Research Interests

This research direction addresses the dual demands of routine inspection and emergency response for urban roads and railway infrastructure. It leverages UAV-acquired multi-source sensing data to drive high-fidelity digital twin modeling of representative scenarios, including road–rail combined bridges, trackside environments, and in-service vehicles, thereby enabling real-time mapping and state synchronization between the physical and virtual spaces. Building on this foundation, intelligent reconstruction and simulation of safety-critical scenarios are employed to establish a closed-loop support system for the early detection of anomalous events, risk warning, and decision-making. The framework further enhances the capacity of intelligent flight systems to assist—and, when necessary, take over from—human pilots, enabling a gradual shift in flight control and risk assessment from a human-dominated model to an intelligent operational paradigm characterized by system-led decision-making and human-in-the-loop supervision. This transition ultimately advances UAV inspection from reliance on human experience toward autonomous, intelligence-driven decision-making.