Search Results (408)

Remotely operated vehicles (ROVs) face challenges in achieving optimal trajectory tracking performance during underwater movement due to external disturbances and parameter uncertainties. To address this issue, this paper proposes a position and attitude control strategy for underwater robots based on a reinforcement learning active disturbance rejection controller. The linear active disturbance rejection controller has achieved satisfactory results in the field of underwater robot control. However, fixed-parameter controllers cannot achieve optimal control performance for the controlled object. Therefore, further exploration of the adaptive capability of control parameters based on the linear active disturbance rejection controller was conducted. The deep deterministic policy gradient (DDPG) algorithm was used to optimize the linear extended state observer (LESO). This strategy employs deep neural networks to adjust the LESO parameters online based on measured states, allowing for more accurate estimation of model uncertainties and environmental disturbances, and compensating the total disturbance into the control input online, resulting in better disturbance estimation and control performance. Simulation results show that the proposed control scheme, compared to PID and fixed parameter LADRC, as well as the double closed-loop sliding mode control method based on nonlinear observers (NESO-DSMC), significantly improves the disturbance estimation accuracy of the linear active disturbance rejection controller, leading to higher control precision and stronger robustness, thus demonstrating the effectiveness of the proposed control strategy. Full article

The trend toward conducting vision-based spacecraft pose estimation using deep neural networks, which necessitates accurately labeled datasets for training, is addressed in this paper. A method for generating an image regression-labeled dataset for spacecraft pose estimation through simulations involving Unreal Engine 5 is proposed herein. This work provides detailed algorithms for pose sampling and image generation, making it easy to reproduce the employed dataset. The dataset consists of images obtained under harsh lighting conditions and high-resolution backgrounds, featuring spacecraft models including Dragon, Soyuz, Tianzhou, and the ascent vehicle of Chang’E-6. The dataset comprises 40,000 high-resolution images, which are evenly distributed, with 10,000 images for each spacecraft model in scenes with both the Earth and the Moon. Each image is labeled with multivariate pose vectors that represent the relative position and attitude of the corresponding spacecraft with respect to the camera. This work emphasizes the critical role of realistic simulations in creating cost-effective synthetic datasets for training neural network-based pose estimators and publicly available for further study. Full article

With the rapid changes in external environments, cognitive adaptability has become crucial for vocational students’ personal growth and career development. However, previous research has predominantly focused on traditional single-level effects, overlooking the multilevel impacts of school climates. Hence, based on social cognitive theory and social–ecological systems theory, this study employs multilevel structural equation modeling (MSEM) to examine the effects of competitive and collaborative environments on vocational students’ competitive attitude, task motivation, and cognitive adaptability at both the student level (Within) and school level (Between). This study utilizes data from the Programme for International Student Assessment (PISA) 2018, analyzing a sample of 814 vocational schools and 20,978 vocational students from 18 countries and regions. Using Mplus 8.10, we applied maximum likelihood estimation with robust standard errors (MLR) to validate the multilevel structural equation model (MSEM) and examine the hierarchical effects of competitive and collaborative environments on vocational students’ competitive attitude, task motivation, and cognitive adaptability. The findings indicate that both competitive attitude and task motivation positively impact cognitive adaptability at both the student and school levels. While competitive environments enhance competitive attitudes at both levels, their effects on task motivation differ, as they are positive at the student level but negative at the school level. Conversely, collaborative environments positively influence task motivation at both levels but only affect competitive attitudes at the student level. A comparison between multilevel and single-level models suggests that multilevel modeling better captures the hierarchical effects within school environments. The results highlight that moderate competition at the student level fosters motivation and adaptability, whereas highly competitive school environments may suppress motivation. In contrast, fostering a collaborative school climate enhances task motivation and cognitive adaptability. These findings underscore the importance of balancing competition and collaboration in vocational education to support students’ holistic development. Full article

Compared to traditional remote sensing technology, video satellites have unique advantages such as real-time continuous imaging and the ability to independently complete staring observation. To achieve effective staring control, the satellite needs to perform attitude maneuvers to ensure that the target’s projection stays within the camera’s visual field and gradually reaches the desired position. The generation of image-based control instructions relies on the calculation of projection coordinates and their rate of change (i.e., visual velocity) of the projection point on the camera’s image plane. However, the visual velocity is usually difficult to obtain directly. Traditional calculation methods of visual velocity using time differentials are limited by video frame rates and the computing power of onboard processors, and is greatly affected by measurement noise, resulting in decreased control accuracy and a higher consumption of maneuvering energy. In order to address the shortcomings of traditional calculations of visual speed by time difference methods, this paper proposes a control method based on the estimation of visual velocity, which achieves real-time calculation of the target’s visual speed through adaptive estimation; then, the stability of the closed-loop system is rigorously demonstrated. Finally, through simulation comparison with the traditional differential method, the results show that the proposed method has an improvement in attitude accuracy for about 74% and a reduction in energy consumption by about 77%. Full article

The q-method for pose estimation utilizes on-board measurement vectors of reference objects to calculate air vehicle position and orientation with respect to an Inertial frame. This new method solves for the quaternion eigenvalue solution of the optimal pose to minimize the error in the derived system of equations. The generalized q-method extends Davenport’s q-method for satellite attitude estimation by incorporating inertial position into the relative model and eliminating assumptions throughout the derivation that require spacecraft applications. Thus, the pose estimation model is developed and implemented for UAV applications using an onboard camera to obtain measurements in a controlled environment. Combined with numerical methods, algorithm outputs for position and orientation are validated against truth data to prove accurate estimation despite sensor error. Full article

This paper proposes a pose estimation algorithm using INS and LiDAR for precise cooperative relative navigation between target and chaser spacecraft in a close docking mission scenario. Previous cooperative algorithms have proposed estimating position and pose transformations using typical matching methods or to pre-extract and utilize features from point cloud data. However, in the case of general proximity rendezvous docking, a straight-line approach scenario with very few changes in attitude is usually assumed, and, in this case, pose estimation using simple matching techniques or feature point extraction leads to inaccurate results. To solve this problem, this paper performed a principal component analysis (PCA) based on ICP to align the initial transformation matrix. To keep the distribution of point cloud data constant, the point cloud at the time of docking was applied to ICP to minimize the change in the distribution of point clouds over time. Finally, we designed an EKF filter that estimates the relative position, velocity, and attitude using the INS model and combines it with the relative pose estimated from the point cloud; the proposed method showed the results of estimating the relative pose more effectively than the previous method. The simulation and experiment showed more accurate estimation results than the ICP method in position and attitude, respectively. In particular, in the case of position, both the simulation and experiment showed 0.46 m and 0.32 m better estimation results in the z-axis. Also, attitude estimation showed 0.11° and 2.66° better results in roll and 0.01° and 0.34° better results in pitch. This shows that the proposed algorithm provided better pose estimation results in the docking scenario in a straight line. Full article

Initial alignment plays a pivotal role in inertial navigation systems, as even small orientation errors introduced at startup can significantly degrade subsequent positioning and attitude estimates. In this context, we propose a novel inverse alignment method for rotational inertial navigation that leverages the group affine property and high-speed computing to accelerate and refine the alignment process. Adopting inverse navigation and Lie group theory, we derive a left-invariant error model in the geocentric geosynchronous coordinate framework and rapidly achieve alignment by integrating forward and inverse Kalman filtering. During 2.5-h in-vehicle tests, our approach reduced both the maximum error and CEP (Circular Error Probable 50%) by 60% compared to standard alignment methods, and it surpassed the performance of conventional group affine alignment by improving accuracy by 7.2% and 20%, respectively. These results highlight the method’s ability to deliver swift, precise alignment across diverse initial misalignment angles, offering significant benefits for modern high-precision inertial navigation applications. Full article

This study introduces a pose estimation algorithm integrating an Inertial Navigation System (INS) with an Alternating Current (AC) magnetic field-based navigation system, referred to as the Magnetic Positioning System (MPS), evaluated using a 6 Degrees of Freedom (DoF) drone. The study addresses significant challenges such as the magnetic vector distortions and model uncertainties caused by motor noise, which degrade attitude estimation and limit the effectiveness of traditional Extended Kalman Filter (EKF)-based fusion methods. To mitigate these issues, a Tightly Coupled Unscented Kalman Filter (TC UKF) was developed to enhance robustness and navigation accuracy in dynamic environments. The proposed Unscented Kalman Filter (UKF) demonstrated a superior attitude estimation performance within a 6 m coil spacing area, outperforming both the MPS 3D LS (Least Squares) and EKF-based approaches. Furthermore, hyperparameters such as alpha, beta, and kappa were optimized using the Sequential Importance Resampling (SIR) process of the Particle Filter. This adaptive hyperparameter adjustment achieved improved navigation results compared to the default UKF settings, particularly in environments with high model uncertainty. Full article

Coal mining machine positioning technology is the key to realizing unmanned and intelligent mining of the comprehensive mining zone. Based on the traditional Strapdown Inertial Navigation System combined with Kalman-filtering coal mining machine positioning technology, non-integrity constraints are introduced, and the error of the output of the above system is filtered by an optimized Kalman filtering method proposed in this paper: non-Gaussian summation and a parallel Kalman filter bank. This method decomposes the non-Gaussian system into a linear combination of multiple Gaussian systems through the parallel Kalman filter group, then fuses the states occupying different weight coefficients and designs a method of Gaussian-term number trimming to solve the problem of parameter explosion in the filtering process, and ultimately obtains the optimal estimation of the positioning information of the coal mining machine. Experiments show that, for the coal mining machine positioning issue in the complex noise interference environment of intelligent mines, the non-Gaussian summation and parallel Kalman filter group method in this paper, compared with the traditional particle filtering method, greatly reduces the three-dimensional attitude error, three-dimensional velocity error, three-dimensional position error in the nine dimensional parameters of the estimation error, and the average estimation error. The average estimation error is reduced by 49%, 52%, 50%, 53%, 51%, 48.8%, 50.1%, 54%, and 51.3%, respectively, which significantly improves the positioning accuracy of coal mining machines, and has stronger real-time performance, stability, and accuracy in the coal mining machine positioning system. Full article

Navigation services and high-precision positioning play a significant role in emerging fields such as self-driving and mobile robots. The performance of precise point positioning (PPP) may be seriously affected by signal interference and struggles to achieve continuous and accurate positioning in complex environments. LiDAR/inertial navigation can use spatial structure information to realize pose estimation but cannot solve the problem of cumulative error. This study proposes a PPP/inertial/LiDAR combined localization algorithm based on factor graph optimization. Firstly, the algorithm performed the spatial alignment by adding the initial yaw factor. Then, the PPP factor and anchor factor were constructed using PPP information. Finally, the global localization is estimated accurately and robustly based on the factor graph. The vehicle experiment shows that the proposed algorithm in this study can achieve meter-level accuracy in complex environments and can greatly enhance the accuracy, continuity, and reliability of attitude estimation. Full article

Inertial navigation systems (INSs) provide autonomous position estimation capabilities independent of global navigation satellite systems (GNSSs). However, the high cost of traditional sensors, such as fiber-optic gyroscopes (FOGs), limits their widespread adoption. In contrast, micro-electromechanical system (MEMS)-based inertial measurement units (IMUs) offer a low-cost alternative; however, their lower accuracy and sensor bias issues, particularly in maritime environments, remain considerable obstacles. This study proposes an improved method for bias estimation by comparing the estimated values from a trajectory generator (TG)-based acceleration and angular-velocity estimation system with actual measurements. Additionally, for X- and Y-axis accelerations, we introduce a method that leverages the correlation between altitude differences derived from an INS/GNSS/gyrocompass (IGG) and those obtained during the TG estimation process to estimate the bias. Simulation datasets from experimental voyages validate the proposed method by evaluating the mean, median, normalized cross-correlation, least squares, and fast Fourier transform (FFT). The Butterworth filter achieved the smallest angular-velocity bias estimation error. For X- and Y-axis acceleration bias, altitude-based estimation achieved differences of 1.2 × 10⁻² m/s² and 1.0 × 10⁻⁴ m/s², respectively, by comparing the input bias using 30 min data. These methods enhance the positioning and attitude estimation accuracy of low-cost IMUs, providing a cost-effective maritime navigation solution. Full article

This paper focuses on the problem of ground-motion target localization tracking and motion state estimation for high-altitude reconnaissance using fixed-wing UAVs. Our goal is to accurately locate and track ground-moving targets and estimate their motion using visible light images, laser measurements of distance, and UAV position and attitude information. Firstly, this paper uses the target detection model of YOLOv8 to obtain the target pixel positions, combined with the measurement data, to establish the geolocalization model of the ground-motion target. Secondly, a motion state estimation algorithm with hierarchical filtering is proposed, and this algorithm performs motion state estimation for optoelectronic loads and ground-motion targets separately. Using the laser range sensor measurements as constraints, the optoelectronic load angle state quantities are involved together in estimating the ground target motion state, resulting in improved accuracy of ground-motion target localization tracking and motion state estimation. The experimental data show that the UAV ground-motion target localization tracking and motion estimation algorithm using hierarchical filtering reduces the localization tracking error by at least 7.5 m and the motion state estimation error by at least 0.8 m/s compared to other algorithms. Full article

The spacecraft microgravity simulation air-bearing platform is a crucial component of the spacecraft ground testing system. Special disturbances, such as the flatness and roughness of the contact surface between the air bearings and the granite platform, increasingly affect the control accuracy of the simulation experiment as the number of air bearings increases. To address this issue, this paper develops a novel compensation control system based on Active Disturbance Rejection Control (ADRC), which estimates and compensates for the disturbing forces and moments caused by the roughness and levelness of the contact surface, thereby improving the control precision of the spacecraft ground simulation system. A dynamic model of the multi-air-bearing platform under disturbance is established. A cascade ADRC algorithm based on the Linear Extended State Observer (LESO) is designed. The Gauss–Newton iteration method is used to identify the parameters of the sliding friction coefficient and the tilt angle of the air-bearing platform. A full-physics simulation experimental platform for spacecraft with rotor-based propulsion is constructed, and the proposed algorithm is validated. The experimental results show that on a marble surface with a flatness of grade 00, an overall tilt angle of 0–1 degrees, and a surface friction coefficient of 0–0.01, the position control accuracy for the simulated spacecraft can reach 1.5 cm, and the attitude control accuracy can reach 1°. Under ideal conditions, the identification accuracy for the contact surface friction coefficient is 2 × 10⁻⁴, and the recognition accuracy for the overall levelness of the marble surface can reach 1 × 10⁻³, laying the foundation for high-precision ground simulation experiments of spacecraft in multi-air-bearing scenarios. Full article

With the ever-increasing volume of maritime traffic, the risks of ship navigation are becoming more significant, making the use of advanced multi-source perception strategies and AI technologies indispensable for obtaining information about ship navigation status. In this paper, first, the ship tracking system was optimized using the Bi-YOLO network based on the C2f_BiFormer module and the OC-SORT algorithms. Second, to extract the visual trajectory of the target ship without a reference object, an absolute position estimation method based on binocular stereo vision attitude information was proposed. Then, a perception data fusion framework based on ship spatio-temporal trajectory features (ST-TF) was proposed to match GPS-based ship information with corresponding visual target information. Finally, AR technology was integrated to fuse multi-source perceptual information into the real-world navigation view. Experimental results demonstrate that the proposed method achieves a mAP0.5:0.95 of 79.6% under challenging scenarios such as low resolution, noise interference, and low-light conditions. Moreover, in the presence of the nonlinear motion of the own ship, the average relative position error of target ship visual measurements is maintained below 8%, achieving accurate absolute position estimation without reference objects. Compared to existing navigation assistance, the AR-based navigation assistance system, which utilizes ship ST-TF-based perception data fusion mechanism, enhances ship traffic situational awareness and provides reliable decision-making support to further ensure the safety of ship navigation. Full article

The robust and high-precision estimation of position and attitude information using a combined global navigation satellite system/inertial navigation system (GNSS/INS) model is essential to a wide range of applications in intelligent driving and smart transportation. GNSS systems are susceptible to inaccuracies and signal interruptions in occluded environments, which lead to unreliable parameter estimations in GNSS/INS based on filter models. To address this issue, in this paper, a GNSS/INS combination model based on factor graph optimization (FGO) is investigated and the robustness of this optimization model is evaluated in comparison to the traditional extended Kalman filter (EKF) model and robust Kalman filter (RKF) model. In this paper, both high- and low-accuracy GNSS/INS combination data are used and the two sets of urban scene data are collected using high- and low-precision consumer-grade inertial guidance systems and an in-vehicle setup. The experimental results demonstrate that the position, velocity, and attitude estimates obtained using the GNSS/INS and the FGO model are superior to those obtained using the traditional EKF and robust EKF methods. In the simulated scenarios involving gross interference and GNSS signal loss, the FGO model achieves optimal results. The maximum improvement rates of the position, velocity, and attitude estimates are 81.1%, 73.8%, and 75.1% compared to the EKF method and 79.8%, 72.1%, and 57.1% compared to the RKF method, respectively. Full article