Robotics and Digital Twin

A digital twin is a virtual representation of a physical object or system that mirrors its real-world counterpart. It combines data from various sources such as sensors, IoT devices, and historical records to create a dynamic model that can be used for analysis, simulation, and monitoring. Digital twins are commonly used in industries like manufacturing, healthcare, and urban planning to optimize operations, improve efficiency, and predict outcomes.

Researching the process of creating a digital twin involves studying various technologies and methodologies used in data collection, integration, modeling, and visualization. It requires understanding how to capture and process real-time data, develop accurate models that reflect the physical system, and ensure seamless communication between the digital twin and its physical counterpart. Researchers also explore ways to enhance the accuracy, reliability, and security of digital twins to enable better decision-making and performance optimization.