Escape Route

Escape route planning research focuses on developing intelligent systems that rapidly and safely guide humans and robots out of hazardous environments, such as wildfires and building fires. Current approaches leverage diverse techniques, including reinforcement learning, A* search algorithms enhanced with dynamic hazard models (e.g., fire spread, smoke), and hybrid quantum-classical machine learning for improved pathfinding in complex, uncertain conditions. These advancements are crucial for improving emergency response efficiency and enhancing the safety of both first responders and civilians during disasters, with applications ranging from wildfire evacuation to building escape scenarios.