On the Development of a Tether-based Drone Localization System. Lima, R. R. & Pereira, G. A. S. In 2021 International Conference on Unmanned Aircraft Systems (ICUAS), pages 195–201, June, 2021. ISSN: 2575-7296doi abstract bibtex This paper proposes an approach for localization of tethered drones using information from the shape of the tether and the vehicles' inertial data. Our approach relies on the fact that, in static or quasi-static conditions, a flexible tether will assume a catenary shape, thus allowing the use of known equations to formulate the localization algorithm. These equations are based on tether variables such as tether length, tension, and azimuth and elevation angles on both endpoints. To deal with uncertainties and to improve localization performance, a sensor fusion algorithm based on the Extended Kalman Filter (EKF) is applied. In this preliminary validation, we tested our method through experiments with a static real-world tether, and simulations with a drone in slow flight. In both cases, the proposed method successfully estimated positions in the presence of noisy measurements.
@inproceedings{lima_development_2021,
title = {On the {Development} of a {Tether}-based {Drone} {Localization} {System}},
doi = {10.1109/ICUAS51884.2021.9476778},
abstract = {This paper proposes an approach for localization of tethered drones using information from the shape of the tether and the vehicles' inertial data. Our approach relies on the fact that, in static or quasi-static conditions, a flexible tether will assume a catenary shape, thus allowing the use of known equations to formulate the localization algorithm. These equations are based on tether variables such as tether length, tension, and azimuth and elevation angles on both endpoints. To deal with uncertainties and to improve localization performance, a sensor fusion algorithm based on the Extended Kalman Filter (EKF) is applied. In this preliminary validation, we tested our method through experiments with a static real-world tether, and simulations with a drone in slow flight. In both cases, the proposed method successfully estimated positions in the presence of noisy measurements.},
booktitle = {2021 {International} {Conference} on {Unmanned} {Aircraft} {Systems} ({ICUAS})},
author = {Lima, Rogerio R. and Pereira, Guilherme A. S.},
month = jun,
year = {2021},
note = {ISSN: 2575-7296},
keywords = {Azimuth, Location awareness, Mathematical model, Position measurement, Sensor fusion, Shape, Uncertainty},
pages = {195--201},
}
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