Standard Trajectory Research Articles

Three-dimensional coverage-based cooperative guidance law with overload constraints to intercept a hypersonic vehicle

This paper proposes a coverage-based cooperative guidance (CBCG) law to enable low-velocity and constrained-manoeuvrability interceptors to intercept a hypersonic vehicle, considering overload constraints. First, the reachable, feasible, and escape areas are considered to establish the guidance law under a nonlinear interception model. Second, the problem of intercepting the hypersonic vehicle is transformed into a coverage problem by using the coverage strategy. A guidance command including basis and bias terms is generated considering the standard trajectory. The basis term generates the guidance command for nonstandard trajectories to nullify the line-of-sight angular rate at the time of interception to ensure successful interception, and the bias term generates the guidance command for the standard trajectory to enhance the coverage ability of the interceptors. The optimal number and location constraints of the interceptors are theoretically analysed. The effectiveness, applicability, superiority, sensitivity, and robustness of the CBCG law are evaluated through a series of numerical simulations, and its performance is compared with that of the proportional navigation guidance law. The results demonstrate that the CBCG law can ensure that at least one interceptor successfully intercepts the hypersonic vehicle, with high robustness to errors and noise.

Innovation in engineering education using AR technology on the example of disciplines “Descriptive Geometry” and “Engineering Graphics”

Research relevance. With the use of computer-electronic-digital equipment, the achievements of scientific and technological progress become part of education. This obliges to improve the field of education: to change and create the forms and content of education, methods and approaches to the educational process, to increase the volume and quality of knowledge. Augmented reality favors autonomous learning by using mobile devices, simplifies the understanding and interpretation of geometric shapes and contributes to the development of students' spatial thinking. The research purpose is to form new knowledge, skills and abilities for the convergence of the real and digital worlds based on the possibility of using augmented reality technology, to determine the reasons that make it difficult for users to accept information technology as an educational tool. Materials and methods. Scientific papers published between 2013 and 2022 in the scientometric databases Scopus, Web of Science, Google Scholar, E-library were considered, pedagogical experience on the research topic was studied; various types of diagnostics of students were taken into account with summing up the outcomes and the results of theoretical and practical research were summarized. A practical study of the problem was carried out by creating a 3D model of octants and projections of points. Research results. An augmented reality application was created on the Unity 2018.4.12f1 platform with additional modules – Vuforia Augmented Reality Support and Android Build Support. When developing the application, the authors created 3D models of octants, projections of points in the SketchUp program. A survey on conscious understanding and mastering the descriptive geometry course showed that the majority of students showed positive dynamics in mastering the descriptive geometry course. Conclusion. As a result of using a specially developed application based on the augmented reality platform, it is possible to move from standardized educational trajectories to individual ones, which significantly increases the effectiveness of modes of study and restructuring of the educational process.

The ‘false hope’ argument in discussions on expanded access to investigational drugs: a critical assessment

When seriously ill patients reach the end of the standard treatment trajectory for their condition, they may qualify for the use of unapproved, investigational drugs regulated via expanded access programs. In medical-ethical discourse, it is often argued that expanded access to investigational drugs raises ‘false hope’ among patients and is therefore undesirable. We set out to investigate what is meant by the false hope argument in this discourse. In this paper, we identify and analyze five versions of the false hope argument which we call: (1) the limited chance at benefit argument, (2) the side effects outweighing benefits argument, (3) the opportunity costs argument, (4) the impossibility of making informed decisions argument, and (5) the difficulty of gaining access argument. We argue that the majority of these five versions do not provide normative ground for disqualifying patients’ hopes as false. Only when hope is rooted in a mistaken belief, for example, about the likelihood of benefits or chances on medical risks, or when hope is directed at something that cannot possibly be obtained, should it be considered false. If patients are adequately informed about their odds of obtaining medical benefit, however small, and about the risks associated with an investigational treatment, it is unjustified to consider patients’ hopes to be false, and hence, to deny them access to investigational drug based on that argument.

Pulmonary Function Trajectories in People with HIV: Analysis of the Pittsburgh HIV Lung Cohort.

Rationale: Human immunodeficiency virus (HIV) infection is associated with chronic lung disease and impaired pulmonary function; however, longitudinal pulmonary function phenotypes in HIV are undefined. Objectives: To identify pulmonary function trajectories, their determinants, and outcomes. Methods: We used data from participants with HIV in the Pittsburgh HIV Lung Cohort with three or more pulmonary function tests between 2007 and 2020. We analyzed post-bronchodilator forced expiratory volume in 1 second (FEV1), forced vital capacity (FVC), and FEV1/FVC, and diffusing capacity of the lung for carbon monoxide (DlCO) using group-based trajectory modeling to identify subgroups of individuals whose measurements followed a similar pattern over time. We examined the association between participant characteristics and trajectories using multivariable logistic regression. In exploratory adjusted analyses restricted to individuals with available plasma cytokine data, we investigated the association between 18 individual standardized cytokine concentrations and trajectories. We compared mortality, dyspnea prevalence, respiratory health status, and 6-minute-walk distance between phenotypes. Results: A total of 265 participants contributed 1,606 pulmonary function measurements over a median follow-up of 8.1 years. We identified two trajectories each for FEV1 and FVC: "low baseline, slow decline" and "high baseline, rapid decline." There were three trajectory groups for FEV1/FVC: "rapid decline," "moderate decline," and "slow decline." Finally, we identified two trajectories for DlCO: "baseline low" and "baseline high." The low baseline, slow decline FEV1 and FVC, rapid decline, and moderate decline FEV1/FVC, and baseline low DlCO phenotypes were associated with increased dyspnea prevalence, worse respiratory health status, and decreased 6-minute-walk distance. The baseline low DlCO phenotype was also associated with worse mortality. Current smoking and pack-years of smoking were associated with the adverse FEV1, FEV1/FVC, and DlCO phenotypes. Detectable viremia was the only HIV marker associated with the adverse DlCO phenotype. C-reactive protein and endothelin-1 were associated with the adverse FEV1 and FVC phenotypes, and endothelin-1 trended toward an association with the adverse DlCO phenotype. Conclusions: We identified novel, distinct longitudinal pulmonary function phenotypes with significant differences in characteristics and outcomes. These findings highlight the importance of lung dysfunction over time in people with HIV and should be validated in additional cohorts.

Collisional-model quantum trajectories for entangled-qubit environments

We study the dynamics of quantum systems interacting with a stream of entangled-qubit pairs in the weak coupling limit. For a large class of two-qubit bath states, we present a detailed framework describing conditional dynamical maps for the system, known as quantum trajectories, that arise when the qubits are measured. Depending on the measurement basis, these quantum trajectories can be jump-type or diffusive-type, and can successively transfer entanglement from the bath qubits other quantum systems. They also exhibit features not present in standard quantum optical trajectories due to the fact that collisional models are not confined by the standard white-noise limit for optical-mode baths. As an example of this formalism, we consider the case of two remote two-level systems, where jump-type quantum trajectories herald the birth and death of entanglement.

Practical Part-Specific Trajectory Optimization for Robot-Guided Inspection via Computed Tomography

Robot-guided computed tomography enables the inspection of parts that are too large for conventional systems and allows, for instance, the non-destructive and volumetric evaluation of mechanical joining components within already assembled cars in the automotive industry. However, the typical scan time required by such setups is still significant and represents a major barrier for its industrial large-scale application. As an approach to mitigate the necessary time demand, we propose a part-specific adjustment of the acquisition trajectory. Common circular standard trajectories are inherently inefficient, since they are applied independently of the considered inspection task, while the use of acquisition orbits tailored particularly to the investigated object effectively allows a reduction of the required number of projections, which in turn has the potential to directly decrease the scan time significantly. In contrast to former simulation-guided approaches, this work is considered to be the first successful task-specific trajectory optimization being performed on a robot-based industrial CT platform and aims towards providing a first proof of concept that such methods can be practically applied in a shop floor environment. Based on representative results, a reduction of the number of required projections by approx. 55 % or an image quality improvement according to the root-mean squared error by approx. 40 % compared to the conventionally applied planar acquisition trajectory was achieved.

University without Borders

The paper discusses the problem of master theses’ production in psychology from a decolonial perspective. It presents a critique to the reproductive and monological model of knowledge currently promoted in higher education. Then, it proposes an alternative pedagogic model of research-tandem. The research-tandem is an example of an innovative way of understanding a university without borders, as developed within the international network of excellence “IBEF- Ideas for the Basic Education of the Future”. Higher education must be detached from national-based curricula, and become a nomadic and collaborative across-cultural knowledge building endeavor. Current higher education aims to be national in its curricula but global in its marketability. In cultural psychology’s perspective, higher education of the future shall be regarded as global in its vision yet local in its solutions. Future students must have the opportunity to build new knowledge by experiencing and sharing diversity rather than complying with standardized and monological trajectories.

Paths to postsecondary education enrollment among adolescents with and without childhood attention-deficit/hyperactivity disorder (ADHD): A longitudinal analysis of symptom and academic trajectories.

We examined developmental trajectories of attention-deficit/hyperactivity disorder (ADHD) symptoms, standardized achievement, and school performance for adolescents with and without ADHD who did and did not enroll in postsecondary education (PSE; N=749; 79% boys; 63% White, 17% non-Hispanic Black, 10% Hispanic, and 10% other ethnicities). In a multisite study (recruitment based in New York, North Carolina, Pennsylvania, California, and Quebec), participants were originally enrolled between 1994 and 1998 at ages 7 to 9.9 and followed up through 2012 (Mage = 25 at final follow-up). Adolescents who eventually enrolled in PSE had less severe symptoms, but differences were modest and trajectories were similar over time. For all adolescents, standardized achievement trajectories declined up to two thirds of a standard deviation from ages 9 to 17. By the end of high school, the average GPA of adolescents with ADHD was three quarters of a point higher for those who eventually enrolled in PSE compared to those who did not. Overall, school performance mattered more than academic achievement for understanding eventual enrollment of adolescents with ADHD.

The Motivation for Alcohol Reward: Predictors of Progressive-Ratio Intravenous Alcohol Self-Administration in Humans.

The Progressive Ratio (PR) self-administration paradigm is a common pre-clinical method used to examine the motivation for a drug attributed to a craving, reward, or the relief of negative affect. The Computer-assisted Alcohol Infusion System (CAIS) enables intravenous alcohol self-administration behavior in humans. This system provides the investigator with control over the trajectory of each incremental breath alcohol concentration (BrAC) reward and the maximum BrAC allowed in a session. This paradigm allows participants to earn these alcohol rewards using a sequence of button presses specified by the investigator. The system employs a physiologically-based pharmacokinetic model-based algorithm to achieve the same incremental BrAC exposure in every participant. Participants (n = 11) took part in two identical sessions to examine test-retest reliability, and an additional group (n = 73) completed a single session. Sessions began with a 25 min priming phase: participants were instructed to press a button an increasing number of times per reward, accumulating four standardized incremental BrAC trajectories. The second phase comprised an ad-lib, PR paradigm lasting 125 min. Each reward required an increasing number of button presses. Measures of self-administration included: average and peak BrAC, total rewards earned, total grams of ethanol consumed per unit of total body water, the total number of button presses, and the average rate of button pressing. Self-administration measures were highly correlated both between and within sessions, demonstrating test-retest reliability and internal consistency. Recent drinking history was strongly associated with self-administration measures; heavier drinkers chose greater alcohol self-administration. These results indicate the reliability and sensitivity of this progressive-ratio intravenous alcohol self-administration method for assessing the motivational properties of alcohol, with the potential for improved testing of the efficacy of new medications thought to reduce consumption of alcohol. This method can be used to understand the genetic and environmental determinants of alcohol self-administration in humans.

The Motivation for Alcohol Reward: Predictors of Progressive-Ratio Intravenous Alcohol Self-Administration in Humans

The Progressive Ratio (PR) self-administration paradigm is a common pre-clinical method used to examine the motivation for a drug attributed to a craving, reward, or the relief of negative affect. The Computer-assisted Alcohol Infusion System (CAIS) enables intravenous alcohol self-administration behavior in humans. This system provides the investigator with control over the trajectory of each incremental breath alcohol concentration (BrAC) reward and the maximum BrAC allowed in a session. This paradigm allows participants to earn these alcohol rewards using a sequence of button presses specified by the investigator. The system employs a physiologically-based pharmacokinetic model-based algorithm to achieve the same incremental BrAC exposure in every participant. Participants (n = 11) took part in two identical sessions to examine test-retest reliability, and an additional group (n = 73) completed a single session. Sessions began with a 25 min priming phase: participants were instructed to press a button an increasing number of times per reward, accumulating four standardized incremental BrAC trajectories. The second phase comprised an ad-lib, PR paradigm lasting 125 min. Each reward required an increasing number of button presses. Measures of self-administration included: average and peak BrAC, total rewards earned, total grams of ethanol consumed per unit of total body water, the total number of button presses, and the average rate of button pressing. Self-administration measures were highly correlated both between and within sessions, demonstrating test-retest reliability and internal consistency. Recent drinking history was strongly associated with self-administration measures; heavier drinkers chose greater alcohol self-administration. These results indicate the reliability and sensitivity of this progressive-ratio intravenous alcohol self-administration method for assessing the motivational properties of alcohol, with the potential for improved testing of the efficacy of new medications thought to reduce consumption of alcohol. This method can be used to understand the genetic and environmental determinants of alcohol self-administration in humans.

An algorithm for trajectory optimization of dual-arm coordination based on arm angle constraints

In this paper, the motion planning of a dual-arm robot with kinematic constraints is studied based on arm-angle constraints. When a dual-arm robot moves a common object, a closed kinematic chain is formed between the dual-arm and the object. The standard sampling-based trajectory planning algorithm solves the problem with closed-chain constraint, but this causes other problems; the running time increases, the success rate decreases, and the motion trajectory of the end effector is not smooth resulting in large output error. Therefore, this paper proposes a dual-arm coordinated trajectory optimization algorithm based on arm-angle constraints. Firstly, the kinematics of the dual-arm robot is modeled and analyzed, and the definition of the arm-angle in a seven-axis robot is proposed, the workspace of the dual-arm coordinated operation is considered to constrain it, the kinematics equation combined with the single/multi-objective optimization algorithm is used to optimize the end output error, and the joint trajectory is parameterized. This paper solves the problems that the slave arm lags behind the main arm, the motion trajectory of the dual-arm is not smooth, and the dual-arm are squeezed due to internal force during the coordinated movement of the dual-arm. The trajectory optimization improves the synchronization of the coordinated operation of the dual-arm, reduces the output error of the velocity and acceleration at the end of the dual-arm. After limiting the arm-angle, dual-arm manipulation is anthropomorphic the robot does not produce distorted arm configurations.

State-selective electron capture cross sections in collision between fully striped ions and ground state hydrogen atom—Classical treatment of the collision

We present a state-selective electron capture cross sections database from the ground state hydrogen atom. A standard three-body classical trajectory Monte Carlo (CTMC) and quasi-classical trajectory Monte Carlo (QCTMC) models were employed for impact energies between 10 and 200 keV/amu. The projectile ions are H+, He2+, Li3+, Be4+, B5+, C6+, N7+, and O8+. The cross sections are tabulated for each value of the final quantum numbers n, l, m, summed over m for the given n and l and over l and m for the given n. The maximum quantum number nmax is set to 4 for H+, He2+ and Li3+, as well as to 5 for Be4+, B5+, C6+, N7+ and O8+, respectively.

Multi-Modal Model Predictive Control Through Batch Non-Holonomic Trajectory Optimization: Application to Highway Driving

Standard Model Predictive Control (MPC) or trajectory optimization approaches perform only a local search to solve a complex non-convex optimization problem. As a result, they cannot capture the multi-modal characteristic of human driving. A global optimizer can be a potential solution but is computationally intractable in a real-time setting. In this letter, we present a real-time MPC capable of searching over different driving modalities. Our basic idea is simple: we run several goal-directed parallel trajectory optimizations and score the resulting trajectories based on user-defined meta cost functions. This allows us to perform a search over several locally optimal motion plans. Although conceptually straightforward, realizing this idea in real-time with existing optimizers is highly challenging from technical and computational standpoints. With this motivation, we present a novel batch non-holonomic trajectory optimization whose underlying matrix algebra is easily parallelizable across problem instances and reduces to computing large batch matrix-vector products. This structure, in turn, is achieved by deriving a linearization-free multi-convex reformulation of the non-holonomic kinematics and collision avoidance constraints. We extensively validate our approach using both synthetic and real data sets (NGSIM) of traffic scenarios. We highlight how our algorithm automatically takes lane-change and overtaking decisions based on the defined meta cost function. Our batch optimizer achieves trajectories with lower meta cost, up to 6x faster than competing baselines.

A novel surgical planning system using an AI model to optimize planning of pedicle screw trajectories with highest bone mineral density and strongest pull-out force.

The purpose of this study was to evaluate the ability of a novel artificial intelligence (AI) model in identifying optimized transpedicular screw trajectories with higher bone mineral density (BMD) as well as higher pull-out force (POF) in osteoporotic patients. An innovative pedicle screw trajectory planning system called Bone's Trajectory was developed using a 3D graphic search and an AI-based finite element analysis model. The preoperative CT scans of 21 elderly osteoporotic patients were analyzed retrospectively. The AI model automatically calculated the number of alternative transpedicular trajectories, the trajectory BMD, and the estimated POF of L3-5. The highest BMD and highest POF of optimized trajectories were recorded and compared with AO standard trajectories. The average patient age and average BMD of the vertebral bodies were 69.6 ± 7.8 years and 55.9 ± 17.1 mg/ml, respectively. On both sides of L3-5, the optimized trajectories showed significantly higher BMD and POF than the AO standard trajectories (p < 0.05). On average, the POF of optimized trajectory screws showed at least a 2.0-fold increase compared with AO trajectory screws. The novel AI model performs well in enabling the selection of optimized transpedicular trajectories with higher BMD and POF than the AO standard trajectories.

To precisely calculate battery capacity by developing simultaneous and quick state-of-health diagnosis of lithium battery

Although many car manufacturers have begun producing electric vehicles, there remains room for improvement. For example, there are issues with lithium battery capacity that could be overcome by the development of a technology for checking car batteries. Associate Professor Jih-Hsin Liu, Department of Electrical Engineering, Tunghai University, Taiwan, is investigating how lithium battery capacity can be more accurately calculated, with a focus on simultaneous and quick state-of-health diagnoses. The idea is that if the ageing of each battery cell can be detected quickly, other compatible battery manufacturers can use the technology of partial replacement of battery cells to perform battery pack inspection and maintenance. Liu and the team are using artificial intelligence and software to calculate the battery level and, in doing so, can advise users on how best to extend the life of a battery, providing tailored solutions for a range of situations. In order to ensure that their research is applied to real-world functions and is incorporated into electricity storage devices, Liu and the team first observed the Nyquist diagram of AC impedance to find out a number of key AC signal excitation frequencies to detect the battery's response. Liu plans to find a company that is interested in the technology he is developing and can detect the standard decay trajectory of each battery as a comparison, so that the capacity of each battery can be widely and accurately measured.

Trajectory Optimization in Computed Tomography Based on Object Geometry

We present a new approach for the task of trajectory optimization for X-ray computed tomography (CT) in a robot-based CT (RoboCT) application. An optimized trajectory corresponds to a projection set of predefined size that leads to a 3Dreconstruction with less image-artifacts than other possible sets of the same size. We incorporate prior knowledge through a model of the object and simulated X-ray projections of the interested scenario. We formulate the optimization problem as maximization of an objective function and solve it heuristically by means of a genetic algorithm. As objective function, we use a metric that correlates with the amount of edge information of a projection set, since we assume that this characteristic is important for a reconstruction with low image-artifacts. The metric is based on wavelet analysis of the projections. We carry out computer experiments for a test object made of homogeneous material. The optimized trajectory allows a sharp reconstruction of straight edges and clearly outperforms reconstructions from standard trajectories.

Uncertain trajectory planning integrating polynomial chaos and convex programming

The present work explores the robust trajectory optimization scheme considering both the initial state disturbance and multiple constraints. An uncertain multi-constraint optimization model has been first established. Due to the existence of the stochastic disturbance, standard numerical trajectory planning algorithms cannot be directly applied to address the considered issue. Hence, based on the intrusive polynomial chaos expansion, we present a deterministic quantification for the stochastic state and constraints, so that the transformed optimization model becomes solvable for standard numerical optimization methods. To obtain the enhanced computational performance, an hp pseudo-spectral sequential convex programming procedure combined with a penalty function and backtracking search is proposed. This is achieved by discretizing and convexifying the nonlinear dynamics/constraints using hp quadrature collocation and successive linearization, respectively, and by adjusting the confidence region manually in the iteration. The simulation of a three-dimensional interception with the specific impact angle is conducted to verify the effectiveness. The simulation results show that the initial solutions are insensitive to the convex optimization, and the control commands generated by the proposed algorithm are effective against the initial state disturbance.

Motion optimization of an omnidirectional mobile robot with MY wheel based on contact mechanics

PurposeThe mechanical properties between wheel and ground will affect the motion performance of wheeled omnidirectional mobile robot (OMR). MY3 wheel is an omnidirectional wheel. This paper aims to analyze the contact mechanical characteristics between MY3 wheel and ground to improve the motion accuracy of an omnidirectional mobile platform with MY3 wheel (MY3-OMR).Design/methodology/approachThis method takes MY3 wheel as the research objective. The normal and tangential contact mechanics model and rolling contact mechanics model of MY3 wheel are established by analyzing the structure of MY3 wheel, and thereby, the slip ratio of MY3 wheel in the process of motion is calculated. The kinematics model of MY3-OMR is optimized by taking the slip ratio as the optimization parameter that aims to improve motion accuracy of MY3-OMR.FindingsThe correctness of the mechanical analysis and the feasibility of the method are verified by the MY3-OMR prototype. Let MY3-OMR move along the set circular trajectory and square trajectory, and the error between the motion trajectory before and after optimization and the standard trajectory is obtained. It illustrates that the error in the square trajectory is reduced by 1.5%, and the circular trajectory error is reduced by 2%; therefore, the method is effective.Originality/valueA method based on contact mechanics is proposed and verified. Through the establishment of wheel-ground contact mechanics model to optimize MY3-OMR kinematics model, and thereby, the motion accuracy of MY3-OMR is improved, which lays a foundation for MY3-OMR engineering application.

Static coded aperture in robotic X-ray tomography systems.

Coded aperture X-ray computed tomography is a computational imaging technique capable of reconstructing inner structures of an object from a reduced set of X-ray projection measurements. Coded apertures are placed in front of the X-ray sources from different views and thus significantly reduce the radiation dose. This paper introduces coded aperture X-ray computed tomography for robotic X-ray systems which offer positioning flexibility. While single coded-aperture 3D tomography was recently introduced for standard trajectory CT scanning, it is shown that significant gains in imaging performance can be attained by simple modifications in the CT scanning trajectories enabled by emerging dual robotic CT systems. In particular, the subject is fixed on a plane and the CT system uniformly rotates around the r -axis which is misaligned with the coordinate axes. A single stationary coded aperture is placed on front of the robotic X-ray source above the plane and the corresponding X-ray projections are measured by a two-dimensional detector on the second arm of the robotic system. The compressive measurements with misalignment enable the reconstruction of high-resolution three-dimensional volumetric images from the low-resolution coded projections on the detector at a sub-sampling rate. An efficient algorithm is proposed to generate the rotation matrix with two basic sub-matrices and thus the forward model is formulated. The stationary coded aperture is designed based on the Pearson product-moment correlation coefficient analysis and the direct binary search algorithm is used to obtain the optimized coded aperture. Simulations using simulated datasets show significant gains in reconstruction performance compared to conventional coded aperture CT systems.

DACA: Distributed adaptive grid decision graph based clustering algorithm

AbstractClustering algorithms play a very important role in machine learning. With the development of big‐data artificial intelligence, distributed parallel algorithms have become an important research field. To reduce the computational complexity and running time of large‐scale datasets in the clustering process, this study proposes a distributed clustering algorithm DACA (distributed adaptive grid decision graph based clustering algorithm). In a distributed environment, DACA uses relative entropy to adaptively mesh the data to form an obvious sparse grid and dense grid. Then, the decision graph is used to determine the cluster center mesh object. Finally, the KD‐tree is used to accelerate the determination of the cluster center of sparse points to complete clustering. The algorithm is implemented using the popular Apache Spark computing framework, compared with other distributed clustering algorithms, DACA can adaptively divide the grid according to the data distribution to obtain better clustering effect. At the same time, KD tree algorithm is used to speed up the decision‐making of clustering center. Numerous experiments show that the DACA algorithm has excellent performance and accuracy on six standard datasets and real GPS trajectory datasets.