Real-Time Optimization for Kites

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.

This paper presents a systematic method for generating optimal helicopter flight trajectories in real time for cockpit display. Helicopter flights in the event of a single engine failure are critical, and display of optimal flight paths on-board is desirable. Due to the large number of states and constraints, however, numerical solutions on a mainframe computer can take a long tune. In this paper, optimal solutions are first obtained off-line using a ground computer. For a given set of initial helicopter states and flight parameters, optimal solutions are fitted by a Fourier series. The coefficients of the Fourier approximations are then interpolated as functions of initial flight states and parameters. The approximate optimal trajectories can be quickly generated on-board using the proposed method. Introduction Engine failure is a major safety issue for all helicopters, especially during takeoff and landing operations. Due to the complexity of the situation and the dependence of safety on many parameters, optimal flight trajectories can provide useful guidance for pilot operation. In particular, on-board display of optimal trajectories is desirable. This paper presents a method for generating optimal flight trajectories in real-tune that can be used for on-board display. Depending on its capability and performance during an engine failure, a transport helicopter can be certified as Category A or Category B. The Federal Aviation Administration (FAA) grants Category B certification to a single or multi-engine helicopter with gross weight less than 20,000 Ib and capable of landing safely in case of one or all engine failures. Category A certification, on the other hand, is granted to multi-engine helicopters with independent engine systems. Category A helicopters must be able to either land safely or continue flight in case of One Engine Inoperation (OEI). This makes Category A helicopters uniquely suited for remote places where emergency landing sites are not available. I Ph.D. Candidate, Center for Control Sciences, Member AIAA * Assistant Professor, Dept. of Aerospace Eng. and Mechanics, Member AIAA + Civil Rotorcraft Group Leader, Rotorcraft and Powered Lift Branch The decision to continue the flight or to land represents an energy management problem. It has been shown''' that this decision depends on initial conditions at the time of failure as well as flight parameters. Due to the numerous initial variables involved in this decision, a method to communicate all this information to the pilot instantaneously, at engine failure, is needed. This method must keep the emergency procedure simple and manageable. In particular, on-board cockpit display of optimal flight trajectories is useful. This requires a reliable real-tune solution of optimal trajectories, on board, with limited time, limited computing power, and limited memory available. If possible, an analytical solution of optimal trajectories is desirable. Unfortunately, this can only be achieved for systems of low order and where the equations of motion and the cost function are simple. Therefore, for a complex system, an open-loop numerical procedure becomes necessary. Research efforts were made to solve optimal control problems in real-time. Schultz et al. used calculus of variation and energy state method to generate optimal trajectories on-board for the National Aerospace Plane (NASP). In his study, the equations of motion of the NASP are less complex than those of the helicopter, and an interval of time of few minutes is allowed. Bryson used neighboring control theory to generate near-optimal solutions that could be used in real-time. This method is elegant but requires a large amount of memory to store the needed perturbation matrices. Also, it is valid on a relatively small neighborhood of the nominal trajectory. Perturbation methods and table lookups are often used if only few parameters are involved. In the current problem, the equations of motion of the helicopter are complex and too many path equality and inequality constraints are present. Also, limitations on tune, computing power, and memory available make all the above methods unsuitable for this problem. This paper proposes a technique of real-time optimal trajectory generation based on approximation and

Real-time trajectory optimization for collision-free asteroid landing based on deep neural networks

In this paper, a real-time optimizing controller is developed to steer a differentially flat nonlinear control system to closed-loop trajectories that optimize a cost functional of interest. An interior point optimization method with penalty function is used to formulate a real-time optimization scheme. The problem is posed as a real-time optimal trajectory generaction problem in which the optimal trajectories are computed using an adaptive extremum-seeking approach. A fed-batch bioreactor simulation example is used to demonstrate the application of the technique to a finite time dynamic optimization problems that arise in batch process control.

Real-time dynamic optimization of nonlinear systems: A flatness-based approach

An Agent-Based Approach to Aircraft Conflict Resolution with Constraints

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

The model-based real-time optimization (MRTO) of heating, ventilation, and air-conditioning (HVAC) systems is an efficient tool for improving energy efficiency and for effective operation. Model-based real-time optimization of HVAC systems needs to regularly optimize the set points for local-loop operation, taking into account the interactions between HVAC components with the help of system-performance models. MRTO relies on the accuracy of the performance model to provide reliability in decision making. In practice, due to high diversity in ambient conditions and load demands, system-model mismatches are difficult to avoid. This paper presents an adaptive, model-based, real-time optimization (AMRTO) approach for large-scale, complex HVAC systems, to counter any model mismatches by updating the performance model in real time with real-time measurements. Furthermore, to make this approach practically applicable and to keep the online training process computationally manageable, an empirical-physical model of HVAC system components was set up that is suitable for online training, and hybrid genetic algorithms (HGAs) method was used for faster, yet reliable, online training of the performance model. A case study was used to evaluate the performance of the proposed approach. The results demonstrated that the proposed AMRTO was able to provide energy saving approximately 8% and reduce the online computational burden by 99%.

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.