Maneuvering Target Track Prediction Model

In this paper, we introduce a novel and efficient hybrid trajectory planning method for autonomous driving in highly constrained environments. The contributions of this paper are fourfold. First, we present a trajectory planning framework that is able to handle geometry constraints, nonholonomic constraints, and dynamics constraints of cars in a humanlike and layered fashion and generate curvature-continuous, kinodynamically feasible, smooth, and collision-free trajectories in real time. Second, we present a derivative-free global path modification algorithm to extract high-order state information in free space for state sampling. Third, we extend the regular state-space sampling method widely used in on-road autonomous driving systems to a multi-phase deterministic state-space sampling method that is able to approximate complex maneuvers. Fourth, we improve collision checking accuracy and efficiency by using a different car footprint approximation strategy and a two-phase collision checking routine. A range of challenging simulation experiments show that the proposed method returns high-quality trajectories in real time and outperforms existing planners, such as hybrid A* and conjugate-gradient descent path smoother in terms of path quality, efficiency, and computation resources used.

Autonomous capture of an unknown non-cooperative aerial target is a complex task requiring real-time target localization, trajectory prediction and control. The current work presents an estimation and control system for a quadrotor in order to track and grab a dynamic aerial target. A Kalman filter is used to estimate and predict the target’s pose in real time, which is further used as reference by a Model Predictive Controller for tracking and grabbing the target while chasing it. The efficacy of the proposed controller is demonstrated through repeated trials with varying initial conditions and target maneuvers. The controller is compared with a conventional PD controller. The accuracy of estimation algorithms and the ability of the proposed controller to neutralize the target is demonstrated by means of simulations in gazebo.

An Agent-Based Approach to Aircraft Conflict Resolution with Constraints

Accurate quantitative analysis and prediction of dust concentration in mines play a vital role in avoiding pneumoconiosis to a certain extent, improving industrial production efficiency, and protecting the ecological environment. The research has far‐reaching significance for the prediction of dust concentration in mines in the future. Aiming at the shortcomings of the grey GM (1, 1) model in forecasting the data sequence with large random fluctuation, a grey Markov chain forecasting model is established. Firstly, considering the timeliness of monitoring data, the new dust concentration data is supplemented by using the method of cubic spline interpolation in the original data sequence. Therefore, the GM (1, 1) model is established by the method of metabolism. Then, the GM (1, 1) model is optimized by the theory of the Markov chain model. According to the relative error range generated during the prediction, the state interval is divided. Subsequently, the corresponding state probability transition matrix is constructed to obtain the grey Markov prediction model. The model was applied to the prediction of mine dust concentration and compared with the prediction results of the BP neural network model, grey prediction model, and ARIMA (1, 2, 1) model. The results showed that the prediction accuracy of the grey Markov model was significantly improved compared with other traditional prediction models. Therefore, the rationality and accuracy of this model in the prediction of mine dust concentration were verified.

Face has rich geometrical changes because of the differences in identity and expression. This brings great inconvenience to the collection of human face features. Motion capture technology is a new data acquisition method in recent years. In the emerging fields where computer graphics, image processing, data processing and other disciplines permeate and cross each other, motion capture is a process of recording dynamic motion trajectories in real time. It tracks key points in space and measures key points in real time. The three-dimensional coordinates transform the trajectory into a mathematical representation. The Caralive and Carapost software provided by Vicon provide convenience for the collection of facial features.

Fellow-Driven Project Leads to National Registry for COVID-19 Biomarkers.

Spatial Sequence Coding Differs during Slow and Fast Gamma Rhythms in the Hippocampus

Maneuvering targets state prediction based on robust H ∞ filtering in opto-electronic tracking system

Rapid development in robotics and bionics makes it possible for robotic leg prostheses to help amputees while imposing challenges on duplicating the motion characteristics of the amputees’ healthy leg. One critical problem in prosthetic control is joint angle drift problem, that is, a repetitive motion trajectory in task space cannot guarantee that the generated motion trajectories in joint space are also repetitive. In order to solve this problem, in this article, we propose neural-dynamics optimization for robotic leg prostheses to generate the repetitive joint trajectories in real time. Our proposed method duplicates the self-selected walking speed of the amputees’ healthy leg. The online motion generation is formulated as a constrained quadratic programming (QP) optimization problem whose objective function adopts the kinetic energy and joint displacement performance criterion. The varying parameter convergent differential neural network is developed as a real-time QP solver which can globally converge to the optimal solution of the constrained QP problem. Then, a repetitive learning controller is designed for robotic leg prostheses to reduce the tracking errors while following the repetitive motion. Through the physical experiments on the developed two-degree-of-freedom robotic leg prosthesis worn on an amputee, the neural-dynamics optimization is substantiated to be a remedy of online motion generation, and the effectiveness of repetitive learning control for reducing the prosthetic tacking errors is verified.

This paper describes the development and preliminary testing of an image-guided system for the placement of iliosacral screws to stabilize pelvic ring fractures percutaneously, with the aim of decreasing the incidence of surgical complications and increasing the accuracy of screw placement. Pre-operative planning of screw trajectories is performed on a 3D model of the pelvis constructed from CT scans. During surgery, a 6D optical localizer is used to track the positions and orientations of an ultrasound probe, a surgical drill and a reference frame fixed to the posterior iliac crest. Registration of the pre-operative model with curves segmented from the ultrasound scans is performed using a surface-based algorithm. The drill tip and axis are displayed relative to the desired screw trajectories in real time. The accuracy of the system has been verified in four cadaver specimens by comparing actual screw trajectories to the desired trajectories and by measuring the distance from each screw to important anatomical landmarks on post-operative CT scans. All screws were considered to be in correct position.

Motorized functional electrical stimulation (FES) cycling has been demonstrated to have numerous health benefits for individuals suffering from neurological disorders. FES-cycling is usually designed to track the desired trajectories in real time. However, there are input delays between the exertion of the stimulation and the corresponding muscle contraction that potentially destabilize the system and undermine training efforts. Meanwhile, muscle fatigue gives rise to a time-varying input delay and decreased force. Moreover, switching between FES and motor control can be chattering and destabilizing owing to the high frequency. This article constructs Lyapunov-Krasovskii functionals to analyze the stability and robustness of the nonlinear cycling system with time-varying input delay. A new average dwell time condition is then provided to ensure the input-to-state stability of the considered systems. Finally, numerical simulations are illustrated to verify the effectiveness of the developed controller.

This investigation examines the capacity of low-bandwidth wireless mesh networks to mitigate educational disparities in rural environments by reducing cognitive load and promoting durable content retention. Intermittent and fragmented connectivity across remote educational zones impedes sustained access to audiovisual and interactive resources, thereby widening the achievement gap relative to urban peer groups. The present initiative advances a modular mesh-network infrastructure specifically calibrated for constrained capital and operating budgets, permitting incremental scaling while embedding redundancy within and among rural primary and secondary campuses. Utilizing adaptive routing algorithms, the fabric assembles a dynamic, self-correcting lattice that provides persistent, standards-aligned resource delivery, overcoming the weaknesses of conventional backbone and satellite deployment modalities. The primary innovation of our design lies in the integration of cognitive-load-sensitive mechanisms across the entire network topology. A salient embodiment of this is our anticipatory dynamic content caching algorithm, which assimilates historical and contextual tensor information in order to project future curricular needs. Educational artifacts are thus instantly retrievable, thus avoiding the intrinsic fatigue associated with wait times, an effect magnified when operating over intermittent, low-bandwidth links. In parallel, our link-aware routing protocol dynamically reoptimizes data trajectories in real time, using continuous telemetry of link health measurements. By diverging packets away from localized loss zones or congestion, the protocol stabilizes aggregate throughput and compresses jitter. The synergistic effect of these two layers equips learners with a stable, high-definition pedagogical sequence, effectively decoupling instructional advancement from the unpredictable fluctuations of the underlying network infrastructure.

The CNC machine tool is the passive executor of machining code. It cannot predict the machining accuracy during machining. If the error is found to be out of tolerance after processing, it will not only scrap the parts, but also greatly affect the processing efficiency. This phenomenon is very prominent when machining sculptured surface parts with five-axis machine tools. Therefore, this paper proposes a Digital Twin (DT) modeling method of five-axis machine tools for predicting Continuous Trajectory Contour Error (CTCE) caused by tracking errors and geometric errors. The DT consists of three parts: the Setpoints Trajectory (ST) model, the Actual Trajectory (AT) model considering tracking errors and geometric errors and the CTCE model. For a specific machine tool, according to the basic geometric information of the machine tool (tool length, kinematic chain information, etc.) and 41 geometric errors, the DT can be established. Inputting the Setpoints Positions (SPs) and the Linear Encoder Detection Positions (LEDPs), the DT can be used to predict the Tool-Tip Position Trajectory (TTPT) contour error and the Tool Orientation Trajectory (TOT) contour error. In order to verify the proposed method experimentally, the KMC400S U five-axis machine tool is selected to establish its DT by which the contour error of the S-shaped trajectory are predicted offline. Then, the DMU50 five-axis machine tool is selected to establish its DT to predict the contour error of the circular trajectory in real time. Combined with the deep motion mechanism, this paper proposes a DT modeling method for the vertical application scene of parts machining accuracy prediction, which is of great significance to developing the DT application theory and ensuring the machining accuracy of parts.

An accurate single-molecule kinetic isotope effect (sm-KIE) was applied to circumvent the inherent limitation of conventional ensemble KIE by using graphene-molecule-graphene single-molecule junctions. In situ monitoring of the single-molecule reaction trajectories in real time with high temporal resolution has the capability to characterize the deeper information brought by KIE. The C-O bond cleavage and the C-C bond formation of the transition state (TS) were observed in the Claisen rearrangement through the secondary kinetic isotope effect, demonstrating the high detection sensitivity and accuracy of this method. More interestingly, this sm-KIE can be used to determine TS structures under different electric fields, revealing the multidimensional regulation of the TS. The detection and manipulation of the TS provide a broad perspective to understand and optimize chemical reactions and biomimetic progress.

Real-time and efficient monitoring of geological disasters has received extensive attention in the application of UAV surveying and mapping control technology. The application of traditional landslide monitoring methods lacks the accuracy of control algorithms, which has become a hot issue currently facing. Based on the landslide surface subsidence monitoring method, this article designs the UAV trajectory scheduling subsidence monitoring software, which can monitor the UAV’s flight status and navigation information, and draw the flight trajectory in real time. At the same time, the model solves the problem of storage and management of landslide inspection results by the landslide inspection management system, and realizes the functions of entering and querying landslide information, viewing inspection results, landslide safety judgment, generating reports, and autonomous control. The simulation results show that the global accuracy reaches 0.975, and the algorithm recognition degree reaches 99.8%, which promotes the reliability of the landslide monitoring data for the identification of the surveying and mapping trajectory, and provides a decision-making basis for landslide treatment.