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Experimental autonomous flight of a small-scaled helicopter using accurate dynamics model and low-cost sensors

Authors: D. Vissière, P.-J. Bristeau, A. P. Martin, N. Petit Proc. of the 2008 IFAC World Congress
In this paper, we address the problem of guidance and control of a small-scaled helicopter (Benzin Acrobatic from Variowith a 1.8 m diameter rotor) equipped with only low-cost sensors. These sensors are an Inertial Measurement Unit (IMU), a GPS, and a barometer, which represent a total cost of USD 3000 including one PC board and one micro-controller). By contrast to other experiments reported in the literature, we do not rely on any accurate IMU or GPS systems which costs are, separately, largely above the mentioned amount of USD 3000. To compensate the weaknesses of this low cost equipment, we put our eorts in obtaining an accurate flight dynamics model. This improves the prediction capabilities of our embedded Kalman filter that serves for data fusion. The main contribution of this paper is to detail, at the light of a successful reported autonomous hovering flight, the derivation of the model. We give numerous details about implementation and discuss the relevance of some modelling hypothesis based on our experience.
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BibTeX:
@Proceedings{,
author = {David Vissière, Pierre-Jean Bristeau, Alain Pierre Martin, Nicolas Petit},
editor = {},
title = {Experimental autonomous flight of a small-scaled helicopter using accurate dynamics model and low-cost sensors},
booktitle = {17th World Congress of the International Federation of Automatic Control},
volume = {Proc. of the 17th World Congress of the International Federation of Automatic Control},
publisher = {},
address = {Seoul},
pages = {14642-14650},
year = {2008},
abstract = {In this paper, we address the problem of guidance and control of a small-scaled helicopter (Benzin Acrobatic from Vario TM with a 1.8 m diameter rotor) equipped with only low- cost sensors. These sensors are an Inertial Measurement Unit (IMU), a GPS, and a barometer, which represent a total cost of USD 3000 including one PC board and one micro-controller). By contrast to other experiments reported in the literature, we do not rely on any accurate IMU or GPS systems which costs are, separately, largely above the mentioned amount of USD 3000. To compensate the weaknesses of this low cost equipment, we put our efforts in obtaining an accurate flight dynamics model. This improves the prediction capabilities of our embedded Kalman filter that serves for data fusion. The main contribution of this paper is to detail, at the light of a successful reported autonomous hovering flight, the derivation of the model. We give numerous details about implementation and discuss the relevance of some modelling hypothesis based on our experience.},
keywords = {Low-cost sensors, unmanned vehicle, data fusion, autonomous helicopter, embedded systems, Kalman filter}}