Search Results (129)

The parameter identification of Autonomous Underwater Vehicles (AUVs) serves as a fundamental basis for achieving high-precision motion control, state monitoring, and system development. Currently, AUV parameter identification typically relies on the complete motion information obtained from onboard sensors. However, in practical applications, it is often challenging to accurately measure key state variables such as velocity and angular velocity, resulting in incomplete measurement data that compromises identification accuracy and model reliability. This issue is particularly pronounced in vertical motion tasks involving low-speed, large pitch angles, and highly maneuverable conditions, where the strong coupling and nonlinear characteristics of underwater vehicles become more significant. Traditional hydrodynamic models based on full-state measurements often suffer from limited descriptive capability and difficulties in parameter estimation under such conditions. To address these challenges, this study investigates a parameter identification method for AUVs operating under vertical, large-amplitude maneuvers with constrained measurement information. A control autoregressive (CAR) model-based identification approach is derived, which requires only pitch angle, vertical velocity, and vertical position data, thereby reducing the dependence on complete state observations. To overcome the limitations of the conventional Recursive Least Squares (RLS) algorithm—namely, its slow convergence and low accuracy under rapidly changing conditions—a Multi-Innovation Least Squares (MILS) algorithm is proposed to enable the efficient estimation of nonlinear hydrodynamic characteristics in complex dynamic environments. The simulation and experimental results validate the effectiveness of the proposed method, demonstrating high identification accuracy and robustness in scenarios involving large pitch angles and rapid maneuvering. The results confirm that the combined use of the CAR model and MILS algorithm significantly enhances model adaptability and accuracy, providing a solid data foundation and theoretical support for the design of AUV control systems in complex operational environments. Full article

With the development of the shipbuilding industry, nonparametric prediction has become the mainstream method for predicting ship maneuvering motion. However, the lack of transparency and interpretability make the output process of the prediction results challenging to track and understand. An interpretable deep learning framework based on the NbeatsX model is presented for nonparametric ship maneuvering motion prediction. Its three-tier fully connected architecture incorporates trend, seasonal, and exogenous constraints to decompose motion data, enhancing temporal and contextual learning while rendering the prediction process transparent. On the KVLCC2 zig-zag maneuver dataset, NbeatsX achieves NRMSEs of 0.01872, 0.01234, and 0.01661 for surge speed, sway speed, and yaw rate, with SMAPEs of 9.21%, 6.40%, and 7.66% and R² values all above 0.995, yielding a more than 20% average error reduction compared with LS-SVM, LSTM, and LSTM–Attention and reducing total training time by about 15%. This method unifies high-fidelity forecasting with transparent decision tracing. It is an effective aid for ship maneuvering, offering more credible support for maritime navigation and safety decision-making, and it has substantial practical application potential. Full article

Star sensors, as the most precise attitude measurement devices currently available, play a crucial role in spacecraft attitude estimation. However, traditional frame-based cameras tend to suffer from target blur and loss under high-dynamic maneuvers, which severely limit the applicability of conventional star sensors in complex space environments. In contrast, event cameras—drawing inspiration from biological vision—can capture brightness changes at ultrahigh speeds and output a series of asynchronous events, thereby demonstrating enormous potential for space detection applications. Based on this, this paper proposes an event data extraction method for weak, high-dynamic space targets to enhance the performance of event cameras in detecting space targets under high-dynamic maneuvers. In the target denoising phase, we fully consider the characteristics of space targets’ motion trajectories and optimize a classical spatiotemporal correlation filter, thereby significantly improving the signal-to-noise ratio for weak targets. During the target extraction stage, we introduce the DBSCAN clustering algorithm to achieve the subpixel-level extraction of target centroids. Moreover, to address issues of target trajectory distortion and data discontinuity in certain ultrahigh-dynamic scenarios, we construct a camera motion model based on real-time motion data from an inertial measurement unit (IMU) and utilize it to effectively compensate for and correct the target’s trajectory. Finally, a ground-based simulation system is established to validate the applicability and superior performance of the proposed method in real-world scenarios. Full article

The paper presents the innovative approach to a high-fidelity motorcycle riding simulator based on VR (Virtual Reality)-visualization, equipped with a Gough-Stewart 6-DOF (Degrees of Freedom) motion platform. Such a solution integrates a real-time tension sensor system as a source for highly realistic motion cueing control as well as the servomotor integrated into the steering system. Tension forces are measured at four points on the mock-up chassis, allowing a comprehensive analysis of rider interaction during various maneuvers. The simulator is developed to simulate realistic riding scenarios with immersive motion and visual feedback, enhanced with the simulation of external influences—headwind. This paper presents results of a validation study—pilot experiments conducted to evaluate selected riding scenarios and validate the innovative simulator setup, focusing on force distribution and system responsiveness to support further research in motorcycle HMI (Human–Machine Interaction), rider behavior, and training. Full article

Reliable wireless communication is essential for mobile robotic systems operating in dynamic environments, particularly in the context of smart mobility and cloud-integrated urban infrastructures. This article presents an experimental study analyzing the impact of robot motion dynamics on wireless network performance, contributing to the broader discussion on data reliability and communication efficiency in intelligent transportation systems. Measurements were conducted using a quadruped robot equipped with an onboard edge computing device, navigating predefined trajectories in a laboratory setting designed to emulate real-world variability. Key wireless parameters, including signal strength (RSSI), latency, and packet loss, were continuously monitored alongside robot kinematic data such as speed, orientation (roll, pitch, yaw), and movement patterns. The results show a significant correlation between dynamic motion—especially high forward velocities and rotational maneuvers—and degradations in network performance. Increased robot speeds and frequent orientation changes were associated with elevated latency and greater packet loss, while static or low-motion periods exhibited more stable communication. These findings highlight critical challenges for real-time data transmission in mobile IoRT (Internet of Robotic Things) systems, and emphasize the role of network-aware robotic behavior, interoperable communication protocols, and edge-to-cloud data integration in ensuring robust wireless performance within smart city environments. Full article

To address the interception problem against maneuvering targets, this paper proposes a multi-agent cooperative guidance law based on a multi-directional interception formation. A three-dimensional agent–target engagement kinematics model is established, and a fixed-time observer is designed to estimate the target acceleration. By utilizing the agent-to-agent communication network, real-time exchange of motion state information among the agents is realized. Based on this, a control input along the line-of-sight (LOS) direction is designed to directly regulate the agent–target relative velocity, effectively driving the agent swarm to achieve time-to-go consensus within a fixed-time boundary. Furthermore, adaptive variable-power sliding mode control inputs are designed for both elevation and azimuth angles. By adjusting the power of the control inputs according to a preset sliding threshold, the proposed method achieves fast convergence in the early phase and smooth tracking in the latter phase under varying engagement conditions. This ensures that the elevation and azimuth angles of each agent–target pair converge to the desired values within a fixed-time boundary, forming a multi-directional interception formation and significantly improving the interception performance against maneuvering targets. Simulation results demonstrate that the proposed cooperative guidance law exhibits fast convergence, strong robustness, and high accuracy. Full article

Snowboard Big Air (SBA), recognized as an Olympic discipline since 2018, emphasizes maneuver difficulty as a key scoring criterion, requiring athletes to integrate technical skill with adaptive responses to dynamic environments in order to perform complex aerial rotations. The takeoff phase is critical, determining both flight trajectory and rotational performance through coordinated lower limb extension and upper body movements. Despite advances in motion analysis technology, quantitative assessment of key takeoff parameters remains limited. This study investigates parameters related to performance, joint kinematics, and rotational kinetics during the SBA takeoff phase to identify key factors for success and provide practical guidance to athletes and coaches. Eleven athletes from the Chinese national snowboard team performed multiple backside tricks (720°, 1080°, 1440°, and 1800°) at an outdoor dry slope with airbag landings. Three-dimensional motion capture with synchronized cameras was used to collect data on performance, joint motion, and rotational kinetics during takeoff. The results showed significant increases in most measured metrics with rising trick difficulty from 720° to 1800°. The findings reveal that elite SBA athletes optimize performance in high-difficulty maneuvers by increasing the moment of inertia, maximizing propulsion, and refining joint kinematics to enhance rotational energy and speed. These results suggest that training should emphasize lower limb power, core and shoulder strength, flexibility, and coordination to maximize performance in advanced maneuvers. Full article

To address the issue of misclassification and diminished accuracy that is prevalent in existing intent recognition models for non-cooperative spacecraft due to the omission of environmental influences, this paper presents a novel recognition framework leveraging a hybrid neural network subject to multiple constraints. The relative orbital motion of the targets is characterized and categorized through the use of Clohessy–Wiltshire equations, forming the foundation of a constrained intention dataset employed for training and evaluation. Furthermore, the method incorporates a composite architecture combining a convolutional neural network (CNN), long short-term memory (LSTM) unit, and self-attention (SA) mechanism to enhance recognition performance. The experimental results demonstrate that the integrated CNN-LSTM-SA model attains a recognition accuracy of 98.6%, significantly surpassing traditional methods and neural network models. Additionally, it demonstrates high efficiency, indicating significant promise for practical applications in avoiding spacecraft collisions and performing orbital maneuvers. Full article

Visual–Inertial Odometry (VIO) algorithms are widely adopted for autonomous drone navigation in GNSS-denied environments. However, conventional monocular and stereo VIO setups often lack robustness under challenging environmental conditions or during aggressive maneuvers, due to the sensitivity of visual information to lighting, texture, and motion blur. In this work, we enhance an existing open-source VIO algorithm to improve both the robustness and accuracy of the pose estimation. First, we integrate an IMU-based motion prediction module to improve feature tracking across frames, particularly during high-speed movements. Second, we extend the algorithm to support a multi-camera setup, which significantly improves tracking performance in low-texture environments. Finally, to reduce the computational complexity, we introduce an adaptive feature selection strategy that dynamically adjusts the detection thresholds according to the number of detected features. Experimental results validate the proposed approaches, demonstrating notable improvements in both accuracy and robustness across a range of challenging scenarios. Full article

Despite the pivotal role of filtering and smoothing techniques in the preprocessing of ship maneuvering data for robust identification, persistent challenges in reconciling noise suppression with dynamic fidelity preservation have limited algorithmic advancements in recent decades. We propose an online smoothing method enhanced by the Expectation-Maximization (EM) algorithm framework that effectively extracts high-fidelity dynamic features from raw maneuvering data, thereby enhancing the fidelity of subsequent ship identification systems. Our method effectively addresses the challenges posed by heavy-tailed Student-t distributed noise and parameter uncertainty inherent in ship motion data, demonstrating robust parameter learning capabilities, even when initial ship motion system parameters deviate from real conditions. Through iterative data assimilation, the algorithm adaptively calibrates noise distribution parameters while preserving motion smoothness, achieving superior accuracy in velocity and heading estimation compared to conventional Rauch–Tung–Striebel (RTS) smoothers. By integrating parameter adaptation within the smoothing framework, the proposed method reduces motion prediction errors by 23.6% in irregular sea states, as validated using real ship motion data from autonomous navigation tests. Full article

Tracking targets with nonlinear motion patterns remains a significant challenge in state estimation. We propose an energy-adaptive stochastic gradient Hamiltonian sequential Monte Carlo (SGHSMC) filter that combines adaptive energy dynamics with efficient particle sampling. The proposed method features a novel energy function that automatically adapts to target dynamics while minimizing the need for resampling operations. By integrating Hamiltonian Monte Carlo sampling with stochastic gradient techniques, our approach achieves a 40% reduction in computational overhead compared to traditional particle filters while maintaining particle diversity. We validate the method through both simulation and experimental studies. The simulation employs a univariate nonstationary growth model, demonstrating improvements of 39% in tracking accuracy over the extended Kalman filter (EKF) and 29% over standard sequential Monte Carlo methods. The experimental validation uses a bearing-only tracking scenario with a quadrupedal robot executing complex maneuvers, tracked by high-precision angular measurement systems. In practical tracking scenarios, the SGHSMC filter achieves a 77% better accuracy than EKF while maintaining the computational efficiency suitable for real-time applications. The algorithm demonstrates effectiveness in scenarios involving rapid state changes and irregular motion patterns, offering a robust solution for challenging target tracking problems. Full article

Traditional guidance and control systems often treat guidance and control systems separately, leading to reduced interception accuracy and responsiveness, especially during high-speed terminal trajectories. These limitations are further exacerbated in autonomous underwater vehicles (AUVs) due to unknown wave/current disturbances, harsh underwater acoustic conditions, and limited sensor capabilities. To address these challenges, this paper studies an integrated guidance and control (IGC) design for AUVs intercepting maneuvering targets with unknown disturbances and unmeasurable system states. The IGC model is derived based on the relative motion equations between the AUV and the target, incorporating the lateral dynamics of the AUV. A model transformation is introduced to synthesize external disturbances with unmeasurable states, extending the resultant disturbance to a new system state. A finite-time convergent extended state observer (ESO) is thus designed for the transformed system to estimate the unknown signals. Using these estimates from the observer, a finite-time event-triggered sliding mode controller is developed, ensuring finite-time convergence of system errors to an adjustable residual set, as rigorously proven through Lyapunov stability analysis. Simulation results demonstrate the superiority of the proposed method in achieving higher interception accuracy and faster response compared to traditional guidance and control approaches with unknown disturbances and unmeasurable states. Full article

Aiming at the problem of nonlinear observation model mismatch and insufficient anti-interference ability of SINS/GNSS integrated navigation system in complex dynamic environment, this paper proposes an adaptive robust filtering algorithm with improved fading factor. Aiming at the problem that the traditional Kalman filter is easy to diverge in severe heave motion and abnormal observation, a multi-source information fusion framework integrating satellite positioning geometric accuracy factor (PDOP), solution quality factor (Q value), effective satellite observation number (Satnum), and residual vector is constructed. The dynamic weight adjustment mechanism is designed to realize the real-time optimization of the fading factor. Through the collaborative optimization of robust estimation theory and adaptive filtering, a dual robust mechanism is constructed by combining the sequential update strategy. In the measurement update stage, the observation weight is dynamically adjusted according to the innovation covariance, and the fading memory factor is introduced in the time update stage to suppress the error accumulation of the model. The experimental results show that compared with EKF, Sage-Husa adaptive filtering and robust filtering algorithms, the three-dimensional positioning accuracy is improved by 47.12%, 35.26%, and 9.58%, respectively, in the vehicle strong maneuvering scene. In the scene of ship-borne heave motion, the corresponding increase is 19.44%, 10.47%, and 8.28%. The research results provide an effective anti-interference solution for navigation systems in high dynamic and complex environments. Full article

Coronary heart disease, a leading global cause of mortality, has witnessed significant advancement through robotic coronary artery bypass grafting (CABG), with the internal mammary artery (IMA) emerging as the preferred “golden conduit” for its exceptional long-term patency. Despite these advances, robotic-assisted IMA harvesting remains challenging due to the absence of force feedback, complex surgical maneuvers, and proximity to the beating heart. This study introduces a novel virtual simulation platform for robotic IMA harvesting that integrates dynamic anatomical modeling and real-time haptic feedback. By incorporating a dynamic cardiac model into the surgical scene, our system precisely simulates the impact of cardiac pulsation on thoracic cavity operations. The platform features high-fidelity representations of thoracic anatomy and soft tissue deformation, underpinned by a comprehensive biomechanical framework encompassing fascia, adipose tissue, and vascular structures. Our key innovations include a topology-preserving cutting algorithm, a bidirectional tissue coupling mechanism, and dual-channel haptic feedback for electrocautery simulation. Quantitative assessment using our newly proposed Spatial Asymmetry Index (SAI) demonstrated significant behavioral adaptations to cardiac motion, with dynamic scenarios yielding superior SAI values compared to static conditions. These results validate the platform’s potential as an anatomically accurate, interactive, and computationally efficient solution for enhancing surgical skill acquisition in complex cardiac procedures. Full article

This paper introduces a nonlinear Lyapunov-based Finite-Time State-Dependent Differential Riccati Equation (FT-SDDRE) control scheme, considering actuator saturation constraints and ensuring that the control system operates within safe operational limits designed for satellite reconfiguration and formation-keeping in low Earth orbit (LEO) missions. This control approach addresses the challenges of reaching the relative position and velocity vectors within a defined timeframe amid various orbital perturbations. The proposed approach guarantees precise formation control by utilizing a high-fidelity relative motion model that incorporates all zonal harmonics and atmospheric drag, which are the primary environmental disturbances in LEO. Additionally, the article presents an optimization methodology to determine the most efficient State-Dependent Coefficient (SDC) form regarding fuel consumption. This optimization process minimizes energy usage through a hybrid genetic algorithm and simulated annealing (HGASA), resulting in improved performance. In addition, this paper includes a sensitivity analysis to identify the optimized SDC parameterization for different satellite reconfiguration maneuvers. These maneuvers encompass radial, along-track, and cross-track adjustments, each with varying baseline distances. The analysis provides insights into how different parameterizations affect reconfiguration performance, ensuring precise and efficient control for each type of maneuver. The finite-time controller proposed here is benchmarked against other forms of SDRE controllers, showing reduced error margins. To further assess the control system’s effectiveness, an input saturation constraint is integrated, ensuring that the control system operates within safe operational limits, ultimately leading to the successful execution of the mission. Full article