Overview

Collaborated to develop a framework in C++ to predict the response of bio-inspired smart wing for "Fly-By-Feel" autonomous vehicles under different flight conditions and loads. Optimized the model by operating on data collected from sensors during wind tunnel experiments and achieved a 98% prognostic accuracy using Finite Element Analysis in ABAQUS.
Flight state estimation framework, named fly-by-feel, leverages the high-dimensionality and multimodality properties of sensor network data. The i-FlyNet model architecture, which acts as the backbone classifier of this framework, is built as a combination of conventional signal processing and modern deep learning techniques to make the richest possible inference from this unique sensory data. SACL’s fly-by-feel powered morphing wing not only excels at predicting stall for safe flight, it also estimates the wing shape and angle of attack that will achieve the maximum flight efficiency.