This work seeks to capture how an expert interacts with a structure during a facade inspection so that more detailed and situationally-aware inspections can be done with autonomous robots in the future. Eye tracking maps where an inspector is looking during a structural inspection, and it recognizes implicit human attention. Experiments were performed on a facade during a damage assessment to analyze key, visually-based features that are important for understanding human-infrastructure interaction during the process. For data collection and analysis, experiments were conducted to assess an inspector’s behavioral changes while assessing a real structure. These eye tracking features provided the basis for the inspector’s intent prediction and were used to understand how humans interact with the structure during the inspection processes. This method will facilitate information-sharing and decision-making during the inspection processes for collaborative human-robot teams; thus, it will enable unmanned aerial vehicle (UAV) for future building inspection through artificial intelligence support.
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