Search Results (160)

Unmanned Aerial Systems (UASs) are playing an increasingly critical role in both civilian and defense applications. However, their heavy reliance on unencrypted Global Navigation Satellite System (GNSS) signals, particularly GPS, makes them highly susceptible to signal spoofing attacks, posing severe operational and safety threats. This paper introduces a comprehensive, AI-driven multi-layer sensor framework that simultaneously enables real-time spoofing detection and secure command-and-control (C2) communication in lightweight UAS platforms. The proposed system enhances telemetry reliability through a refined preprocessing pipeline that includes a novel GPS Drift Index (GDI), robust statistical normalization, cluster-constrained oversampling, Kalman-based noise reduction, and quaternion filtering. These sensing layers improve anomaly separability under adversarial signal manipulation. On this enhanced feature space, a differentiable architecture search (DARTS) approach dynamically generates lightweight neural network architectures optimized for fast, onboard spoofing detection. For secure command and control, the framework integrates a low-latency cryptographic layer utilizing PRESENT-128 encryption and CMAC authentication, achieving confidentiality and integrity with only 1.79 ms latency and a 0.51 mJ energy cost. Extensive experimental evaluations demonstrate the framework’s outstanding detection accuracy (99.99%), near-perfect F1-score (0.999), and AUC (0.9999), validating its suitability for deployment in real-world, resource-constrained UAS environments. This research advances the field of AI-enabled sensor systems by offering a robust, scalable, and secure navigation framework for countering GPS spoofing in autonomous aerial vehicles. Full article

The integration of reconfigurable intelligent surfaces (RISs) with flying ad hoc networks (FANETs) offers new opportunities to enhance performance in aerial communications. This paper proposes a novel FANET architecture in which each unmanned aerial vehicle (UAV) or drone is equipped with an RIS comprising M passive elements, enabling dynamic manipulation of the wireless propagation environment. We address the joint power allocation and RIS configuration problem to maximize the sum spectral efficiency, subject to constraints on maximum transmit power and unit-modulus phase shifts. The formulated optimization problem is non-convex due to coupled variables and interference. We develop an alternating optimization-based joint power and phase shift (AO-JPPS) algorithm that decomposes the problem into two subproblems: power allocation via successive convex approximation and phase optimization via Riemannian manifold optimization. A key contribution is addressing the RIS coupling effect, where the configuration of each RIS simultaneously influences multiple communication links. Complexity analysis reveals polynomial-time scalability, while derived performance bounds provide theoretical insights. Numerical simulations demonstrate that our approach achieves significant spectral efficiency gains over conventional FANETs, establishing the effectiveness of RIS-assisted drone networks for future wireless applications. Full article

This paper presents Aerokinesis, an IoT-based software–hardware system for intuitive gesture-driven control of quadcopter unmanned aerial vehicles (UAVs), developed within the Robot Operating System 2 (ROS2) framework. The proposed system addresses the challenge of providing an accessible human–drone interaction interface for operators in scenarios where traditional remote controllers are impractical or unavailable. The architecture comprises two hierarchical control levels: (1) high-level discrete command control utilizing a fully connected neural network classifier for static gesture recognition, and (2) low-level continuous flight control based on three-dimensional hand keypoint analysis from a depth camera. The gesture classification module achieves an accuracy exceeding 99% using a multi-layer perceptron trained on MediaPipe-extracted hand landmarks. For continuous control, we propose a novel approach that computes Euler angles (roll, pitch, yaw) and throttle from 3D hand pose estimation, enabling intuitive four-degree-of-freedom quadcopter manipulation. A hybrid signal filtering pipeline ensures robust control signal generation while maintaining real-time responsiveness. Comparative user studies demonstrate that gesture-based control reduces task completion time by 52.6% for beginners compared to conventional remote controllers. The results confirm the viability of vision-based gesture interfaces for IoT-enabled UAV applications. Full article

Agricultural robotics and artificial intelligence (AI) are becoming essential to building more sustainable, efficient, and resilient food systems. As climate change, food security pressures, and labour shortages intensify, the integration of intelligent technologies in agriculture has gained strategic importance. This systematic review provides a consolidated assessment of AI and robotics research in agriculture from 2000 to 2025, identifying major trends, methodological trajectories, and underexplored domains. A structured search was conducted in the Scopus database—which was selected for its broad coverage of engineering, computer science, and agricultural technology—and records were screened using predefined inclusion and exclusion criteria across title, abstract, keywords, and eligibility levels. The final dataset was analysed through descriptive statistics and science-mapping techniques (VOSviewer, SciMAT). Out of 4894 retrieved records, 3673 studies met the eligibility criteria and were included. As with all bibliometric reviews, the synthesis reflects the scope of indexed publications and available metadata, and potential selection bias was mitigated through a multi-stage screening workflow. The analysis revealed four dominant research themes: deep-learning-based perception, UAV-enabled remote sensing, data-driven decision systems, and precision agriculture. Several strategically relevant but underdeveloped areas also emerged, including soft manipulation, multimodal sensing, sim-to-real transfer, and adaptive autonomy. Geographical patterns highlight a strong concentration of research in China and India, reflecting agricultural scale and investment dynamics. Overall, the field appears technologically mature in perception and aerial sensing but remains limited in physical interaction, uncertainty-aware control, and long-term autonomous operation. These gaps indicate concrete opportunities for advancing next-generation AI-driven robotic systems in agriculture. Funding sources are reported in the full manuscript. Full article

Shipborne UAV-assisted dock is an important way to recover unmanned systems for remote water surface low-altitude detection. The lack of resisting deck disturbances capability for UAV autonomous landing in dynamic dock stations has led to the inability of traditional hovering recovery methods for single UAV guidance and flight attitude control systems to meet the growing demand for landing assistance. In this work, we present a shipborne manipulator arm designed to grasp drones that use low-altitude visual servo technology for landing on the water surface. The shipborne manipulator arm is fabricated as a key component of a seaplane drone dock comprising a ship-type embedded drone storage, a packaged helistop for power transfer and UAV recovery, and a multi-degree-of-freedom arm integrated with multi-source information sensors for the treatment of air-to-water-related airplane crashes. Dynamic model tests have demonstrated that the end-effector of the shipborne manipulator arm stabilizes and performs optimally for water surface disturbances. A down-to-top grasp docking paradigm for a UAV-assisted perching on a shipborne helistop that enables the charging components of the station system to be equipped automatically to ensure that the drone performs its mission in the best condition is also presented. The surface grasp experiments have verified the efficacy of this grasp paradigm when compared to the traditional autonomous landing method. Full article

This work investigates a quadrotor equipped with dual-stage robotic arms and a cable-suspended payload, developing a unified methodology for modeling and control. A 10-DOF Lagrangian model captures vehicle-arm-payload coupling through structured mass matrices. A hierarchical control architecture combines SO(3)-based attitude regulation with cooperative swing compensation via partial feedback linearization, exploiting coupling matrices to distribute control between platform and arm actuators. Model accuracy is enhanced through physics-informed system identification, achieving improved prediction correlation with bounded corrections. Lyapunov analysis establishes semi-global practical stability with explicit robustness bounds. High-fidelity simulations in MuJoCo demonstrate a 40–70% swing reduction compared to PD control across multiple scenarios, with low computational overhead at kHz-level control rates, making it suitable for embedded implementation. The framework provides a theoretical foundation and implementation guidelines for cooperative aerial manipulation systems. Full article

Rapid and precise intervention in disaster and medical-aid scenarios demands aerial platforms that can both survey and physically interact with their environment. This study presents the design, fabrication, modeling, and experimental validation of a one-piece, 3D-printed quadcopter with an integrated six-degree-of-freedom aerial manipulator robotic arm tailored for emergency response. First, we introduce an ‘X’-configured multi-rotor frame printed in PLA+ and optimized via variable infill densities and lattice cutouts to achieve a high strength-to-weight ratio and monolithic structural integrity. The robotic arm, driven by high-torque servos and controlled through an Arduino-Pixhawk interface, enables precise grasping and release of payloads up to 500 g. Next, we derive a comprehensive nonlinear dynamic model and implement an Extended Kalman Filter-based sensor-fusion scheme that merges Inertial Measurement Unit, barometer, magnetometer, and Global Positioning System data to ensure robust state estimation under real-world disturbances. Control algorithms, including PID loops for attitude control and admittance control for compliant arm interaction, were tuned through hardware-in-the-loop simulations. Finally, we conducted a battery of outdoor flight tests across spatially distributed way-points at varying altitudes and times of day, followed by a proof-of-concept medical-kit delivery. The system consistently maintained position accuracy within 0.2 m, achieved stable flight for 15 min under 5 m/s wind gusts, and executed payload pick-and-place with a 98% success rate. Our results demonstrate that integrating a lightweight, monolithic frame with advanced sensor fusion and control enables reliable, mission-capable aerial manipulation. This platform offers a scalable blueprint for next-generation emergency drones, bridging the gap between remote sensing and direct physical intervention. Full article

Accurate estimation of individual tree aboveground biomass (AGB) is essential for understanding forest carbon dynamics, optimizing resource management, and addressing climate change. Conventional methods rely on destructive sampling, whereas unmanned aerial vehicle (UAV) remote sensing provides a non-destructive alternative. In this study, spectral indices, textural features, and canopy height attributes were extracted from high-resolution UAV optical imagery and Light Detection And Ranging (LiDAR) point clouds. We developed an improved YOLOv8 model (NB-YOLOv8), incorporating Neural Architecture Manipulation (NAM) attention and a Bidirectional Feature Pyramid Network (BiFPN), for individual tree detection. Combined with a random forest algorithm, this hybrid framework enabled accurate biomass estimation of Chinese fir, Chinese pine, and larch plantations. NB-YOLOv8 achieved superior detection performance, with 92.3% precision and 90.6% recall, outperforming the original YOLOv8 by 4.8% and 4.2%, and the watershed algorithm by 12.4% and 11.7%, respectively. The integrated model produced reliable tree-level AGB predictions (R² = 0.65–0.76). SHapley Additive exPlanation (SHAP) analysis further revealed that local feature contributions often diverged from global rankings, underscoring the importance of interpretable modeling. These results demonstrate the effectiveness of combining deep learning and machine learning for tree-level AGB estimation, and highlight the potential of multi-source UAV remote sensing to support large-scale, fine-resolution forest carbon monitoring and management. Full article

Unmanned Aerial Vehicles (UAVs) are increasingly being used in critical infrastructure, defense, and civilian applications, and face new cybersecurity threats. In this work, we present a novel hybrid deep learning architecture that combines Mamba, Kolmogorov-Arnold Networks (KAN), and Liquid Neural Networks for real-time cyberattack detection in UAV systems. The proposed Mamba-KAN-Liquid (MKL) model integrates Mamba’s selective state-space mechanism for temporal dependency modeling, KAN’s learnable activation functions for feature representation, and Liquid networks’ dynamic adaptation capabilities for real-time anomaly detection. Extensive evaluations on CIC-IDS2017, CSE-CIC-IDS2018, and synthetic UAV telemetry datasets demonstrate that our model achieves detection rates exceeding 95% across six different attack scenarios, including GPS spoofing (97.3%), network jamming (95.8%), man-in-the-middle attacks (96.2%), sensor manipulation (94.7%), DDoS (98.1%), and zero-day attacks (89.4%). The model meets real-time processing requirements with an average inference time of 47.3 ms for a sample batch size of 32, making it suitable for practical deployment on resource-constrained UAV platforms. Full article

In this research, two inflatable-winged Unmanned Aerial Vehicles (UAVs) in distinct configurations, a single-fuselage layout with external trailing-edge control surfaces and a twin-fuselage layout with fully movable control surfaces were designed, developed, and flight tested to investigate the flight characteristics of inflatable-winged aircraft. Initially, inflatable wings were designed and fabricated from various materials, followed by rigorous ground testing, including structural characteristics tests, pressure retention and resistance tests, and low-speed wind-tunnel evaluations. Following this, two methods for controlling the inflatable wings were proposed, and their roll control effectiveness was thoroughly investigated. Subsequently, two inflatable-winged UAV prototypes, each employing a different configuration and manipulation method, were designed, assembled, and subjected to basic low-altitude flight tests to assess the feasibility of their aerodynamic layouts and control characteristics. The results demonstrated that a segmented wing design with a multi-boom configuration is particularly well-suited for inflatable wings. Additionally, both proposed control methods were tested and shown to be effective in flight. The findings provide valuable insights into the properties of inflatable wings and offer substantial guidance for the development of inflatable-winged aircraft. Full article

As a pioneering solution for precision agriculture, unmanned, robotics-centred greenhouse farms have become a key technological pathway for intelligent upgrades. The robotic arm is the core unit responsible for achieving full automation, and the level of technological development of this unit directly affects the productivity and intelligence of these farms. This review aims to systematically analyze the current applications, challenges, and future trends of robotic arms and their key technologies within unmanned greenhouse. The paper systematically classifies and compares the common types of robotic arms and their mobile platforms used in greenhouses. It provides an in-depth exploration of the core technologies that support efficient manipulator operation, focusing on the design evolution of end-effectors and the perception algorithms for plants and fruit. Furthermore, it elaborates on the framework for integrating individual robots into collaborative systems analyzing typical application cases in areas such as plant protection and fruit and vegetable harvesting. The review concludes that greenhouse robotic arm technology is undergoing a profound transformation evolving from single-function automation towards system-level intelligent integration. Finally, it discusses the future development directions highlighting the importance of multi-robot systems, swarm intelligence, and air-ground collaborative frameworks incorporating unmanned aerial vehicles (UAVs) in overcoming current limitations and achieving fully autonomous greenhouses. Full article

Insulators on power lines require regular maintenance by operators in high-altitude hazardous environments, and the emergence of aerial manipulators provides an efficient and safe support for this scenario. In this study, a lightweight telescopic aerial manipulator system is developed, which can realize the installation and retrieval of insulator inspection robots on power lines. The aerial manipulator has three degrees of freedom, including two telescopic scissor mechanisms and one pitch rotation mechanism. Multiple types of cameras and sensors are specifically configured in the structure, and the total mass of the structure is 2.2 kg. Next, the kinematic model, dynamic model, and instantaneous contact force model of the designed aerial manipulator are derived. Then, the hybrid position/force control strategy of the aerial manipulator and the visual detection and estimation algorithm are designed to complete the operation or complete the task. Finally, the lifting external load test, grasp and installation operation test, as well as outdoor flight operation test are carried out. The test results not only quantitatively evaluate the effectiveness of the structural design and control design of the system but also verify that the aerial manipulator can complete the accurate automatic grasp and installation operation of the 3.6 kg target device in outdoor flight. Full article

In this paper, we present a VR-based control framework for multi-UAV (rotorcraft-type) aerial manipulation that enables simultaneous control of each UAV and its onboard five-degree-of-freedom (5-DoF) manipulator using virtual-reality controllers. Instead of relying on dense button mappings or predefined gestures, the framework maps natural VR-controller motions in real time to vehicle pose and arm joint commands. The UAVs respond smoothly to translational and rotational inputs, while the manipulators accurately replicate dexterous hand motions for precise grasping. Beyond single-platform operation, we extend the framework to cooperative dual-UAV manipulation, leveraging two-hand poses captured via VR controllers to coordinate two UAV-arm systems for payload transportation and obstacle traversal. Simulation experiments demonstrate accurate trajectory tracking and the potential for successful cooperative transport in cluttered environments, indicating the framework’s suitability for telemanipulation, search-and-rescue, and industrial tasks. Full article

The operational performance of robotic arms for multi-rotor flying robots (MFRs) has attracted growing attention in recent years. To explore new possibilities for aerial manipulation, this study investigates a novel parallel mechanism, the TriVariant, comprising one UP limb and two identical UPS limbs (2-UPS&UP). To evaluate its potential, we analyze its dimensional and kinematic characteristics and benchmark them against the widely adopted Delta robot, which is commonly integrated with unmanned aerial vehicles (UAVs). A prototype of the TriVariant is fabricated for experimental validation. Both analytical and experimental results reveal that, within a cylindrical task workspace characterized by a large diameter and moderate height, the TriVariant offers a more compact structure than the Delta robot, despite its slightly reduced dexterity. These findings highlight that the TriVariant is especially suitable for aerial manipulation in space-constrained environments where all limbs must be mounted beneath the UAV. Full article

This paper presents an adaptive backstepping feedback control design for an unmanned aerial manipulator (UAM) that consists of an unmanned aerial vehicle (UAV) with an attached robotic arm. The effect of the arm is treated as a disturbance force and torque, as well as a parametric uncertainty in inertial parameters. The proposed adaptive law guarantees disturbance rejection assuming constant parameters and disturbances. In practice, this assumption includes the case of fixed-arm configurations. To validate the control design, numerical simulations are performed, including a realistic pick-and-place scenario. Full article