Virtual Target Research Articles

Research of integrated spiral maneuvering and guidance based on virtual target

To address the issue of interception, maneuvering, and precise guidance during the final stage of gliding flight, a study was conducted on the design of integrated maneuvering and guidance using a spiral trajectory as the focus. Firstly, the spiral trajectory was decomposed into an optimal reference trajectory considering the glide angle constraint and a relative motion trajectory. Then, virtual circular moving targets were introduced to meet the requirements of spiral maneuvering and virtual trajectory moving targets were introduced to ensure trajectory convergence, based on the task division of different flight stages. Finally, the guidance rate of the reference trajectory was designed based on optimal control theory, and the guidance rate of the spiral maneuver was designed based on pure proportional guidance. Simulation results show that this guidance method can enable the vehicle to perform active spiral maneuvers, and control the landing deviation and impact angle deviation to within 2.5 m and 1 degree, respectively.

Visuomotor strategies for collision avoidance in athletes: Insights from virtual reality

This study explores visuomotor control in athletes for collision avoidance using virtual reality. Thirty-nine athletes navigated dynamic scenarios, pursuing a virtual target while avoiding two to five virtual defenders. Confirmatory Factor Analysis validated a model that captured two features of scanning behavior based on head movements recorded during activity: the overall amount and temporal pattern. Linear mixed models showed that these features significantly differentiated successful from unsuccessful defender avoidance (p < .05), suggesting that efficient environmental scanning is crucial for collision avoidance while highlighting the potential of visuomotor interventions to reduce collision-related sport injuries.

Clutter resilience via auditory stream segregation in echolocating greater mouse-eared bats.

Bats use echolocation to navigate and hunt in darkness, and must in that process segregate target echoes from unwanted clutter echoes. Bats may do this by approaching a target at steep angles relative to the plane of the background, utilizing their directional transmission and receiving systems to minimize clutter from background objects, but it remains unknown how bats negotiate clutter that cannot be spatially avoided. Here, we tested the hypothesis that when movement no longer offers spatial release, echolocating bats mitigate clutter by calling at lower source levels and longer call intervals to ease auditory streaming. We trained five greater mouse-eared bats (Myotis myotis) to land on a spherical loudspeaker with two microphones attached. We used a phantom-echo setup, where the loudspeaker/target transmitted phantom clutter echoes by playing back the bats' own calls at time delays of 1, 3 and 5 ms with a virtual target strength 7 dB higher than the physical target. We show that the bats successfully landed on the target, irrespective of the clutter echo delays. Rather than decreasing their source levels, the bats used similar source level distributions in clutter and control trials. Similarly, the bats did not increase their call intervals, but instead used the same distribution of call intervals across control and clutter trials. These observations reject our hypothesis, leading us to conclude that bats display great resilience to clutter via short auditory integration times and acute auditory stream segregation rather than via biosonar adjustments.

Methodology and accuracy evaluation of global calibration for Multi-Line-Scan camera system in rail transit tunnel

Vision-based inspection techniques have improved the efficiency of tunnel inspection. This paper presents a rail transit tunnel multi-line-scan camera inspection system that requires global calibration to obtain the spatial relationships among all line-scan cameras, which is crucial for tunnel panoramic image mosaicking and 3D reconstruction. A flexible global calibration method is proposed, a hybrid calibration target is designed that includes triangular patterns based on the cross-ratio(CR) invariance principle and target holes. Establishing a virtual target field and using coplanarity as a constraint to refit a new view plane and globally optimizing the initial coordinates, which would ensure the calibration accuracy. In addition, an accuracy evaluation method based on CR triangles is proposed to assess the calibration results. The experimental results demonstrate the feasibility and reliability of the proposed calibration method and that the developed accuracy evaluation method is feasible compared with the traditional reprojection error method.

Calibration method for binocular vision system with large field of view based on small target image splicing

In vision measurement, camera calibration has a significant impact on measurement precision. The classical target-based calibration methods require the target to occupy more than one-third of the field of view. A small-size target that does not meet the requirements results in poor calibration accuracy, while an appropriate large-size target is difficult to manufacture and inconvenient to operate. In view of the above problem, we propose a flexible and accurate calibration method based on small target image splicing to calibrate the binocular vision system with a large field of view. The spliced images and virtual large targets are constructed to extend the target size, providing better flexibility for calibration. Moreover, an optimization objective function integrating two constraints in the imaging plane and measurement space is presented to improve the calibration accuracy during the parameter optimization process. The simulation experiments and actual experiments are carried out to test the performance of the proposed method. The results demonstrate that the calibration accuracy of the proposed method using a small target is equivalent to that of Zhang’s method using a large target. Additionally, when using a same-size target, the parameter error of the proposed method is less than that of Zhang’s method, and the proposed method reduces the distance measurement error from 1.169 mm to 0.208 mm compared to Zhang’s method.

Transfer accuracy of 3D printed versus CAD/CAM milled surgical guides for temporary orthodontic implants: A preclinical micro CT study

ObjectivesTemporary anchorage devices (TADs) have become an integral part of comprehensive orthodontic treatments. This study evaluated the transfer accuracy of three-dimensional (3D) printed and computer-aided design/computer-aided manufacturing (CAD/CAM) milled surgical guides for orthodontic TADs using micro-computed tomography (CT) imaging in a preclinical trial. MethodsOverall, 30 surgical guides were used to place TADs into typodonts; 3D printing and CAD/CAM milling were used to produce the guides. The virtual target positions of the TADs were compared to the real positions in terms of spatial and angular deviations using digital superimposition. Micro-CT imaging was used to detect the positions. To evaluate reliability, two investigators collected the measurements twice. Intra-rater and inter-rater correlations were tested. ResultsIn total, 60 palatal TADs were evaluated. The mean coronal deviations in the print group ranged from 0.15 ± 0.20 mm to 0.71 ± 0.22 mm, whereas in the mill group, they ranged from 0.09 ± 0.15 mm to 0.83 ± 0.23 mm. At the apical tip, the overall deviations in the print group ranged from 0.14 ± 0.56 mm to 1.27 ± 0.66 mm, whereas in the mill group, they ranged from 0.15 ± 0.57 mm to 1.09 ± 0.44 mm. The mean intra-class and inter-class correlation coefficients ranged from 0.904 to 0.987. No statistically significant differences were found between the groups. ConclusionsCAD/CAM milled guides yielded spatial and angular accuracies comparable to those of 3D printed guides with notable deviations in the vertical positioning of TADs. Clinical significanceDigital planning of orthodontic temporary implants combines clinical predictability and the safety of surrounding tissue. Therefore, the transfer accuracy of the guides is crucial. This preclinical study was the first to evaluate CAD/CAM milling for orthodontic guides and found its accuracy comparable to that of the current "gold standard".

Spiral scanning improves subject fixation in widefield retinal imaging.

Point scanning retinal imaging modalities, including confocal scanning light ophthalmoscopy (cSLO) and optical coherence tomography, suffer from fixational motion artifacts. Fixation targets, though effective at reducing eye motion, are infeasible in some applications (e.g., handheld devices) due to their bulk and complexity. Here, we report on a cSLO device that scans the retina in a spiral pattern under pseudo-visible illumination, thus collecting image data while simultaneously projecting, into the subject's vision, the image of a bullseye, which acts as a virtual fixation target. An imaging study of 14 young adult volunteers was conducted to compare the fixational performance of this technique to that of raster scanning, with and without a discrete inline fixation target. Image registration was used to quantify subject eye motion; a strip-wise registration method was used for raster scans, and a novel, to the best of our knowledge, ring-based method was used for spiral scans. Results indicate a statistically significant reduction in eye motion by the use of spiral scanning as compared to raster scanning without a fixation target.

Robust path-following guidance for an autonomous vehicle in the presence of wind

This paper presents a novel nonlinear guidance law for an autonomous unmanned aerial vehicle (UAV) that enables it to track any predefined path while taking into account wind effects. The approach involves treating the desired path as the trajectory of the virtual target, which the UAV tracks without relying on knowledge of the path's curvature. This makes the design applicable to paths with varying levels of complexity and smooth curvatures. Additionally, the proposed method draws inspiration from pursuit guidance, known for its simplicity and ease of implementation. Theoretical analysis ensures that the UAV will converge to the intended path within a fixed time, irrespective of its initial configuration relative to the path. Simulations demonstrate the effectiveness of the proposed method, showcasing the UAV's ability to accurately follow diverse curvilinear paths.

Added cognitive demand from increased sensorimotor complexity resulted in better goal-directed movement following stroke: a novel finding in post-stroke recovery

BACKGROUND: Stroke often causes impairments in goal-directed movements, which are commonly assessed using goal-directed reaching tasks. Here we present two individuals post-stroke who performed two robotic reaching tasks: 1. Visually Guided Reaching (VGR); reaching a cursor representing the fingertip to virtual targets, 2. Reverse Visually Guided Reaching (RVGR); where the cursor moves in the opposite direction of hand motion, requiring a novel cognitive rule. Participants are typically more successful at reaching targets in the simple VGR task compared to the more complex RVGR task (~92% of our database). The two cases are notable as they performed better on RVGR compared to VGR. CASE DESCRIPTIONS: Case 1: 80-year-old patient with left hemorrhagic thalamic stroke, with interventricular extension, presenting with right hemiparesis/reduced motor control. They were unable to complete reaches to any targets in VGR (0/40 targets), but were able to reach ~30% of the targets (15/48) in RVGR. Case 2: 76-year-old patient with a left ischemic pontine stroke, presenting with right hemiparesis/reduced motor control. They were unable to complete reaches to any targets in VGR (0/40) but were able to reach ~60% of the targets in RVGR (29/48). INTERPRETATION: Better performance in RVGR compared to VGR may be associated with a “dual-task benefit” and has potential clinical implications, including informing neuro-rehabilitative strategies, potentially by including tasks with added complexity or cognitive components. These findings also highlight the utility of robotic tools to provide novel environments within which to identify unique patterns of impairments and abilities.

Modeling and Path-Following Control for Dubins Dynamics in Complex State Space

A path-following control design in the complex state space for the Dubins vehicle is proposed. The vehicle dynamics are modeled as a bilinear system of two complex ordinary differential equations with a scalar complex control input. The developed control solution represents a feedback linearizing proportional–integral–derivative servomechanism, with real valued control gains applied to complex valued tracking error components. The design is performed within the model reference control framework. It is applied to solving path-following and waypoint routing problems for curves with bounded curvature and vehicles with bounded speeds. The path-following controller regulates the system turn rate and the reference model speed. The vehicle speed dynamics are independently maintained as desired. In that regard, the developed control policy forces the system position to track a virtual target point defined by the reference model. Verifiable sufficient conditions are formulated to enable steering the Dubins vehicle along the designated routes in the presence of unknown bounded environmental disturbances, such as wind and gust. Application examples of the developed path-following control solution to waypoint routing guidance for unmanned aerial platforms are discussed.

Dual perspective denoising model for session-based recommendation

Session-based recommendation predicts the next interaction option for an anonymous user based on his/her short-term behavior records. A key challenge faced by session-based recommendation is the processing of noise information. That is, the precise identification of noise items and noise connections. Existing research typically follows two assumptions: (1) the latest interaction item is directly treated as the user’s real-time preference; (2) the temporal order relationship between items is directly treated as the user’s true interaction intention conversion pattern. These strict assumptions ignore two facts: (1) the items that the user accidentally clicked on also exist in the interaction session. (2) the connection edges caused by noise items or user interaction intention drift are also fused into the explicit sequential structure. To address the above two issues, we propose a new Dual Perspective Denoising Model for session-based recommendation called DPDM. Specifically, DPDM mainly consists of two parts: Noise Item Recognition Network and Noise Structure Reconstruction Network. The Noise Item Recognition Network removes noise items from the session through a sparse attention mechanism and virtual target representation. The Noise Structure Reconstruction Network composed of Sequence Module and Co-occurrence Module is used to mine users’ real interaction intention conversion patterns. The Sequence Module captures sequence relationships after removing noise items. The Co-occurrence Module eliminates noise connections by optimizing explicit interaction sequences with L0 activation regularization. Extensive experiments were conducted on three representative real-world datasets, and DPDM achieved positive results, demonstrating the effectiveness of the components in the model.

Identification of Quality Markers in Schisandra chinensis (Turcz.) Baill. Using UPLC-Q-Extractive Orbitrap/MS, Chemometric Analysis, and Network Pharmacology

Chemical composition is a critical factor for determining the efficacy of any traditional Chinese medicine (TCM) and can be used as an indicator of commercial quality. To develop a new strategy for discovering potential quality markers (Q-markers) of TCM by integrating ultra-performance liquid chromatography-Q-extractive orbitrap/mass spectrometry (UPLC-Q-Extractive Orbitrap/MS), chemometric analysis, and network pharmacology, using Schisandra chinensis (Turcz.) Baill. (S. chinensis) as an example. The chemical profiling of S. chinensis was performed using UPLC-Q-Extractive Orbitrap/MS, followed by identification of hepatoprotective Q-markers through a comprehensive understanding of chemometric analysis and virtual target prediction of network pharmacology. Six compounds were considered potent candidates for Q-markers, which were identified as schisandrol A (6), angeloylgomisin H (10), schisantherin A (17), schisantherin B (18), schisandrin A (23), and schisandrin C (26). All Q-markers exhibited significant hepatoprotective activity, as evidenced by in vitro experiments. Subsequently, a method for simultaneous quantification was established and employed to analyse seven batches of S. chinensis. Therefore, the integrated approach of UPLC-Q-Extractive Orbitrap/MS, chemometrics, and network pharmacology proved to be an effective strategy for the discovery of Q-markers that can assist in assessing the overall chemical consistency of samples and provide a basis for quality evaluation of the material basis of S. chinensis.

Optimizing launch window opportunities for ESA's comet Interceptor mission using primer vector theory

Comet Interceptor (Comet-I) was selected in June 2019 as the first ESA F-Class mission and aims to explore a pristine comet, which will visit the inner Solar System for the first time. Comet-I will hitch a ride to a Sun-Earth L2 quasi-halo orbit, as a co-passenger in ESA's M4 ARIEL's launch. At the time of writing, this is scheduled for 2029. It will then remain there awaiting the right departure conditions to leave definitively and intercept a newly discovered comet. Comet-I will be the first mission to be designed and launched without an already identified target. Given the latter, the Comet-I Target Identification Working Group is tasked with the continuous follow-up of newly discovered comets, to analyse their characteristics as virtual targets for Comet-I. The goal of the paper is to inform the decision-making process that will eventually be necessary to commit Comet-I spacecraft to its final target.Within this context, this paper presents a trajectory optimization process based on Primer Vector theory that considers intercept trajectories with multiple revolutions and multiple deep space manoeuvres (DSM). The process is applied to 21 long period comets, which were discovered since July 1, 2020 and whose characteristics are of interest for the Comet-I mission. In fact, one of the latest of these recently discovered comets, C/2023H2, is the most accessible virtual target discovered at the time of writing, with delta-V intercept costs well within Comet-I capabilities. A complete launch window analysis is thus presented for C/2023H2, which accounts not only for the most optimal delta-V intercept transfer, but also considers all launch options that satisfy all Comet-I's current boundary conditions and flyby constraints. A systematic launch window analysis is made possible by a grid search approach on which departure and fly-by dates are fixed and the remaining design variables, including the times for manoeuvres and their Δv impulse vector, are optimized via Primer Vector theory. This allows to understand the latest possible departure date, but also the sensitivity to departure and arrival dates of key encounter characteristics such as relative fly-by speed, the solar aspect angle and the Earth distance.

Transferable Virtual-Physical Environmental Alignment With Redirected Walking.

Several advanced redirected walking techniques have been proposed in recent years to improve natural walking in virtual environments. One active and important research challenge of redirected walking focuses on the alignment of virtual and physical environments by redirection gains. If both environments are aligned, physical objects appear at the same positions as their virtual counterparts. When a user arrives at such a virtual object, she can touch the corresponding physical object providing passive haptic feedback. When multiple transferable virtual or physical target positions exist, the alignment can exploit multiple options, but the process requires more complicated solutions. In this paper, we study the problem of virtual-physical environmental alignment at multiple transferable target positions, and introduce a novel reinforcement learning-based redirected walking method. We design a novel comprehensive reward function that dynamically determines virtual-physical target matching and updates virtual target weights for reward computation. We evaluate our method through various simulated experiments as well as real user tests. The results show that our method obtains less physical distance error for environmental alignment and requires fewer resets than state-of-the-art techniques.

Entering the Next Dimension: A Review of 3D User Interfaces for Virtual Reality

In the last decade, the use of virtual reality (VR) technologies has been increasing, as head-mounted display devices, such as the Meta Quest series, have become commercially accessible to everyday consumers. There are plenty of software applications developed for VR, ranging from games to serious training. Despite the fast-growing emergence of novel and natural gesture-based interface components, traditional windows, icons, menus, and pointer user interfaces still remain popular in these implementations. Therefore, navigating and interacting within 3-dimensional environments can still be challenging in virtual reality. This body of work will serve as a reference to the literature survey on 3-dimensional user interfaces and their implementations, and categorize them to reiterate a set of design recommendations for implementing 3-dimensional user interfaces in virtual reality. While questions revolve around the need for a 3-dimensional user interface if a 2-dimensional user interface suffices, we cannot ignore the capabilities and benefits offered when utilising 3-dimensional space. Unlike other work, this work proposes a novel metaphor-guided quadrant model to delineate the 3-dimensional user interface problem in an end-to-end manner. To achieve this, over 30 unique articles selected using a meta-analyses methodology were reviewed. The result highlights the dimensional semantics of the inputs and dimensional outcomes of the virtual targets. Notable reiterated design recommendations would match devices to interaction and then to the tasks in 3-dimensional environments, reducing the degrees of freedom, imposing constraints when possible, and not implementing one single best interaction technique. The results of this survey will be beneficial for virtual reality simulation designers or researchers attempting to determine the appropriate model when developing user interfaces for virtual reality content.

Large-scale comparison of machine learning methods for profiling prediction of kinase inhibitors

Conventional machine learning (ML) and deep learning (DL) play a key role in the selectivity prediction of kinase inhibitors. A number of models based on available datasets can be used to predict the kinase profile of compounds, but there is still controversy about the advantages and disadvantages of ML and DL for such tasks. In this study, we constructed a comprehensive benchmark dataset of kinase inhibitors, involving in 141,086 unique compounds and 216,823 well-defined bioassay data points for 354 kinases. We then systematically compared the performance of 12 ML and DL methods on the kinase profiling prediction task. Extensive experimental results reveal that (1) Descriptor-based ML models generally slightly outperform fingerprint-based ML models in terms of predictive performance. RF as an ensemble learning approach displays the overall best predictive performance. (2) Single-task graph-based DL models are generally inferior to conventional descriptor- and fingerprint-based ML models, however, the corresponding multi-task models generally improves the average accuracy of kinase profile prediction. For example, the multi-task FP-GNN model outperforms the conventional descriptor- and fingerprint-based ML models with an average AUC of 0.807. (3) Fusion models based on voting and stacking methods can further improve the performance of the kinase profiling prediction task, specifically, RF::AtomPairs + FP2 + RDKitDes fusion model performs best with the highest average AUC value of 0.825 on the test sets. These findings provide useful information for guiding choices of the ML and DL methods for the kinase profiling prediction tasks. Finally, an online platform called KIPP (https://kipp.idruglab.cn) and python software are developed based on the best models to support the kinase profiling prediction, as well as various kinase inhibitor identification tasks including virtual screening, compound repositioning and target fishing.

Trajectory prediction-based guidance law

This paper proposes a guidance law based on trajectory prediction, aiming to address the difficulty of traditional guidance laws in meeting high-speed and highly maneuverable vehicles. The unscented Kalman filtering (UKF) technique is employed to estimate the target’s motion and predict the virtual impact point using the Singer model and measuring model. The midcourse guidance law is applied to the virtual target, taking into account the constraint of the intersection angle, while the terminal guidance utilizes modified proportional guidance. To mitigate the overload chattering in the transition sections of both midcourse and terminal guidance, the distance is used to modify the transition section of the terminal guidance. Simulation results demonstrate that the proposed guidance law effectively reduces both the encounter angle and the required maneuvering. Furthermore, to minimize midcourse guidance errors, the prediction results of the virtual target are continually updated during the trace process. This method can also be applied to trail other highly maneuverable targets.

Balloon Dilatation for Bronchoscope Delivery in a Swine Model: A Novel Technique for Ultra-Peripheral Lung Field Access and Accurate Biopsy.

In transbronchial biopsy of peripheral pulmonary lesions, the bronchoscope can reach only a limited depth due to the progressive narrowing of bronchi, which may reduce the diagnostic rate. This study examined the balloon dilatation for bronchoscope delivery (BDBD) technique, employing a novel balloon device to enhance bronchoscopy into the peripheral lung areas. Anaesthetised swine served as our primary model. Using computed tomography (CT) scans, we positioned virtual targets characterised by a positive bronchus sign and a diameter of 20 mm beneath the pleura. The bronchoscope was navigated along the pathways determined from the CT images. We performed balloon dilatation when bronchial narrowing obstructed progress to assess whether balloon dilatation would enable the bronchoscope to enter further into the periphery. We established 21 virtual targets on the CT scans. An average of 12.1 branches were identified along the pathways on the CT scans; however, bronchoscopy without BDBD only allowed access to an average of 6.7 branches. Based on 72 balloon dilatations with 3.0-mm or 4.0-mm ultra-thin bronchoscopes, there was an average increased access of 3.43 and 5.14 branches per route, respectively, with no significant BDBD complications. The bronchoscope was able to reach the planned location along all pathways, and the mean final bronchoscopic endpoints were at an average distance of 14.7 mm from the pleura. Post-procedure CT confirmed biopsy accuracy. The BDBD technique can enhance access of a flexible bronchoscope into the peripheral lung fields, which could potentially allow more accurate transbronchial interventions for peripheral targets.

Robot kinematics analysis and trajectory planning based on artificial potential field method

Abstract In order for a robot to complete a given task, it must first be made to autonomously reach a specified target location, so optimizing the robot path trajectory planning is a prerequisite for the use of robots. In this paper, for the two problems of the traditional artificial potential field method of target unreachable and local optimization, we first improve the repulsive potential field function so that the robot’s gravitational force and repulsive force are zero at the target position and then construct the robot kinematic analytical model by setting the virtual target point away from the local minimum value point. On this basis, the improved algorithm is used to compare simulation experiments in three environments with the real trajectory planning test in the showroom. In the climate “near the obstacle of the target point” and the pure U-shaped area environment, the robot of the traditional APF algorithm cannot reach the target point. However, the algorithm in this paper reaches the target point in all three environments, with a time taken of only 21, 33, and 42 seconds, respectively. In the real trajectory planning of the showroom, the improved algorithm in this paper reaches the target point quickly and accurately, with a total path trajectory length of 77.7835m, a time of 43 seconds, and a total turning angle of 793°. This paper provides an effective method for planning robot path trajectories in a complex and variable environment.

Longitudinal Control for Cruise Control Features in Vehicle Automation

This research focuses on longitudinal control methods implemented in Cruise Control (CC) and Adaptive Cruise Control (ACC) systems, as well as the mechanism for switching between these two modes. Two primary longitudinal control methods are examined: Constant Space Headway Control, which maintains a constant distance between vehicles, and Constant Time Headway Control, which maintains a constant time gap between vehicles.&#x0D; The noteworthy feature of this research focuses on incorporating a switching mechanism between Cruise Control and Adaptive Cruise Control through the utilization of the virtual target method.