Distributed Pressure Sensing for Enabling Self-Aware Autonomous Aerial Vehicles
Daniel Cellucci, Nicholas Cramer, Sean S.-M. Swei
Abstract
Autonomous aerial transportation will be a fixture of future robotic societies, simultaneously requiring more stringent safety requirements and fewer resources for characterization than current commercial air transportation. More robust, adaptable, self-state estimation will be necessary to create such autonomous systems. We present a modular, scalable, distributed pressure sensing skin for aerodynamic state estimation of a large, flexible aerostructure. This skin used a network of 22 nodes that performed in situ computation and communication of data collected from 74 pressure sensors, which were embedded into the skin panels of an ultra-lightweight 14-foot wingspan made from commutable, lattice-based subcomponents, and tested at NASA Langley Research Center's 14X22 wind tunnel. The density of the pressure sensors allowed for the use of a novel distributed algorithm to generate estimates of the wing lift contribution that were more accurate than the direct integration of the pressure distribution over the wing surface.
BibTeX
@inproceedings{iros2018_distributedpress,
title = {Distributed Pressure Sensing for Enabling Self-Aware Autonomous Aerial Vehicles},
author = {Daniel Cellucci and Nicholas Cramer and Sean S.-M. Swei},
booktitle = {IROS 2018},
year = {2018}
}