Industrial parallel robots are characterized by their stable structure and grasping pick-up speed, and they are commonly used in food packaging and parts assembly industries. However, the traditional robot teaching pendants often provide monitoring data under idealized conditions. The lack of precise monitoring, combined with sensor data, frequently results in misaligned robot positioning and a reduced operational lifespan. To enhance these robots' positioning accuracy and monitoring efficiency, this paper introduces research on status monitoring and positioning compensation system for digital twin of parallel robots. Initially, the digital twin framework is constructed. Establishing a spatial kinematics model that serves as the foundation for the twin model, the kinematics model facilitates basic synchronized interaction. Furthermore, an improved IPSO-SSA-DBP algorithm calibrates the kinematics and predicts the position error. Ultimately, a digital twin system is built to visualize the status monitoring and positioning error compensation, and the system's reliability is confirmed through experimental validation.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11876575 | PMC |
| http://dx.doi.org/10.1038/s41598-025-91892-2 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
From traditional robotic deployments towards assisted robotic deployments in nuclear decommissioning.
Front Robot AI
February 2025
Department of Electrical and Electronics Engineering, The University of Manchester, Manchester, United Kingdom.
The history around teleoperation and deployment of robotic systems in constrained and dangerous environments such as nuclear is a long and successful one. From the 1940s, robotic manipulators have been used to manipulate dangerous substances and enable work in environments either too dangerous or impossible to be operated by human operators. Through the decades, technical and scientific advances have improved the capabilities of these devices, whilst allowing for more tasks to be performed.
View Article and Find Full Text PDFPortable and Self-Powered Sensing AI-Enabled Mask for Emotional Recognition in Virtual Reality.
ACS Appl Mater Interfaces
March 2025
School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, P. R. China.
With the increasing development of metaverse and human-computer interaction (HMI) technologies, artificial intelligence (AI) applications in virtual reality (VR) environments are receiving significant attention. This study presents a self-sensing facial recognition mask (FRM) utilizing triboelectric nanogenerators (TENG) and machine learning algorithms to enhance user immersion and interaction. Various TENG negative electrode materials are evaluated to improve sensor performance, and the efficacy of a single sensor is confirmed.
View Article and Find Full Text PDFQuantifying, predicting, and mitigating nitrous oxide emissions in a full-scale partial nitritation/anammox reactor treating reject water.
Water Res
January 2025
PROSYS Research Center, Department of Chemical and Biochemical Engineering, Technical University of Denmark, Søltofts Plads Building 228A, Kgs. Lyngby 2800, Denmark.
In this paper, a set of mathematical tools are developed and assembled to quantify, predict and virtually assess NO emission mitigation strategies in partial nitritation (PN) / anammox (ANX) granular based reactors. The proposed approach is constructed upon a set of data pre-treatment methods, process simulation models, control tools (and algorithms) and key performance indicators to analyze, reproduce, and forecast the behavior of multiple operational variables within aerobic granular sludge systems. All these elements are tested on two full-scale data sets (#D1, #D2) collected over a period of four months (Sept-Dec 2023).
View Article and Find Full Text PDFSensing the Future of Thrombosis Management: Integrating Vessel-on-a-Chip Models, Advanced Biosensors, and AI-Driven Digital Twins.
ACS Sens
March 2025
School of Biomedical Engineering, The University of Sydney,Darlington,NSW 2008,Australia.
Thrombotic events, such as strokes and deep vein thrombosis, remain a significant global health burden, with traditional diagnostic methods often failing to capture the complex, patient-specific nuances of thrombosis risk. This Perspective explores the revolutionary potential of microengineered vessel-on-chip platforms in thrombosis research and personalized medicine. We discuss the evolution from basic microfluidic channels to advanced 3D-printed, patient-specific models that accurately replicate complex vascular geometries, incorporating all elements of Virchow's triad.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!