Adaptive Digital Twin Framework for Safe Human-Robot Collaborative Hydrogen Storage System Maintenance
Abstract
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.
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Research Gap Analysis
Current research lacks integration between human-aware robotics, digital twin technology, and safety-critical control for hazardous environment maintenance. Existing solutions don't adequately address the unique challenges of hydrogen storage system maintenance while ensuring human safety.
Adaptive Digital Twin Framework for Safe Human-Robot Collaborative Hydrogen Storage System Maintenance
Motivation
Hydrogen storage systems require regular maintenance and inspection, but present significant safety risks to human workers. While robotics could help, current solutions lack the sophisticated awareness and adaptability needed for safe human-robot collaboration in such hazardous environments. Additionally, training personnel for these tasks is challenging and risky in real-world settings.
Proposed Approach
The framework consists of three integrated components:
1. Digital Twin System
- High-fidelity simulation of the hydrogen storage facility
- Real-time synchronization with physical sensors
- Integration of human and robot models
- Safety envelope visualization and prediction
2. Human-State-Aware Robotic Control
- Multi-modal human state monitoring (position, gestures, physiological signals)
- Adaptive safety boundaries based on human state
- Real-time trajectory optimization with safety constraints
- Learning from human expert demonstrations
3. Safety-Critical Control Layer
- Multiple discrete-time control barrier functions for various safety objectives
- Q-learning based optimization of robot behavior
- Real-time risk assessment and mitigation
- Fail-safe mechanisms and emergency protocols
Expected Outcomes
- Reduced maintenance-related accidents through predictive safety measures
- Improved training efficiency using the digital twin
- Enhanced human-robot collaboration capabilities
- Validated safety protocols for hydrogen storage maintenance
- Generalizable framework for other hazardous environments
Potential Applications
- Industrial hydrogen storage facilities
- Aircraft hydrogen fuel system maintenance
- Clean energy infrastructure maintenance
- Training and certification programs
- Risk assessment and safety planning
Proposed Methodology
Develop an integrated framework combining digital twin simulation, human state monitoring, and safety-critical control for adaptive robot behavior in collaborative maintenance tasks.
Potential Impact
This research could significantly improve safety in hydrogen infrastructure maintenance, reduce training costs, and accelerate the adoption of hydrogen technologies across industries. The framework could become a standard for human-robot collaboration in hazardous environments.