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Transforming Facility Inspections with Autonomous UxVs & AI-Driven Digital Twins
Inspecting industrial facilities is vital for product quality, worker safety, and environmental compliance.
However, traditional maintenance relies heavily on manual visual inspections. This approach is expensive, slow, labor-intensive, and prone to human error.
It also puts inspectors at risk when accessing hazardous locations like high structures or confined spaces.
Current automated solutions, such as basic drone usage, still face major hurdles. Unmanned vehicles struggle with vibration, environmental noise, and optimal positioning, while building digital twins remain slow and complex to generate manually.
The MainVerse project solves these challenges by creating an end-to-end platform that automates the entire inspection workflow.
MainVerse combines AI-driven autonomous routing for unmanned vehicles, automated digital twin generation from CAD drawings and sensor data, and smart pathology detection algorithms.
It also introduces a collaborative metaverse environment where teams can interact with and enrich digital twins in real time using web-based extended reality.
This streamlines facility inspections, lowers costs, and improves operational safety.
The goal of MainVerse is to automate industrial facility inspections using autonomous drones and AI.
The project creates real-time digital twins and immersive collaboration tools.
We will extend our Asimovo platform to automatically generate 3D digital twins using existing maps, CAD drawings, and sensor data.
We lead the development of AI computer vision algorithms to detect building pathologies and structural defects. Additionally, we provide a human-in-the-loop learning system to continuously train and refine these detection models.
Through these contributions, we empower industrial partners to easily build digital representations of their facilities and automate building condition analysis.
The MainVerse project will deliver a cloud platform that fully automates industrial facility inspections through autonomous drone routing and AI-powered pathology analysis.
By automatically generating 3D digital twins and web-based collaboration tools, the project enables companies to cut inspection costs by at least 50% while improving worker safety and environmental compliance.
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