Search Results (4,377)

Relative satellite navigation is critical for formation flying, rendezvous, and docking. This study augments a vision-based relative navigation framework with a robust multiplicative extended Kalman filter (RMEKF) that adaptively scales the measurement covariance using innovation-based covariance matching and a chi-square fault-detection test. A two-spacecraft scenario is simulated in which a deputy monocular camera observes six active beacons on a chief spacecraft. To evaluate fault tolerance, constant line-of-sight (LOS) errors are injected on two beacon measurements during a fixed interval. Over the fault-centered evaluation window, the RMEKF reduces attitude root mean square error (RMSE) by approximately 71–73% compared to the conventional multiplicative extended Kalman filter (MEKF), while also improving relative/orbital state accuracy by 19–93%. These results indicate improved robustness to LOS measurement faults without degrading overall estimation stability. Full article

The simulation accuracy and error sources of Remote Sensing (RS)-derived products, model-derived products, and RS-based assimilation products remain poorly understood across varying terrain conditions. Here, we investigated watershed-scale Soil Moisture (SM) dynamics across different slope positions using RS data assimilation, with the targeted area located in New South Wales, Australia. After evaluating and comparing the accuracy of existing SM products, a daily 1 km-resolution surface SM dataset was generated through data fusion. This product was then integrated with Soil and Water Assessment Tool (SWAT) model simulations using a Kalman filter approach, yielding a 10 m-resolution dataset with enhanced physical mechanism. Our results revealed that physically constrained products generally outperformed standalone RS inversions or hydrological model simulations, with their performance varied across slope positions. Furthermore, we demonstrated that high Soil Moisture Content (SMC) and spatial heterogeneity amplified SWAT model dominance in assimilated outcomes, whereas low SMC and spatial heterogeneity elevated RS contributions; the assimilated dataset consistently overcame limitations of standalone RS and hydrological model simulations across all slope positions. Our results demonstrated significant variations in the accuracy of RS-derived and model-derived products across distinct slope positions. This study systematically analyzed the underlying error mechanisms, contributing to intelligent water resource monitoring and water management decisions. Full article

Accurate fruit localization and efficient harvesting are key challenges for agricultural robots, especially in dynamic orchard environments, where platform vibration, fruit occlusion, and computational resource limitations of embedded devices significantly impact system performance. To address these issues, this paper proposes a lightweight “fruit detection + harvesting” framework. First, by integrating MobileNetV4 and Triplet Attention mechanisms, an improved YOLOv8n network is designed, with the improved YOLOv8n Precision reaching 98.148% and FPS reaching 30 FPS on Jetson Nano, achieving a good balance between detection accuracy and computational efficiency suitable for edge deployment. Second, a strawberry three-dimensional coordinate reconstruction method based on weighted 3D centroid reconstruction is proposed, utilizing depth bias adjustment coefficients to improve spatial accuracy. Third, to address localization errors caused by vibration and platform motion, a dynamic compensation and temporal fusion strategy based on an Inertial Measurement Unit (IMU) is proposed. The rotation matrix estimated from IMU data is first used to correct camera pose variations. Then, an adaptive sliding window is employed to smooth the coordinate sequence. Finally, an Extended Kalman Filter (EKF) is applied to further refine the fused results by incorporating temporal dynamics, ensuring that the reconstructed three-dimensional coordinates in the robotic arm reference frame achieve higher stability and continuity. Experimental results in orchard scenarios show that compared with traditional methods, the system has higher localization accuracy, stronger robustness to dynamic disturbances, and higher harvesting efficiency. This work provides a practical and deployable solution for advancing intelligent fruit-harvesting robots. Full article

Multi-object tracking (MOT) is a key technology for enabling autonomous navigation of unmanned surface vehicle (USV) as it provides continuous perception of surrounding maritime targets and supports navigation decision-making. However, videos acquired on maritime platforms typically suffer from challenges such as platform-induced jitter and nonlinear object motion, which significantly degrade tracking performance. To address these challenges, this paper builds upon ByteTrack by incorporating an adaptive Kalman filtering scheme and proposing a density-aware association strategy, resulting in a novel tracker termed the Marine-Aware Adaptive Tracker (MAAT). Specifically, an adaptive Kalman filter is introduced to increase the contribution of high-confidence detections during the state update process, thereby enhancing the stability and robustness of state estimation. Furthermore, to better mitigate the frequent identity switches caused by severe platform jitter from the USV observation platform, a density-aware association strategy is proposed. This strategy dynamically adjusts the composition of the cost matrix according to the density of high-confidence targets, enabling more reliable data association under varying scene conditions. Finally, the proposed tracking algorithm is evaluated against several state-of-the-art methods on the Singapore Maritime Dataset. It achieves competitive performance, attaining 44.37 MOTA and 43.857 IDF1. Moreover, MAAT operates in real time, running at 41.4 FPS. The experimental results demonstrate that MAAT is capable of performing accurate and real-time multi-object tracking in dynamic maritime environments with surface fluctuations, thereby providing effective technical support for intelligent maritime surveillance applications. Full article

This paper proposes a robust navigation method based on a robust square-root cubature Kalman filter (RSRCKF) to address the accuracy divergence of integrated navigation systems caused by drilling-induced slippage and the mismatch between the tail-cable encoder and the robot motion during operations of a seafloor drilling robot in deep-sea soft sedimentary layers. Considering the large-deformation mechanical characteristics of the seabed under drilling conditions, a unified state-space model incorporating a time-varying odometer scale-factor error is first established. To alleviate the numerical instability of the nonlinear system in the presence of non-Gaussian noise, a square-root cubature Kalman filter (SRCKF) framework is employed, in which the positive definiteness of the error covariance matrix is dynamically preserved via QR decomposition. Subsequently, an online fault detection mechanism based on a modified chi-square test is developed. By introducing a two-segment IGG (a classical robust weighting scheme) weighting function, an adaptive variance inflation factor is constructed to enable real-time identification and down-weighting of abnormal observations induced by slippage. Field experiments, including drilling and turning tests conducted on tidal mudflats off the coast of Zhoushan, demonstrate that the proposed method can effectively mitigate the impact of “false displacement” disturbances caused by typical soft clay slippage conditions through enhanced statistical robustness. Taking the conventional SINS/OD integration scheme as the baseline, the proposed method achieves an approximate 82.4% reduction in positioning error. These results verify the robustness and engineering applicability of the proposed algorithm in complex seabed environments. Full article

This paper introduces the concept of the Point of No Return as a physically grounded safety indicator for rear-end vehicle conflicts, addressing fundamental limitations of the widely used time-to-collision metric. Unlike purely kinematic approaches, the proposed formulation incorporates braking capability and reaction constraints, enabling a direct assessment of whether a collision can still be avoided. To illustrate the applicability of the concept, a vision-based framework using a single camera is developed based on dashcam data, combining YOLO-based object detection, Kalman-filter tracking, and geometric distance estimation derived from bounding-box features and camera projection models. The estimated distance is further processed to obtain relative motion, allowing a unified analysis of time to collision and the Point of No Return within the same evaluation pipeline. Experimental results on real-world driving sequences show that the Point of No Return consistently precedes critical conditions identified by time to collision and provides a more stable and physically interpretable characterization of the transition toward collision inevitability. The results also highlight the sensitivity of the proposed indicator to braking capability, while showing lower sensitivity to variations in relative speed. Overall, this study demonstrates the relevance of the Point of No Return as a complementary indicator for collision risk assessment, offering a physically meaningful basis for decision-making in driver assistance systems and improving the interpretation of critical traffic situations. The proposed approach supports sustainable urban mobility by enabling earlier and more reliable intervention strategies, contributing to improved traffic safety, smoother traffic flow, and reduced environmental impact. Full article

To solve the problems of rotor position estimation error caused by the installation deviation of Hall sensors and the increase in yarn amount detection error in complex environments, resulting in speed fluctuations and unstable yarn feeding in the traditional permanent magnet synchronous motor (PMSM) drive system for yarn feeder, a control method for yarn amount in yarn feeder PMSMs based on an improved dual second-order generalized integrator (DSOGI) and Kalman filter is proposed. Firstly, in order to reduce the influence of installation deviation of Hall sensors, the three-phase Hall signals are converted into two-phase orthogonal Hall vector signals. An improved DSOGI is used to filter out high-order harmonic components and specific harmonic components in the Hall vector signals, and a cross-coupled structure is constructed to further enhance the fundamental component and suppress high-order harmonic components of negative coefficients. Then, accurate motor rotor position information is extracted by a quadrature phase-locked loop; secondly, in order to obtain accurate information on yarn amount, a system state model based on yarn amount and its rate of change is established, and Kalman filtering is used for optimal estimation of the yarn amount; finally, the above methods are integrated into the PMSM control system of the yarn feeder. Experimental results show that, compared with traditional methods, the PMSM control system of the yarn feeder using the method proposed in this paper has a shorter startup time and smaller steady-state error in motor speed and yarn amount when conveying yarn at a constant speed; when transporting yarn at variable speed, the motor speed and yarn amount settling time are shorter, and the peak deviation is smaller. Full article

In active debris removal missions in low Earth orbit, the semi-major axis difference between a service platform and its target can be large. Analytical relative dynamics models used in formation-flying applications typically retain only the first-order expansion in the orbital element differences; at large separations, the discarded higher-order terms—particularly the power-law dependence on the semi-major axis—introduce systematic along-track drift that degrades the propagation accuracy. This paper derives the power-law truncation correction, a closed-form additive vector that exactly compensates the truncated semi-major-axis power-law remainder, together with a consistent Jacobian correction for the extended Kalman filter covariance prediction. The state dimension and state transition matrix structure remain unchanged. Propagation tests over semi-major axis differences of 36–146 km yield ten-revolution terminal position errors of 0.008–0.065 km for the corrected models, compared with tens to hundreds of kilometers for the uncorrected first-order models and 0.1–8 km for the second-order state transition tensor. In 500-run Monte Carlo angles-only filtering experiments, the corrected filter reduces the median terminal position error by one to nearly three orders of magnitude relative to the uncorrected model. A process noise sensitivity study confirms robustness to calibration uncertainty across two orders of magnitude at a lower computational cost and with simpler implementation than higher-order tensor methods. Full article

Advancing environmental monitoring through coordinated autonomous systems is central to sustainable smart region governance and data-driven territorial management. The article presents an engineering-oriented architecture and deployment methodology for an integrated wildfire monitoring and response system that combines unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs), and stationary sensor networks (SNs). We formalise hub-and-spoke infrastructure placement as a mixed-integer optimisation problem that accounts for platform types, endurance, travel times and logistical constraints, and propose a practical pre-processing pipeline (confidence scoring, resampling, Kalman/median filtering, strategy fusion) for heterogeneous telemetry and imagery. The system couples multimodal neural network processing (image backbones, clustering and time-series models) with online resource-allocation and mission-planning mechanisms to prioritise UAV/UGV sorties and dynamically select launch sites. The article describes scenario-driven operational modes (early warning, alarm verification, autonomous local extinguishing, post-fire recovery, sensor-gap compensation, and inter-hub reinforcement), defines validation protocols (synthetic experiments, precision/recall/F1, and hardware-in-the-loop testing), and proposes KPIs to assess environmental, social, and economic impacts for smart regions. The contribution is a reproducible, deployment-focused blueprint that bridges conceptual UAV–UGV–SN research and practical implementation, highlighting trade-offs in reliability, communication redundancy, and sustainability, and outlining directions for simulation, field pilots and algorithmic refinement. Full article

Offshore wind energy has developed rapidly in recent years as a crucial component of renewable energy. However, offshore wind turbines (OWTs) face significant challenges in operations under complex marine environmental conditions, such as multimodal nonlinear vibrations, reliable structural monitoring, efficient maintenance, and sustainable long-term operations. The model-updating-based parameter identification takes advantages of structural vibration measurements, assisting in structural health monitoring. However, the traditional methods have not fully accounted for the parameter uncertainties and the need for real-time state updating, making them insufficient to meet the long-term online monitoring requirements for OWTs. This study introduces an innovative structural parameter identification framework that integrates modal parameter identification with Bayesian recursive updating. The proposed framework enables more efficient updates and uncertainty quantification of critical physical parameters for OWTs. It combines the covariance-driven stochastic subspace identification (COV-SSI) method for automatic modal parameter identification with the unscented Kalman filter (UKF) for parameter estimation. A 10 MW jacket-type offshore wind turbine was used as a case study. First, the numerical simulations were conducted to generate synthetic measurements for method validation and demonstration, enabling stepwise updating of the tower material’s elastic modulus across different sea conditions. A comparison of update speed and the convergence rate with the traditional time-step-based UKF method demonstrated the superiority of the proposed sea-condition-based approach in terms of computational efficiency and stability. Finally, the proposed framework was systematically validated using scaled model experimental data of a jacket-type OWT with a 4.2% identification error, confirming its engineering applicability. This research provides reliable technical support for the safety assessment of offshore wind turbine structures. Full article

The zinc–iron redox battery (ZIRB) has become one of the hot technologies of electrochemical energy storage due to its safety, stability and low cost of the electrolyte. In this paper, a collaborative control strategy for an MW-level zinc–iron flow battery energy storage system is studied, and the operation control and management of the MW-level zinc–iron flow battery energy storage system are coordinated and optimized to improve the operation efficiency of the whole system. The model of the megawatt zinc–iron flow battery energy storage system is established in this paper. A ZIRB state of charge (SOC) estimation method based on least squares (LS) and an extended Kalman filter (EKF) is proposed. Experiments under constant-current discharge show that the proposed LS-EKF method can achieve accurate SOC estimation for the tested ZIRB system, with a maximum estimation error of approximately 2.3%. Experiments show that the proposed algorithm has good accuracy, rapidity and robustness at different SOC initial values. According to SOC differences between battery cells, the coordination strategy of each cell is designed to meet the requirements of frequency modulation while taking into account the safety of battery operation. On this basis, the optimization problem is designed and solved with the goal of optimal frequency modulation effect and battery energy loss, and the collaborative control of the MW-level ZIRB energy storage system is realized. Full article

This paper presents an edge-deployable vision-based framework for human–robot interaction using a xArm collaborative robot and a single RGB camera mounted on the robot wrist, and lightweight AI-based perception modules. The system enables intuitive, contact-free control by combining hand understanding and object detection within a unified perception–decision–control pipeline. Hand landmarks are extracted using MediaPipe Hands, from which continuous hand trajectories, static gestures, and dynamic gestures are derived. Task objects are detected using a YOLO-based model, and both hand and object observations are mapped into the robot workspace using ArUco-based planar calibration. To ensure stable robot motion, the hand control signal is smoothed using low-pass and Kalman filtering, while dynamic gestures such as waving are recognized using a lightweight LSTM classifier. The complete pipeline runs locally on edge hardware, specifically NVIDIA Jetson Orin Nano and Raspberry Pi 5 with a Hailo AI accelerator. Experimental evaluation includes trajectory stability, gesture recognition reliability, and runtime performance on both platforms. Results show that filtering significantly reduces hand-tracking jitter, gesture recognition provides stable command states for control, and both edge devices support real-time operation, with Jetson achieving consistently lower runtime than Raspberry Pi. The proposed system demonstrates the feasibility of low-cost edge AI solutions for responsive and practical human–robot interaction in collaborative industrial environments. Full article

Offshore wind turbine operation and maintenance in complex sea states is influenced by the coupled effects of low-frequency vessel drift and high-frequency wave-induced disturbances. In practical operations, the ship dynamic positioning system primarily regulates low-frequency motion through vessel position control, whereas a boarding compensation system is required to attenuate high-frequency six-degrees-of-freedom motions to ensure safe personnel transfer. This study establishes coupled kinematic mapping among the ship dynamic positioning system, the Stewart platform, and a three-degrees-of-freedom gangway and proposes a hierarchical cooperative control architecture. At the upper layer, an extended Kalman filter and an exponential moving average low-pass filter are employed for online state estimation and for separating low-frequency and high-frequency components. A Kalman filter lookahead predictor is then used to generate a short-horizon prediction of the high-frequency component and to construct a feedforward reference signal. At the middle layer, the feedforward reference and the gangway end error feedback are coordinated at the velocity level, and a quadratic programming-based allocation strategy distributes compensation tasks between the Stewart platform and the gangway under safety-related constraints, including actuator stroke limits and singularity avoidance. At the lower layer, a robust feedback controller is designed for the gangway to mitigate modeling uncertainties and environmental disturbances and to ensure stable tracking. MATLAB R2024a-based simulations under representative wave conditions demonstrate that the proposed architecture improves end effector tracking accuracy and closed-loop stability compared with baseline strategies, providing a feasible engineering solution for shipboard boarding operations in complex sea states. Full article

Cyber-physical systems (CPSs) represent sophisticated intelligent architectures that tightly couple computational elements, communication networks, and physical processes. Their deployments now span virtually every industrial and civilian domain—from power grids and manufacturing plants to autonomous transportation networks. Ensuring the secure operation of CPSs relies fundamentally on effective time series anomaly detection, which remains a challenging task due to the complex, often unknown system dynamics and non-negligible sensor noise present in real-world environments. To address these challenges, we introduce a Neural Network-Enhanced Kalman Filter (NNEKF), a novel anomaly detection framework that combines model-based filtering with data-driven learning. The NNEKF employs a two-stage trained neural network with a specialized architecture: the first stage learns the underlying dynamics of the CPS, while the second stage optimizes the computation of the Kalman gain during the update step. At inference time, the enhanced Kalman filter recursively estimates the likelihood of observed sensor measurements to identify anomalies, supported by a batched parallel inference scheme that delivers substantial speedups. Extensive experiments on benchmark datasets demonstrate that the NNEKF attains an average F1-score of 0.935, coupled with rapid inference and minimal model footprint—surpassing all competitive baselines and facilitating dependable real-time anomaly detection for CPS environments. Full article

Energy-efficient and resilient decentralized unmanned aerial vehicles (UAV) swarm operation in global navigation satellite system (GNSS) denied environments remains challenging because propulsion demand, communication load, and onboard inference are tightly coupled at the mission level. Although prior studies have examined some of these components separately, their joint evaluation within adaptive decentralized swarms remains limited under degraded navigation conditions. This study proposes an energy-aware adaptive communication-topology framework integrated with lightweight edge artificial intelligence (AI)-assisted navigation for decentralized UAV swarms operating without reliable GNSS support. The approach combines a unified mission-level energy-accounting structure for propulsion, communication, and onboard inference, a residual-energy-aware topology adaptation mechanism for preserving swarm connectivity, and a convolutional neural network-long short-term memory (CNN–LSTM) based edge-AI navigation module for improving localization robustness. The framework was evaluated in 1200 s Robot Operating System 2 (ROS2)–Gazebo–PX4 simulation scenarios against fixed topology and extended Kalman filter (EKF)-based baselines. Under the adopted simulation assumptions, the proposed configuration achieved a 22.7% reduction in total energy consumption, with the largest decrease observed in the communication-energy component, while preserving positive algebraic connectivity across all evaluated runs. The edge-AI module yielded a 4.8% root mean square error (RMSE) reduction relative to the EKF baseline, indicating a modest but meaningful improvement in localization performance. These results support the feasibility of integrated energy-aware swarm coordination in GNSS-denied environments; however, they should be interpreted as simulation-based evidence under the adopted modeling assumptions, and further high-fidelity propagation modeling, broader learning validation, and hardware-in-the-loop studies remain necessary. Full article