Dynamic Tracking Research Articles

Wireless Sensor Network Predictions of Dynamic Target Tracking Algorithm for Error Tolerance

The use of wireless sensor networks (WSNs) in tracking moving objects has increased significantly in recent years. Target node coverage, a criterion for choosing the right node or nodes for monitoring moving targets, is described in this research utilizing the proposed algorithm that is based on the combined genetic algorithm and MTR (Multiple Target Research). The suggested approach combines factors according to a basic strategy, and while choosing the right node, better attributes are taken into account. The suggested approach determines which node or nodes are suitable for covering and tracking network objectives based on their degree of overlap, distance to the well, and best remaining energy. Criterion for selecting the appropriate node or nodes for tracking moving targets are presented in the form of target node coverage. In the proposed method, parameters are combined based on a fundamental strategy, and superior characteristics are regarded when selecting the appropriate node. Fewer nodes are positioned in the appropriate coverage region, according to the results. Furthermore, given that the communication

Advanced Servo Control and AI Integration in 3-DoF Platforms for Enhanced Simulation Interactivity

This paper proposes a new approach to enhance the realism and interactivity of shooting simulation systems by integrating a three-degree–of–freedom (3-DoF) platform with sensory and interactive elements, as well as digital content. The system employs visual effects computers and servo controls, utilizing network packet messages for communication based on different scene definitions. When the control handle sends commands, the visual effects computer transmits control parameters to the image generator. Additionally, AI-controlled aircrafts act as enemy planes, autonomously determining flight paths, tracking targets, and engaging in combat, thereby enhancing realism in interactive mechanisms. An iterative learning control (ILC) is designed to provide the platform with good dynamic response, load capacity, and tracking ability when operated by a manual control handle. The core control uses a TMS320F28377D digital signal processor from Texas Instruments, integrated with visual effects computers for three-axis control, controller computation, finite state machines, and network communication operations. Experimental results demonstrate the feasibility and effectiveness of the developed three-axis shooting platform, achieving immersion and coordination with AI enemy aircrafts.

Dynamic Modeling and Tracking Of 3-Wheels Omni-Directional Mobile Robot with Manual PID Tuning Method

This paper presents the dynamic modelling and tracking control of a holonomic 3-wheel omni-directional mobile robot. A proportional integral derivative (PID) controller is used, and its performance depends on the dynamic model's accuracy. The controller design for the switched non-linear system is based on both a dynamic model and a kinematic design. A proposed dynamic model averages the contact radius of the 3-wheels. The resultant dynamic model, on average, is non-linear and smooth, serving as a potential solution for model-based control design. The closed-loop simulation results clearly illustrate the dynamic characteristics of the 3-wheels omni-directional mobile robot, offering valuable insights into its behaviour for further analysis and optimisation. Comparative simulation results using MATLAB are presented and demonstrate the effectiveness and legitimacy of the control mechanism. Keywords: Holonomic, kinematic, dynamic model, PID controller, non-linear, omni-directional robot

Kirigami enabled reconfigurable three-dimensional evaporator arrays for dynamic solar tracking and high efficiency desalination.

A kirigami-engineered composite hydrogel membrane is exploited for the construction of three dimensional (3D) solar-tracking evaporator arrays with outstanding evaporation performance and salt tolerance. The hybrid nanofiber network in the hydrogel membrane offers favorable water transport dynamics combined with excellent structural robustness, which are beneficial for the engineering of 3D dynamic structures. Periodic triangular cuts patterned into the membrane allow formation and reconfiguration of 3D conical arrays controlled by uniaxial stretching. With these structures, the tilt angles of the membrane surface are actively tuned to follow the solar trajectory, leading to a solar evaporation rate ~80% higher than that of static planar devices. Furthermore, the tapered 3D flaps and their micro-structured surfaces are capable of localized salt crystallization for prolonged solar desalination, enabling a stable evaporation rate of 3.4 kg m-2 hour-1 even in saturated brine. This versatile design may facilitate the implementation of solar evaporators for desalination and provide inspirations for other soft functional devices with dynamic 3D configurations.

Predefined‐time adaptive neural dynamic surface tracking control for high‐order nonlinear switched systems

Predefined‐time adaptive neural dynamic surface tracking control for high‐order nonlinear switched systems

Tracking single hiPSC-derived cardiomyocyte contractile function using CONTRAX an efficient pipeline for traction force measurement

Cardiomyocytes derived from human induced pluripotent stem cells (hiPSC-CMs) are powerful in vitro models to study the mechanisms underlying cardiomyopathies and cardiotoxicity. Quantification of the contractile function in single hiPSC-CMs at high-throughput and over time is essential to disentangle how cellular mechanisms affect heart function. Here, we present CONTRAX, an open-access, versatile, and streamlined pipeline for quantitative tracking of the contractile dynamics of single hiPSC-CMs over time. Three software modules enable: parameter-based identification of single hiPSC-CMs; automated video acquisition of >200 cells/hour; and contractility measurements via traction force microscopy. We analyze >4,500 hiPSC-CMs over time in the same cells under orthogonal conditions of culture media and substrate stiffnesses; +/− drug treatment; +/− cardiac mutations. Using undirected clustering, we reveal converging maturation patterns, quantifiable drug response to Mavacamten and significant deficiencies in hiPSC-CMs with disease mutations. CONTRAX empowers researchers with a potent quantitative approach to develop cardiac therapies.

A Dynamic Entropy Approach Reveals Reduced Functional Network Connectivity Trajectory Complexity in Schizophrenia.

Over the past decade and a half, dynamic functional imaging has revealed low-dimensional brain connectivity measures, identified potential common human spatial connectivity states, tracked the transition patterns of these states, and demonstrated meaningful transition alterations in disorders and over the course of development. Recently, researchers have begun to analyze these data from the perspective of dynamic systems and information theory in the hopes of understanding how these dynamics support less easily quantified processes, such as information processing, cortical hierarchy, and consciousness. Little attention has been paid to the effects of psychiatric disease on these measures, however. We begin to rectify this by examining the complexity of subject trajectories in state space through the lens of information theory. Specifically, we identify a basis for the dynamic functional connectivity state space and track subject trajectories through this space over the course of the scan. The dynamic complexity of these trajectories is assessed along each dimension of the proposed basis space. Using these estimates, we demonstrate that schizophrenia patients display substantially simpler trajectories than demographically matched healthy controls and that this drop in complexity concentrates along specific dimensions. We also demonstrate that entropy generation in at least one of these dimensions is linked to cognitive performance. Overall, the results suggest great value in applying dynamic systems theory to problems of neuroimaging and reveal a substantial drop in the complexity of schizophrenia patients' brain function.

Kalman Filter for a Particular Class of Dynamic Object Images

We discuss the problem of estimating the state of a dynamic object by using observed images generated by an optical system. The work aims to implement a novel approach that would ensure improved accuracy of dynamic object tracking using a sequence of images. We utilize a vector model that describes the object image as a limited number of vertexes (reference points). Upon imaging, the object of interest is assumed to be retained at the center of each frame, so that the motion parameters can be considered as projections onto the axes of a coordinate system matched with the camera's optical axis. The novelty of the approach is that the observed parameters (the distance along the optical axis and angular attitude) of the object are calculated using the coordinates of specified points in the object images. For estimating the object condition, a Kalman-Bucy filter is constructed on the assumption that the dynamic object motion is described by a set of equations for the translational motion of the center of mass along the optical axis and variations in the angular attitude relative to the image plane. The efficiency of the proposed method is illustrated by an example of estimating the object's angular attitude.

Robotic assessment of patella tracking in total knee arthroplasty

ObjectivesRobotic tools have been developed to improve planning, accuracy and outcomes in total knee arthroplasty (TKA). The purpose of this study was to describe and illustrate a novel technique for assessing the patellofemoral (PFJ) in TKA using an imageless robotic platform. MethodsA consecutive series of 30 R-TKA was undertaken by a single surgeon utilising the described technique. A technique to dynamically assess the PFJ intra-operatively, pre- and post-implantation was developed. A full set of data from 9 cases was then collected and reviewed for analysis. A series of dynamic PFJ tracks collected intra-operatively pre- and post-implantation are presented. Furthermore, a full assessment of PFJ over and under-stuffing through a 90° arc of flexion is illustrated. Finally, a pre- and post-centre of rotation for the PFJ was defined and measured. ResultsThe described technique was defined over a series of 30 R-TKA using the described robotic platform. Nine cases were analysed to determine what data could be measured using the robotic platform. Intra-operative real-time data allowed a visual assessment of PFJ tracking through a range of motion of 0°–90° flexion pre- and post-implantation. PFJ over and under-stuffing was also assessed intra-operatively through a range of motion of 0°–90° flexion. Post-operative analysis allowed a more detailed study to be performed, including defining a pre- and post-implantation centre of rotation (COR) for the patella. Defining the COR allowed the definition of a patella plane. Furthermore, patella mediolateral shift in full extension, and end flexion could be measured. ConclusionIntra-operative assessment of the PFJ in TKA is challenging. Robotic tools have been developed to improve measurement, accuracy of delivery and outcomes in TKA. These tools can be adapted in novel ways to assess the PFJ, which may lead to further refinements in TKA techniques.

Internal Temperature Estimation of Lithium Batteries Based on a Three-Directional Anisotropic Thermal Circuit Model

In order to improve the accuracy of internal temperature estimation in batteries, a 10-parameter time-varying multi-surface heat transfer model including internal heat production, heat transfer and external heat transfer is established based on the structure of a lithium iron phosphate pouch battery and its three directional anisotropic heat conduction characteristics. The entropy heat coefficient, internal equivalent heat capacity and internal equivalent thermal resistance related to the SOC and temperature state of the battery were identified using experimental tests and the least square fitting method, and were then used for online calculation of internal heat production and heat transfer in the battery. According to the time-varying and nonlinear characteristics of the heat transfer between the surface and the environment of the battery, an internal temperature estimation algorithm based on the square root cubature Kalman filter was designed and developed. By iteratively calculating the estimated surface temperature and the measured value, dynamic tracking and online correction of the internal temperature of the battery can be achieved. The verification results using FUDS and US06 dynamic working condition data show that the proposed method can quickly eliminate the influence of initial temperature deviations and accumulated process errors and has the characteristics of a high estimation accuracy and good robustness. Compared with the estimation results of the adaptive Kalman filter, the proposed method improves the estimation accuracy of FUDS and US06 working conditions by 67% and 54%, respectively, with a similar computational efficiency.

An artificial potential field approach for event-triggered cooperative control of multiple high-speed trains with free initial states

ABSTRACT In this paper, a multi-algorithm-fusion-based cooperative tracking control strategy is proposed for high-speed trains to achieve the control objectives of reference speed trajectory tracking and adjacent safety distance maintenance simultaneously. First, the multiple high-speed train (MHST) system is formulated as a multi-agent system (MAS) with a virtual leader. Second, a coordination controller that considers the dynamic tracking performance and input saturation is designed by combining the MAS leader-follower consensus algorithm and the improved artificial potential field (APF) method. In addition, within the train-to-train (T2T) communication network, an event-triggered mechanism is introduced in the continuous state transmission process to address the communication channel capacity protection limitation issue. Third, the closed-loop stability of the MHST system is ensured by the comprehensive analysis of a novel log-type Lyapunov function. Finally, the effectiveness of the proposed event-triggered cooperative tracking control strategy is verified through a numerical example of the MHST system on a typical line.

An improved BAT algorithm for collaborative dynamic target tracking and path planning of multiple UAV

It is well known that multi-UAV cooperative dynamic target path planning is a challenging field. In this field, multi-UAV cooperative dynamic target path planning is very important to achieve efficient task completion. However, the existing algorithms have some limitations in solving the problems of insufficient search range, premature convergence, local optimization and insufficient population diversity. In order to solve these problems and improve the efficiency and accuracy of path planning, this paper proposes an innovative method. Firstly, we use A* algorithm to obtain the initial assignment result of UAV target, and then use Hungarian algorithm and genetic algorithm to optimize the assignment result and expand the target assignment range. Secondly, in the path planning stage, the bat algorithm is improved, and sine function, dynamic expansion factor and nonlinear function are introduced to solve the problems of insufficient search range, premature convergence and local optimization. At the same time, genetic algorithm is used to solve the problem of insufficient population. By optimizing target assignment and path planning, the performance of multi-UAV cooperative system is improved, so as to better adapt to the task requirements in dynamic environment and provide more reliable solutions for UAV applications. The experimental results show that the tracking and path planning are more effective than BA, CCWOA and OUMPOA under the condition of good stability. Compared with BA, the fitness function and convergence speed are improved by 34.64% and 30.29% respectively.

Evaluating the Kinetics and Molecular Mechanism for Biomimetic Metabolic Activation of PAHs by Surface-Enhanced Raman Scattering Spectroscopy.

Biomimetic cytochrome P450 for chemical activation of environmental carcinogens is an efficient in vitro model for evaluating their mutagenicity and ultimately acquiring the metabolites that cannot be easily accessed by conventional routes of organic synthesis. Different kinds of mutagen derived from polyaromatic hydrocarbons (PAHs) by metalloporphyrin/oxidant model systems have been reported, but the underlying molecular mechanisms are poorly understood. Herein, we have for the first time demonstrated an effective surface-enhanced Raman scattering (SERS) protocol to study the dynamics and biomimetic metabolic behaviors of pyrene (Pyr) in the presence of various oxygen donors. Quantitative information on the relative concentration of Pyr and its metabolites in the biomimetic system can be extracted from the SERS spectra. On the basis of our results, we conclude that the oxidative metabolism of Pyr is highly influenced by the types and concentrations of oxygen donors, leading to the formation of 1-hydroxypyrene and dioxygenated products. Besides, the addition of an appropriate amount of an organic solvent can promote the formation of secondary oxidation products. These results offer valuable insights into the dynamics of PAHs metabolism and the regulation of their metabolic pathways in biomimetic activation. In comparison to traditional liquid chromatography-mass spectrometry, the present SERS approach is more suitable for high-throughput evaluation of the metabolic process and kinetics of PAHs. We anticipate that this approach will enable a more general and comprehensive tracking of metabolic dynamics and molecular mechanisms involved in the biomimetic activation of other xenobiotics, such as procarcinogens, promutagens, and drugs.

Electrochemical and Plasmonic Detection of Myocardial Infarction Using Microfluidic Biochip Incorporated with Mesoporous Nanoscaffolds.

This paper reports a microfluidic device for the electrochemical and plasmonic detection of cardiac myoglobin (cMb) and cardiac troponin I (cTnI) with noticeable limits of detection (LoD) as low as a few picograms per milliliter (pg/mL) ranges, achieved in a short detection time. The device features two working electrodes, each with a mesoporous Ni3V2O8 nanoscaffold grafted with reduced graphene oxide (rGO) that improves the interaction of diffusing analyte molecules with the sensing surface by providing a high surface area and reaction kinetics. Electrochemical studies reveal sensitivities as high as 9.68 μA ng/mL and a LoD of 2.0 pg/mL for cTnI, and 8.98 μA ng/mL and 4.7 pg/mL for cMb. Additionally, the surface plasmon resonance (SPR) studies demonstrate a low-level LoD of 8.8 pg/mL for cMb and 7.3 pg/mL for cTnI. The dual-modality sensor enables dynamic tracking of kinetic antigen-antibody interactions during sensing, self-verification through providing signals of two modes, and reduced false readout. This study demonstrates the complementary nature of the electrochemical and SPR modes in biosensing, with the electrochemical mode being highly sensitive and the SPR mode providing superior tracking of molecular recognition behaviors. The presented sensor represents a significant innovation in cardiovascular disease management and can be applied to monitor other clinically important biomolecules.

High-Temperature Characteristics of Polyphosphoric Acid-Modified Asphalt and High-Temperature Performance Prediction Analysis of Its Mixtures

To promote the application of economical and sustainable polyphosphoric acid (PPA)-modified asphalt in road engineering, styrene-butadiene block copolymer (SBS), styrene-butadiene rubber (SBR), and PPA were used to prepare PPA/SBS and PPA/SBR composite-modified asphalts, which were tested and the data analyzed. Fourier transform infrared spectroscopy (FTIR) tests and thermogravimetric analysis (TG) tests were carried out to study the modification mechanisms of the composite-modified asphalts, and the high-temperature performance of the PPA-modified asphalt and asphalt mixtures was analyzed by dynamic shear rheology (DSR) tests and wheel tracking tests. A gray correlation analysis and a back-propagation (BP) neural network were utilized to construct a prediction model of the high-temperature performance of the asphalt and asphalt mixtures. The test results indicate that PPA chemically interacts with the base asphalt and physically integrates with SBS and SBR. The PPA-modified asphalt has a higher decomposition temperature than the base asphalt, indicating superior thermal stability. As the PPA dosage increases, the G*/sinδ value of the PPA-modified asphalt also increases. In particular, when 0.6% PPA is combined with 2% SBS/SBR, it surpasses the high-temperature performance achieved with 4% SBS/SBR, suggesting that PPA may be a good alternative for polymer modifiers. In addition, the creep recovery of PPA-modified asphalt is influenced by the stress level, and as the stress increases, the R-value decreases, resulting in reduced elastic deformation. Furthermore, the BP neural network model achieved a fit of 0.991 in predicting dynamic stability, with a mean percentage of relative error (MAPE) of 6.15% between measured and predicted values. This underscores the feasibility of using BP neural networks in predictive dynamic stability models.

Precise Obstacle Avoidance Movement for Three-Wheeled Mobile Robots: A Modified Curvature Tracking Method

This paper proposes a precise motion control strategy for a three-wheeled mobile robot with two driven rear wheels and one steered front wheel so that an obstacle avoidance motion task is able to be well implemented. Initially, the motion laws under nonholonomic constraints are expounded for the three-wheeled mobile robot in order to facilitate the derivation of its dynamic model. Subsequently, a prescribed target curve is converted into a speed target through the nonholonomic constraint of zero lateral speed. A modified dynamical tracking target that is aligned with the dynamic model is then developed based on the relative curvature of the prescribed curve. By applying this dynamical tracking target, path tracking precision is enhanced through appropriate selection of a yaw motion speed target, thus preventing speed errors from accumulating during relative curvature tracking. On this basis, integral sliding mode control and feedback linearization methods are adopted for designing robust controllers, enabling the accurate movement of the three-wheeled mobile robot along a given path. A theoretical analysis and simulation results corroborate the effectiveness of the proposed trajectory tracking control strategy in preventing off-target deviations, even with significant speed errors.

Design and analysis of tracking differentiator based on SO(3)

Motivated by the issue of insufficient dynamic performance and tracking accuracy in SO(3)-based attitude tracking differentiators during large-angle maneuvers and complex trajectory tracking, a novel design approach for a three-degree-of-freedom attitude tracking differentiator within the SO(3) framework is proposed by incorporating second-order system theory and Lie group theory and improving the classical tracking differentiator. The kinematics model and error dynamics model of a rigid body on SO(3) are derived, and a reasonable virtual control input on SO(3) is constructed subsequently in order to achieve better dynamic response and tracking performance. Simulation and experimental results validate that the designed tracking differentiator could realize rapid and smooth convergence during large-angle maneuvers, and the initial large tracking error rapidly drops to near zero in a short period of time; additionally, it can also track expected time-varying curves well in complex trajectory tracking, with initial errors rapidly decreasing and maintaining at normal levels, demonstrating excellent tracking and control capabilities. There are strong application prospects for this new approach in addition to its theoretical significance.

Developing a System on the Dynamics and Implications of Parental and School Tracking of Children's Activities and Academic Progress

This paper explores the dynamics and effects of the school and parent tracking system at École Secondaire Islamique de Gisenyi. The study addresses the problem of monitoring students' academic performance and attendance by examining the extent to which parents and the school utilize tracking mechanisms. Motivations, benefits, and challenges associated with parental and school tracking practices are explored, considering factors like privacy concerns, parental involvement, and student autonomy. The research objectives focus on understanding how the implementation of a tracking system can enhance student success and the overall educational experience through collaborative efforts between parents and the school. By investigating the impact of parental tracking, the study aims to strengthen the bond between parents and the school, fostering a more supportive and engaged learning environment. The methodology involves qualitative data collection methods, including interviews and observations, to provide a comprehensive understanding of the dynamics and effects of the tracking system. The expected results are based on the assumption that the implementation of a tracking system at École Secondaire Islamique de Gisenyi will positively impact student progress, parental involvement, and overall school dynamics. The study's findings aim to contribute to the broader discourse on educational monitoring practices, inform policy recommendations, and empower stakeholders to make informed decisions regarding student tracking in Rwandan secondary schools. In conclusion, this research sheds light on the dynamics and effects of the school and parent tracking system at École Secondaire Islamique de Gisenyi. The anticipated positive outcomes of implementing a robust and user-friendly tracking system include enhancing student success and fostering collaboration between parents and school staff. Keywords: parental tracking, school tracking, data collection, tracking practices.

DMOT-SLAM: visual SLAM in dynamic environments with moving object tracking

Visual simultaneous localization and mapping (SLAM) in dynamic environments has received significant attention in recent years, and accurate segmentation of real dynamic objects is the key to enhancing the accuracy of pose estimation in such environments. In this study, we propose a visual SLAM approach based on ORB-SLAM3, namely dynamic multiple object tracking SLAM (DMOT-SLAM), which can accurately estimate the camera’s pose in dynamic environments while tracking the trajectories of moving objects. We introduce a spatial point correlation constraint and combine it with instance segmentation and epipolar constraint to identify dynamic objects. We integrate the proposed motion check method into DeepSort, an object tracking algorithm, to facilitate inter-frame tracking of dynamic objects. This integration not only enhances the stability of dynamic features detection but also enables the estimation of global motion trajectories for dynamic objects and the construction of object-level semi-dense semantic maps. We evaluate our approach on the public TUM, Bonn, and KITTI dataset, and the results show that our approach has a significant improvement over ORB-SLAM3 in dynamic scenes and performs better compared to other state-of-the-art SLAM approaches. Moreover, experiments in real-world scenarios further substantiate the effectiveness of our approach.

A Local-and-Global Attention Reinforcement Learning Algorithm for Multiagent Cooperative Navigation.

The cooperative navigation algorithm is the crucial technology for multirobot systems to accomplish autonomous collaborative operations, and it is still a challenge for researchers. In this work, we propose a new multiagent reinforcement learning algorithm called multiagent local-and-global attention actor-critic (MLGA2C) for multiagent cooperative navigation. Inspired by the attention mechanism, we design the local-and-global attention module to dynamically extract and encode critical environmental features. Meanwhile, based on the centralized training and decentralized execution (CTDE) paradigm, we extend a new actor-critic method to handle feature encoding and make navigation decisions. We also evaluate the proposed algorithm in two cooperative navigation scenarios: static target navigation and dynamic pedestrian target tracking. The multiple experimental results show that our algorithm performs well in cooperative navigation tasks with increasing agents.