Real-time State Awareness Nerve-like Sensing System for Autonomous Fly-by-Feel Aerial Vehicle
PI: Prof. Fu-Kuo Chang
Department: Aeronautics and Astronautics
Sponsor: United States Navy (USN) ONR NEPTUNE Program
The next generation intelligent vehicles will be able to “react” during operation through a richer “perception” of its environment, leading to a more elaborate “interpretation” of its current state. This project builds on the sensor network technology that has long been matured at the Structures and Composites Lab (SACL) at Stanford University. One of the main applications SACL has been focusing on is development of sensor network equipped UAV wing. The data obtained through sensor network have unique properties of (i) having been collected from multiple locations on the wing, and (ii) providing information on different characteristics of the wing through the use different modality sensors, such as piezoelectric, strain, and temperature.
Our first objective in this project is bringing global discernment capability to flying vehicle by fusing local information collected by individual sensors on the wing, leading to a high-level interpretation of its existing flight state. The level of insight we propose to obtain will be made possible by processing the multi-sensory & multi-modality data via advanced physics-assisted learning and inference algorithms. Such a robust knowledge on the operational conditions paves the way for the second objective: We propose to develop a real-time active control algorithm to achieve advanced flight objectives using the domain knowledge of interpreted states. The flight objectives to be designated include stall avoidance, minimizing vibration under turbulence, and lift/drag ratio maximization. The wing control variables include angle of attack, airfoil shape, and freestream airspeed.
H4D Focus Areas: Smart Materials, Autonomy, Technology Transition