Mobile robots have an important role in material handling in manufacturing and can be used for a variety of automated tasks. The accuracy of the robot's moving trajectory has become a key issue affecting its work efficiency. This paper presents a method for optimizing the trajectory of the mobile robot based on the digital twin of the robot. The digital twin of the mobile robot is created by Unity, and the trajectory of the mobile robot is trained in the virtual environment and applied to the physical space. The simulation training in the virtual environment provides schemes for the actual movement of the robot. Based on the actual movement data returned by the physical robot, the preset trajectory of the virtual robot is dynamically adjusted, which in turn enables the correction of the movement trajectory of the physical robot. The contribution of this work is the use of genetic algorithms for path planning of robots, which enables trajectory optimization of mobile robots by reducing the error in the movement trajectory of physical robots through the interaction of virtual and real data. It provides a method to map learning in the virtual domain to the physical robot.
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http://dx.doi.org/10.3389/fbioe.2021.793782 | DOI Listing |
JMIR Pediatr Parent
January 2025
Department of Design Innovation, College of Design, University of Minnesota, Twin Cities, Minneapolis, MN, United States.
Background: Congenital heart disease (CHD) is the most common birth defect, affecting 40,000 births annually in the United States. Despite advances in medical care, CHD is often a chronic condition requiring continuous management and education. Effective care management depends on children's understanding of their condition.
View Article and Find Full Text PDFBehav Sci (Basel)
December 2024
Smart Design Lab, School of Design, Northumbria University, Newcastle upon Tyne NE1 8ST, UK.
Nostalgic scenes can trigger nostalgia to a considerable extent and can be effectively used as a nostalgic trigger that contributes to the psychological comfort of the elderly and immigrant populations, but a design system has not been adequately studied. Therefore, the design principles and digital twin (DT) design system of nostalgic scenes is proposed in this study. It focuses on the construction of a nostalgic scene DT model based on the system of system (SoS) theory.
View Article and Find Full Text PDFChild Youth Serv Rev
July 2024
Department of Psychology, Arizona State University, Tempe, AZ, USA.
Introduction: Parenting programs are widely used to prevent and ameliorate children's emotional and behavioral problems but low levels of engagement undermine intervention effectiveness and reach within and beyond research settings. Technology can provide flexible and cost-effective alternate service-delivery formats for parenting programs, and studies are needed to assess the extent to which parents are willing to engage with digitally assisted formats.
Methods: After Deployment, Adaptive Parenting Tools (ADAPT) is an evidence-based parenting program for military families.
J Nucl Med
January 2025
United Theranostics, Bethesda, Maryland.
Computational nuclear oncology for precision radiopharmaceutical therapy (RPT) is a new frontier for theranostic treatment personalization. A key strategy relies on the possibility to incorporate clinical, biomarker, image-based, and dosimetric information in theranostic digital twins (TDTs) of patients to move beyond a one-size-fits-all approach. The TDT framework enables treatment optimization by real-time monitoring of the real-world system, simulation of different treatment scenarios, and prediction of resulting treatment outcomes, as well as facilitating collaboration and knowledge sharing among health care professionals adopting a harmonized TDT.
View Article and Find Full Text PDFShock
January 2025
Department of Industrial and Systems Engineering, University of Florida, P.O. Box 116595, Gainesville, FL, 32611, USA.
Understanding clinical trajectories of sepsis patients is crucial for prognostication, resource planning, and to inform digital twin models of critical illness. This study aims to identify common clinical trajectories based on dynamic assessment of cardiorespiratory support using a validated electronic health record data that covers retrospective cohort of 19,177 patients with sepsis admitted to ICUs of Mayo Clinic Hospitals over eight-year period. Patient trajectories were modeled from ICU admission up to 14 days using an unsupervised machine learning two-stage clustering method based on cardiorespiratory support in ICU and hospital discharge status.
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