Dynamic data-driven sensor placement for enabling capability estimation of self - aware aerospace vehicles
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abstract
Unmanned aerial vehicles (UAV) are utilized in numerous industries and in recent years with the advent of learning techniques, the focus is now on developing Self aware UAVs that rely on an array of environmental sensors to replace a pilots awareness of the structural capability of the UAV and provide time critical analysis of the sensor data to make complex decisions in real time.Hence a self aware UAV is capable of dynamically and autonomously sense its structural state and act accordingly to perform the required task. In this thesis we propose a data driven approach to producing estimates of capability of a self aware UAV and using that to optimize the sensor location is presented. This process involves using high physics-based models such as ASWING, an aerodynamic, structural, and control response analysis software in tandem with Akselos modeler to produce an offline library that comprises of damage states along with capabilities corresponding to different kinematic states of a UAV. Further this generated information is used to create a classification model which is used to predict the capability for the real time data. The classification model serves as an enabler for the optimization algorithm to measure the error value between the true and the predicted capability of the UAV. The objective of the study is to provide evidence that optimizing sensor locations will yield to better decision making and extended life cycle of the UAV. We demonstrate this through a performance comparison between optimum placement and standard placement of sensors. We have also provided evidence of proof of concept of how dynamic sampling of information can improve the process of capability estimation in self aware aerospace vehicles.
