Harnessing Robotic Scouts for Resilient Evacuation Policies in Disaster Scenarios

Efficient evacuation route planning is critical for enhancing emergency response systems in disaster scenarios. Unlike traditional navigation systems that rely on pre-existing data and provide traffic-based routing under normal conditions, our method integrates robotic scouts-comprising drones and ground vehicles-to dynamically assist in evacuation planning during disasters. We propose an effective method for synthesizing evacuation policies that enable robotic scouts to guide evacuees through disaster-affected areas. By leveraging realworld disaster assessment data mapped onto a roadmap, we model the disaster environment and formulate the problem of generating evacuation policies in a stochastic framework using a Markov Decision Process (MDP). Within this framework, we assign location-specific costs on the roadmap based on the degree of structural damage in surrounding areas. Through policy iteration, we solve the MDP to synthesize the optimal evacuation policy for robotic scouts, ensuring effective routes from impacted zones to safe locations. Our simulation results based on real-world data from previous disaster assessments and performance analysis demonstrate the effectiveness of our method and validate its potential to significantly improve disaster management and emergency response strategies.