Luca Saltalamacchia
An Artificial Intelligent methodology to estimate the population density of urban areas to compute risk maps for Unmanned Aircraft Systems.
Rel. Alessandro Rizzo, Stefano Primatesta. Politecnico di Torino, Corso di laurea magistrale in Mechatronic Engineering (Ingegneria Meccatronica), 2020
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Abstract
As the adoption and usage of Unmanned Aircraft Systems (UAS) is growing over time, the safety of the surrounding environment when they are used autonomously over urban areas has been questioned. Urban areas are characterized by high population density therefore a potential UAS crash may involve people injuries or even people death, as a consequence, it is essential to preserve human safety in mission planning. This thesis is based on a previous work done by the research group, in which a path planning algorithm for UAS has been developed to determine a safe flight mission. The proposed methodology uses a risk-based map that quantifies the risk of fly over specific areas.
In order to compute a safe path for the UAS several input parameters are required, but, unfortunately it’s not always easy to obtain those specific data, such as the population density and distribution that is one of the most important parameter for the risk assessment
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