Autonomous post-typhoon structural inspection and damage assessment: an agentic AI and aerial robotics-enabled model
The increasing frequency and intensity of typhoons in recent years due to climate change has heightened the need for rapid post-disaster assessment. Inspection of post-typhoon damage is required to assess damages to critical infrastructure and facilitate disaster management. Unmanned aerial vehicles (UAVs) are commonly used for the recognition of post-typhoon damage. However, existing UAVs require either to be manually teleoperated or to follow a pre-programmed flight path, which is significantly limited in terms of rapid or adaptive post-typhoon inspection. To address this critical limitation, this study introduces a novel agentic AI and aerial robotics-enabled model designed for autonomous inspection operations. We propose a three-layered conceptual framework that closes the perception–cognition–action loop, thereby enabling goal-directed mission reasoning. This framework is operationalized as the Agentic AI and UAV-enabled Autonomous Inspection and Assessment System (AAUIAS)—a comprehensive cyber-physical system architecture. Within this architecture, the core agentic AI layer independently performs world modeling, real-time damage assessment, and dynamic path replanning. The proposed model and system were evaluated through high-fidelity simulations of a typhoon-impacted urban environment. Results demonstrate that AAUIAS reduces overall inspection time by 34.5%–40.2% relative to conventional approaches, achieves robust dynamic obstacle avoidance, and intelligently prioritizes critical damage sites. This preliminary work contributes a conceptual model and system architecture, supported by simulation-based evidence, for transforming UAVs into proactive intelligent agents, thereby advancing the state of the art in autonomous disaster response and resilient infrastructure management.