Advancements in the area of unmanned aerial vehicles (UAVs) are expected to result in an increase in flights being conducted in urban areas. However, the presence of buildings with varying sizes and the dynamic conditions in urban areas generate stark differences in environmental conditions that UAVs operate such as the wind, temperature, pressure, and humidity across a city. These changes in meteorological conditions can be significant for the operation of UAVs even across short distances. Smaller UAVs find themselves more susceptible to any variations in weather which can cause substantial challenges in flights due to their lower weight. This challenge requires a method to continuously collect reliable meteorological data at multiple points across cities. This paper proposes the use of crowdsensing and agent-based modeling for high-resolution spatial and temporal data collection in three dimensions (3D) and predictive performance improvement for UAV operations in urban areas. This approach uses an agent-based model to simulate a 3D urban environment where we use a game-theory based crowdsensing model to collect data from multiple agents across the city. The simulation aims to find a solution to the research challenge that provides consistent spatial and temporal weather data for creating a 3D weather map of urban environments. This data will allow us to assess the safety and efficiency of a planned flight route as well as suggest alternative paths that can allow the vehicle to safely reach its destination.

Keywords
AGENT-BASED SIMULATION, CROWD SOURCING, DATA, UAV, URBAN ENVIRONMENT

Additional Keywords