Research Index

A novel framework combining digital twin technology, human-state-aware robotics, and safety-critical control for collaborative maintenance of hydrogen storage systems. The system adapts robot behavior based on real-time human state monitoring and safety constraints while maintaining a digital twin for training, simulation, and risk assessment.

Develop an intelligent control system that combines machine learning, human expertise, and real-time safety monitoring for hydrogen storage facilities. The system would adapt its control parameters based on both sensor data and human operator feedback, while maintaining strict safety constraints through barrier functions.