Hybrid Trajectory Planning for Autonomous Driving in Highly Constrained Environments

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.

An Agent-Based Approach to Aircraft Conflict Resolution with Constraints

We formulated a cubic spline trajectory planner with arc-length parameter which is estimated by summing up to the 3rd order in Taylor's expansion. The PVAJT motion planning is presented to reduce trajectory calculation time at every cycle time of servo control loop so that it is able to generate cubic spline trajectory in real time. These method can be used to more complex spline trajectory. We executed 3 case studies with different values of Tc and Ts, and showed the advantages of PVAJT motion planner. DSP-based motion controller is designed to implement The PVAJT motion planning.

The recovery of functional use of the upper extremity in patients with hemiparesis has been a longstanding struggle for patients and therapists. Assistive and robotic therapies are proving to be tools with great potential to assist in the rehabilitation process of patients. With respect to traditional rehabilitation interventions, robotic systems can provide more intensive physiotherapy and new interventions have come to be used to aid the rehabilitation of patient with certain movement restrictions, especially after stroke. The present work aims to show a study on trajectory planning of a robotic arm, which will be suggested to the patient in order to perform the movement for rehabilitation. In addition, it is intended that the trajectory can be update in real time to better meet the patients movement requirements and restrictions. In view of the requirement to generate trajectory in real time, there is a need for a simple model of the manipulators workspace to be used as limit and boundary condition for the task space.In parallel, there is the development of a prototype of the REHABOT robot, using parts of the Cyton Gamma 1500 robot as a base.

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

<p style='text-indent:20px;'>Aiming at the energy management problem of unmanned aerial vehicles (UAVs) in the terminal area energy management (TAEM) phase, a real-time online trajectory planning and guidance strategy based on judging energy is proposed. The trajectory planning strategy estimates the flight profile online in real time by judging the energy and changing the radius of the heading alignment circle. In addition, guidance instructions are also obtained at once. In the S-turn stage, the lateral guidance adopts a closed loop control mode with a fixed bank angle. In the remaining stage, the lateral guidance adopts a closed loop control mode for tracking the azimuth angle. In all stages, the longitudinal guidance adopts a closed loop control mode for tracking the flight path angle and flight height. The trajectory planning strategy is able to quickly generate a reference trajectory for testing cases with large variations not only in the initial energy but also in the energy of the flight process. The simulation results show that the proposed trajectory planning and guidance strategy can effectively manage a UAV's energy in the TAEM phase, ensuring that the UAV lands safely.</p>

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.

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.

The issues about maneuvering target track prediction were discussed in this paper. Firstly, using Kalman filter which based on current statistical model describes the state of maneuvering target motion, thereby analyzing time range of the target maneuvering occurred. Then, predict the target trajectory in real time by the improved gray prediction model. Finally, residual test and posterior variance test model accuracy, model accuracy is accurate.

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.