progress in agricultural robots and navigation based on systems vision for crop row detection, with a focus on comparisons balancing technical performance with economic and practical constraints. Full article

Foggy weather degrades road images by reducing visibility, attenuating object contrast, and blurring boundaries, while autonomous ground vehicles require accurate and lightweight environment perception on resource-constrained onboard platforms. This study proposes Fog-YOLO11n, a lightweight traffic object detector based on YOLO11n. A GhostRTA backbone combines RepGhostNet with the proposed C2RTA module: RepGhostNet reduces redundant computation, whereas C2RTA uses channel statistics and multi-scale spatial context to reinforce weak low-contrast responses. FRISA, a dual-branch interactive attention module, separates semantic responses from residual details and uses bidirectional gates to suppress fog- and reflection-related textures while preserving object contours. SD-MPDIoU adaptively modulates corner-distance penalties according to relative box geometry and reweights samples by localization quality, improving regression stability for ambiguous boundaries. Experiments on the RTTS dataset show improvements of 3.80 and 2.52 percentage points in mAP@0.5 and mAP@0.5:0.95, respectively, over YOLO11n, while reducing parameters from 2.59 M to 2.14 M and computation from 6.44 to 5.66 GFLOPs. The resulting accuracy-complexity balance supports real-time foggy-road perception for autonomous unmanned vehicles. Full article

Aerospace engineering applications typically entail complex high-performance systems with sustainable safety. They are usually designed by multidisciplinary development teams where collaboration efficiency is undermined by the diversity of views (models) provided by the disciplines involved. This produces design deadlocks (augmenting risks and costs) when crosschecking modeling representations to assess the impact of different models on each other to maintain system integrity. This paper presents details of the design process of a planetary rover in which diverse stakeholders deal with distinct aspects of the above space ground vehicle. It includes preliminary results from requirements analysis and system design that are used to interlink system models for an autonomous model crosschecking design process. The approach reduces collaborative design efforts by enabling multiple developers to minimize potential design inconsistencies by cross-relating their system models. Concluding remarks and future research are also presented. Full article

Autonomous acoustic sensing systems are increasingly investigated for unmanned aerial vehicle (UAV)-based surveillance and environmental monitoring applications due to their passive operation and relatively low computational requirements. However, the integration of acoustic classification and direction-of-arrival estimation on UAV-mounted microphone arrays remains challenging, particularly because realistic flight conditions introduce propulsion noise, aerodynamic flow, vibration, and complex acoustic interference. This paper presents a static ground validation of an AI-assisted acoustic target detection and azimuth estimation framework integrated on a flying-wing vertical take-off and landing (VTOL) UAV equipped with a distributed microphone array. The proposed system combines MFCC-based chainsaw sound classification using a Random Forest model with amplitude-based and SRP-PHAT-based azimuth estimation. Four HiFiBerry measurement microphones were mounted on a 4 m wingspan flying-wing VTOL UAV and connected to a Raspberry Pi 5 processing unit. Experimental validation was conducted under controlled indoor laboratory conditions using loudspeaker playback, with the UAV propulsion system inactive and only the acoustic acquisition and processing subsystem powered. The tests included single-source angular measurements, simultaneous multi-source acoustic scenarios, and source height variation. The SRP-PHAT method achieved a mean angular error of 3.55° in the single-source tests and 4.81° in the multiple-source tests, outperforming the amplitude-based baseline. The results support the feasibility of the proposed acoustic-processing framework under static ground conditions. However, because propulsion noise and in-flight aerodynamic effects were not included in the present validation, future work must address simulated propulsion noise injection, propulsion-on static testing, outdoor validation with real chainsaw sources, and eventual in-flight experiments. Because propulsion noise, aerodynamic flow, and in-flight vibration were not included in the present experimental campaign, the results should be interpreted as baseline static ground validation results rather than evidence of in-flight robustness. Full article

This paper addresses the problem of autonomous landing of a quadrotor unmanned aerial vehicle (UAV) on a moving ground vehicle subjected to unknown vertical oscillations generated by road irregularities. The proposed approach considers simultaneous longitudinal, lateral, heading, and altitude regulation in the presence of nonlinear coupled dynamics, aerodynamic effects, and platform motion disturbances. A nonlinear control architecture is developed by combining backstepping techniques, super-twisting sliding-mode control, and an adaptive internal model regulator. The longitudinal, lateral, and heading subsystems are stabilized through a block backstepping–sliding-mode framework, whereas the altitude subsystem is regulated using an adaptive internal model controller capable of compensating unknown multi-frequency oscillatory disturbances without prior knowledge of their amplitudes or frequencies. The complete UAV dynamics are derived from the Newton–Euler formulation, including aerodynamic forces, gyroscopic effects, and coupled translational–rotational dynamics. To improve robustness and avoid algebraic differentiation, exact first-order differentiators based on the super-twisting algorithm are incorporated into the control implementation. The proposed adaptive regulator is compared against a robust super-twisting sliding-mode altitude controller under low- and high-frequency oscillatory platform motions. Simulation results demonstrate that the adaptive internal model regulator achieves accurate trajectory tracking and consistently lower accumulated tracking errors than the robust super-twisting sliding-mode controller under both low- and high-frequency platform oscillations. These results highlight the suitability of adaptive output regulation techniques for autonomous UAV landing operations under oscillatory platform conditions with measurement noise. Full article

Hilly orchards are characterized by undulating terrain, canopy occlusion, irregular tree-row structures, and marked variations in ground adhesion conditions, which challenge the autonomous navigation of agricultural machinery by degrading perception and localization, increasing reference path uncertainty, and reducing closed-loop robustness. Perception and localization errors can propagate progressively along the chain of “sensor observation–vehicle pose estimation/environmental-structure perception–reference path generation–path-tracking controller inputs–vehicle closed-loop response,” ultimately affecting path-tracking accuracy, control smoothness, and operational safety. This review examines perception and localization error propagation and robust path-tracking control for agricultural machinery in hilly orchards. The review first summarizes the navigation roles and error characteristics of key observation sources and then analyzes how perception, localization, and reference path generation provide state variables, reference variables, and safety constraints to controllers. It subsequently compares typical path-tracking methods under multi-source disturbances and actuator constraints and discusses key challenges and an integrated robust design direction coupling perception and localization, reference path generation, and path-tracking control. The proposed integrated framework represents a synthesis-derived design direction rather than a complete architecture that has already been experimentally validated in hilly orchard field environments. Full article

In this article, machine learning (ML) is proposed as a solution to detect and classify false message injection attacks in autonomous ground navigation. First, multiple trajectories are designed and simulated to collect authentic feature samples offered by the odometry and inertial measurement unit (IMU) of an autonomous ground vehicle (UGV). Then, a dataset comprising these samples and other injected samples that simulate two cyberattacks, namely path modification (PM) and velocity drift (VD), is created to train, validate, and benchmark various ML classification models. These include decision tree (DT), k-nearest neighbors (KNN), multi-layer perceptron (MLP), random forest (RF), and support vector machine (SVM). The optimum classification model is experimentally evaluated using a UGV platform, and results suggest that the proposed solution allows the detection of authentic and attacked messages with more than 98% average accuracy and sub-millisecond prediction time. Thus, this solution is ideal for real-time classification, especially in fixed-route applications, e.g., public transportation. Full article

Hybrid environmental monitoring systems combining fixed and mobile sensing platforms are increasingly attracting attention for the characterization of complex outdoor environments. However, the practical integration of heterogeneous sensing infrastructures remains challenging because measurement consistency, sensor calibration, data fusion, and spatial reconstruction are often addressed separately rather than within a unified monitoring methodology. This work presents a hybrid environmental monitoring framework that integrates professional fixed monitoring stations with an autonomous ground vehicle equipped with low-cost environmental sensors. The proposed methodology combines reference-based calibration, temporal alignment, heterogeneous data fusion, and spatial interpolation to generate spatially consistent environmental information from complementary sensing platforms. The proposed methodology is experimentally validated through a monitoring campaign conducted within the IRCCS Policlinico San Martino hospital campus (Genoa, Italy), where repeated mobile measurements were integrated with two professional monitoring stations along a 350 m outdoor route characterized by heterogeneous environmental conditions. The calibration procedure significantly improved the agreement between fixed and mobile observations, while the comparative analysis of four interpolation techniques demonstrated that interpolation performance depends on the spatial distribution of measurements and the characteristics of the monitored environmental field rather than on the intrinsic superiority of a specific algorithm. The results further demonstrate the feasibility of integrating fixed and mobile sensing into a coherent and reproducible environmental monitoring workflow. Although validated in a hospital environment, the proposed methodology is applicable to other complex outdoor scenarios featuring distributed infrastructures and repeated operational routes, including industrial campuses, logistics hubs, freight terminals, airports, and port facilities. Full article

Airdrops with autonomous ram-air type parachutes are increasingly important in today’s unconventional warfare conjuncture and humanitarian aid operations. However, there are fundamental issues to be considered by operators or decision makers as to its utilization in the theatre. It is inevitable that new threats will arise with the increase in technology. Therefore, cyber defense elements for air supply should be secured for guided parachute systems that have the ability to glide through long distances. Some of these implied cyber-attacks could target sensitive information (identity, location, etc.) carried by guided parachutes, which are basically unmanned aerial vehicles, and deviate the system by taking over the routing control. The capture of flight information could lead to the disclosure of such covert operations, or at least lead to unexpected complications such as unauthorized airspace violations. Due to various adverse situations that may occur, ensuring the cybersecurity of the parachute payload system both in flight and on the ground has always been an important research topic. In this study, the concept of information replenishment with autonomous parachute systems is introduced to the literature and the cybersecurity of the system is detailed. Specifically, an efficient, lightweight, and pairing-free Elliptic Curve Cryptography (ECC)-based authentication scheme is proposed to secure the system. Considering the resource-constrained nature of autonomous parachute platforms, the proposed scheme is designed to ensure robust security with minimal computational and communication overheads. Furthermore, a security evaluation of the proposed scheme is performed. Although ECC-based authentication protocols have been widely investigated for UAV and IoT systems, this study is, to the best of our knowledge, the first to adapt a lightweight authentication framework to the cybersecurity requirements of autonomous ram-air parachute systems. Full article

To enhance unmanned ground vehicle (UGV) intelligence in smart cities, disaster rescue, and infrastructure inspection, this paper investigates the collaborative optimization of multimodal fusion end-to-end architectures. Through dynamic alignment of heterogeneous features, a multi-head distillation attention mechanism, and parallel decision-planning, a high-precision, low-latency closed-loop autonomous navigation framework is constructed. A Multi-Head Distillation Attention-based Trajectory Prediction (MDA-TP) method is proposed, combining a BEVFormer-based multimodal fusion perception model with a two-stage progressive knowledge distillation framework. On NuScenes, the method achieves an ADE of 0.88 m, an FDE of 1.32 m (4.34% and 10.81% reductions), and a collision rate of 15.2%, with 42.6 M parameters and 46 ms latency. Ablation shows removing attention distillation increases FDE by 13.6%. For system validation, a multi-sensor UGV platform is built. Through NuScenes online testing and real-world closed-loop validation, the Euclidean deviation remains within 0.5 m. Compared with traditional distillation, speed prediction MSE is reduced by 51.5%, wheel angle RMSE by 58.4%, and route completion improves from 60.99% to 97.26%. The results provide practical support for autonomous driving in smart cities, disaster rescue, and infrastructure inspection. Full article

The autonomous exploration capability of Autonomous Underwater Vehicles (AUVs) in complex underwater structures is fundamentally constrained by the coupling between sensing and motion execution. Unlike ground and aerial robots, limited underwater communication makes it difficult for AUVs to rely on external computing resources for perception and motion computation, imposing higher requirements on algorithmic efficiency. Meanwhile, motion constraints require arc-shaped course adjustments rather than in-place turns, thereby increasing navigation costs. This paper focuses on fixed-depth two-dimensional exploration represented by an occupancy grid and assumes deterministic sonar observations in order to isolate the effect of sensing–motion coupling. This sensing–motion coupling makes conventional frontier-based exploration strategies inefficient for underwater environments. To address this issue, this paper proposes a motion-aware autonomous exploration framework for AUVs that jointly considers information acquisition and motion cost during exploration decision-making and path execution. The proposed framework constructs adaptive local viewpoint sampling regions directly from the real-time sonar detection coverage and introduces a weighted sampling strategy to improve viewpoint generation efficiency in structured underwater environments. A motion-aware viewpoint utility function is further designed by integrating frontier information gain, path cost, and heading deviation, enabling the exploration strategy to favor viewpoints with lower execution cost. To balance exploration efficiency and coverage completeness, a local-priority and global-backtracking target assignment mechanism is developed. In addition, motion-constrained path execution is achieved through Douglas–Peucker keypoint extraction, cubic Bezier path smoothing, and dynamic window approach (DWA)-based local trajectory tracking. Experiments on a high-fidelity Unreal Engine–ROS simulation platform show consistent reductions in exploration path length and mapping time relative to a classical frontier-based baseline, with average improvements of about 11–15% in the tested scenarios. In corridor scenarios, the adaptive viewpoint generation strategy improves local viewpoint generation efficiency by 58.5% compared with a sliding-window RRT baseline. The results demonstrate that the proposed framework can effectively improve exploration efficiency and motion consistency for AUV autonomous exploration in complex underwater structures. Full article

Autonomous ground vehicles operating in steep-slope vineyards must reason jointly about three coupled factors: the order of waypoints across multiple polygon sub-fields, the points at which the route crosses sub-field boundaries, and terrain-aware travel cost in a Digital Elevation Model (DEM). Existing planners address one side at a time: Coverage Path Planning (CPP) libraries treat each field in isolation, vineyard-specific A* variants reason about terrain and rollover safety within one vineyard, and energy-aware planners optimise coverage within a single field. This paper presents a novel field-scale planner, which optimises a tour over the waypoints of all sub-fields with one Travelling Salesman Problem (TSP) whose travel costs come from three-dimensional polylines sampled from the DEM and a travel-cost model of rolling resistance, climbing work, and a differential-drive pivot-turn term. Transitions between sub-fields are routed through chains of adjacent sub-fields. Four strategies are evaluated: a greedy nearest-neighbour (NN) heuristic and three Simulated Annealing (SA) TSP variants minimising distance (SA-D), energy (SA-E), or time (SA-T). Evaluated on two Moselle sites in Luxembourg, the objective-matched variants reduce mission cost over the nearest-neighbour baseline by up to 21%, on average, and 40% on the best mission, within an integrated pipeline from public geodata to executable, terrain-aware routes. Full article

Despite the rapid expansion of unmanned aerial vehicle (UAV) applications across precision agriculture, logistics, infrastructure inspection, disaster response, and aerial surveying, objective and quantitative hardware evaluation criteria for UAV components remain insufficiently developed. This paper proposes quantitative key performance indicators (KPIs) for thirteen core hardware subsystems, including airframe and propulsion, battery and power supply, flight control, wireless communication, imaging (camera), Global Positioning System (GPS)/Global Navigation Satellite System (GNSS) positioning, thermal management, acoustic and vibration characteristics, AI-based autonomous flight, electromagnetic compatibility (EMC), cybersecurity, and reliability and environmental qualification, together with LiDAR payload evaluation criteria. International standardization activities by 3GPP (Release 15/17), IEEE (1936–1958 series), American society for photogrammetry and remote sensing (ASPRS), and national regulatory frameworks are synthesized to define measurable performance metrics and recommended test methods for each subsystem. An integrated KPI matrix maps application-domain-specific performance targets—encompassing surveying (real-time kinematic (RTK) horizontal accuracy ≤ 2 cm root-mean-square error (RMSE), ground sample distance (GSD) ≤ 2 cm/px), infrastructure inspection (LiDAR payload up to 8 kg, beyond visual line-of-sight (BVLOS) latency ≤ 140 ms), and logistics delivery (payload ≥ 2 kg, precision landing ≤ 50 cm)—demonstrating that no universal platform can simultaneously satisfy all domain requirements. A fuzzy-AHP weighting procedure and inter-subsystem coupling analysis are introduced to address size, weight, and power (SWaP) trade-off relationships that purely additive scoring models cannot capture. The proposed evaluation framework is intended to contribute practically to UAV standardization, certification, and quality management across the full design–procurement–operation lifecycle. Full article

Autonomous vehicle (AV) technologies have advanced rapidly in recent years, driving an increasing demand for large-scale, high-quality annotated data. However, collecting and annotating real-world pedestrian video datasets is time-consuming, costly, and often insufficient to cover rare and safety-critical scenarios. Recent world foundation models have demonstrated impressive capabilities in generating realistic videos, yet their suitability for safety-critical autonomous driving applications remains largely unexplored. In this work, we investigate whether current world foundation models can generate driving scenarios that are sufficiently realistic and behaviourally consistent for autonomous driving research. We conduct a case study centred on pedestrian–vehicle interactions captured from ego-vehicle dashcam viewpoints, where subtle behavioural and geometric errors can have significant safety implications. To support this investigation, we develop SynPeDAS, an open research framework comprising a collection of synthetic pedestrian-interaction videos, a reusable generation pipeline for transforming real-world driving footage into synthetic scenarios, an automated evaluation suite, and downstream demonstration code. Through quantitative evaluation and structured human assessment, we identify several recurring failure modes, including dynamic misalignment, depth drift, and object persistence inconsistencies. More importantly, we find that commonly used evaluation metrics frequently exhibit ceiling effects and weak alignment with human judgement, limiting their ability to detect safety-critical behavioural errors. These findings indicate that, despite high perceptual realism at the frame level, current generative world models and existing evaluation methodologies remain insufficient for capturing physically grounded motion and task-critical semantics. Consequently, significant challenges remain before world model-generated videos can be considered reliable for safety-critical autonomous driving applications. SynPeDAS provides an open platform for systematically studying these challenges and developing improved generation and evaluation methods. Full article