Automated Guided Vehicles (AGVs) are the fastest commercially available application of unmanned driving technology, and the research significance of unmanned vehicle technology remains substantial. This paper investigates the driving mode of AGVs and proposes a method to extend the kinematic model of center-driven unmanned vehicles to front-wheel drive. This change in driving force enables unmanned vehicles to achieve faster tracking and higher consistency, solving the problems of long tracking time and insufficient accuracy in complex environments and reducing production costs. By analyzing the posture relationship of the unmanned vehicle system during movement, we established a posture error system to analyze the trajectory tracking problem. Utilizing Lyapunov stability theory and the concept of backstepping, we designed a control scheme that uses linear velocity and heading angular velocity as variables for the posture error system. This control scheme aims to stabilize the system and achieve synchronized trajectory tracking control of the unmanned vehicle. The impact of control parameters in the controller on tracking performance is also discussed. The final experimental simulation results show that the system error stabilizes, and the unmanned vehicle accurately follows the predetermined trajectory, verifying the feasibility of our proposed method and control scheme.

Crop change detection plays a pivotal role in ensuring agricultural sustainability and environmental monitoring. Leveraging the high spectral resolution of hyperspectral imagery and bi-temporal analysis, this study presents a Fast Hyperspectral Change Detection algorithm based on Spatial Reconstruction (FHCDSR) designed to identify subtle agricultural changes with improved accuracy and computational efficiency. The proposed method incorporates three key innovations: (1) boundary-constrained preprocessing of 3D hyperspectral data, (2) Laplacian-regularized spatial reconstruction, and (3) a novel tensor-based change detection framework. We conduct a comprehensive evaluation of FHCDSR using two datasets: the Hermiston dataset and the Yancheng dataset. Experimental results demonstrate that FHCDSR achieves superior performance on both datasets, with AUC values of 90.20% (Hermiston) and 95.39% (Yancheng), outperforming six state-of-the-art comparison methods by 3.39–14.78% in detection accuracy. Remarkably, the algorithm maintains high computational efficiency, completing analyses in 9.76 seconds (Hermiston) and 10.90 seconds (Yancheng), representing up to 94.05% reduction in processing time compared to conventional methods. The consistent performance across different agricultural landscapes highlights FHCDSR’s robustness as an unsupervised change detection solution, with significant potential for precision agriculture and wetland ecosystem monitoring applications.

Hyperspectral images (HSIs) contain large amounts of spectral and spatial information, and this provides the possibility for ground object classification. However, when using the traditional method, achieving a satisfactory classification result is difficult because of the insufficient labeling of samples in the training set. In addition, parameter adjustment during HSI classification is time-consuming. This paper proposes a novel fusion method based on the maximum noise fraction (MNF) and adaptive random multigraphs for HSI classification. Considering the overall spectrum of the object and the correlation of adjacent bands, the MNF was utilized to reduce the spectral dimension. Next, a multiscale local binary pattern (LBP) analysis was performed on the MNF dimension-reduced data to extract the spatial features of different scales. The obtained multiscale spatial features were then stacked with the MNF dimension-reduced spectral features to form multiscale spectral-spatial features (SSFs), which were sent into the RMG for HSI classification. Optimal performance was obtained by fusion. For all three real datasets, our method achieved competitive results with only 10 training samples. More importantly, the classification parameters corresponding to different hyperspectral data can be automatically optimized using our method.

With the increase in data pattern diversity, multi-view clustering has become a hot topic for researchers. However, most of the current multi-view clustering methods focus on unsupervised learning scenarios, which cannot utilize the label information in the data. Furthermore, they could not handle the outliers, which may exist in the data. In order to address these issues, this paper proposes a Correntropy-based Multi-view Low-rank Matrix Factorization (CMLMF) method for multi-view data semi-supervised clustering. In order to sufficiently utilize the local structure information, a correntropy-based multi-view constraint graph learning framework is proposed to extract the local structure hidden in the multi-view data adaptively. Furthermore, a correntropy-based multi-view Low-rank Matrix Factorization (LMF) model is proposed, which can be incorporated with the adaptive graph learning framework to extract the global reconstruction information of data. The label information is introduced by a constraint matrix, which can be utilized for both global reconstruction and local structure information learning without adding any hyperparameter. Due to the maximum correntropy criterion, the proposed method is robust against the outliers in the data in each view and helps to extract a more current consensus representation. An efficient solving algorithm based on accelerated Block Coordinate Update (BCU) is designed to solve the model. Experiments are conducted on five real-world datasets, and the proposed method is compared with ten state-of-the-art methods to evaluate the effectiveness of the CMLMF method. The results show that the proposed method performs better clustering in most cases.