Use of Unmanned Aerial Vehicles for Calibration of the Precision Approach Path Indicator System

Carlos Eduardo Uc Ríos; Pedro Lopez Teruel

doi:10.15866/irease.v14i4.20709

Use of Unmanned Aerial Vehicles for Calibration of the Precision Approach Path Indicator System

Carlos Eduardo Uc Ríos⁽¹⁾, Pedro Lopez Teruel^(2*)

^(*) Corresponding author

Authors' affiliations

DOI: https://doi.org/10.15866/irease.v14i4.20709

Abstract

Use of UAVs (Unmanned Aerial Vehicles) has increased exponentially due to its flexibility, maneuverability and reduced costs. Technological advances such as improvement of stabilization systems, GPS (Global Position System) and first-person view cameras have enabled the introduction of these platforms into different sectors. However, current regulations prohibit autonomous flights at airport environment, which leaves many business opportunities non-explored. Since approach is one of the most difficult flight phases, this paper presents an onboard autonomous algorithm solution for visual approaches called AVVAS (Autonomous Verification for Visual Approach System). AVVAS reports the current calibration status and the necessary calibration to be reached for PAPI (Precision Approach Path Indicator) system. To optimize and validate the algorithm, a simulation is performed on a FFS (Full Flight Simulator). This algorithm provides the following benefits: reduction of maintenance costs and reduction of the calibration time increasing the runway availability.
Copyright © 2021 Praise Worthy Prize - All rights reserved.

Keywords

UAV; Drone; Calibration; PAPI; Airport

Full Text:

PDF

References

ICAO, Annex 14 - Aerodromes - Volume I - Aerodromes Design and Operations, eighth ed. (Chicago 2018).

Bas, J. Uses and applications of drones in the future. ALTRANTECH360: Your community of INNOVATION and TECHNOLOGY, (Altran S.A. 2016).

Becerra, V. Autonomous Control of Unmanned Aerial Vehicles, (University of Portsmouth, 2016).

Loubar, H., Boushaki, R., Aouati, A., Bouanzoul, M., Sliding Mode Controller for Linear and Nonlinear Trajectory Tracking of a Quadrotor, (2020) International Review of Automatic Control (IREACO), 13 (3), pp. 128-138.
https://doi.org/10.15866/ireaco.v13i3.18522

N. Xuan-Mung, S. K. Hong, Improved Altitude Control Algorithm for Quadcopter Unmanned Aerial Vehicles, (2019), Applied Science, Vol. 9 (Issue 10).
https://doi.org/10.3390/app9102122

Y. Alaiwi, A. Mutlu, Modelling, Simulation and implementation of Autonomous Unmanned Quadrotor, International Scientific Journal "Machines. Technologies. Materials", Vol. 12(Issue 8):320-325, 2018.

Di Vito, V. et al. UAV free path safe DGPS/AHRS approach and landing system with dynamic and performance constraints, (Conference: UAV 2007 International Conference, Exhibition and Workshop, Paris, 2007)

Alnuaimi, M., Perhinschi, M., Al-Sinbol, G., Immunity-Based Framework for Autonomous Flight in GNSS-Denied Environment, (2019) International Review of Aerospace Engineering (IREASE), 12 (6), pp. 239-249.
https://doi.org/10.15866/irease.v12i6.16215

Tezza, D. L., Denis-Caprio, D., Andujar, M. (2020). Let's Fly! An Analysis of Flying FPV Drones through an Online Survey (Creative Commons, Honouloulu, 2020).

Fas-Millán, M., Pastor Llorens, E., Controller-Pilot Data Link Communications Display oriented to multiple Remotely Piloted Aircraft Systems pilots, (2019) International Review of Aerospace Engineering (IREASE), 12 (1), pp. 1-11.
https://doi.org/10.15866/irease.v12i1.15535

Markus Funk. 2018. Human-drone interaction: let's get ready for flying user interfaces! Interactions 25, 3 (2018), 78-81.
https://doi.org/10.1145/3194317

El Gmili, N., Mjahed, M., El Kari, A., Ayad, H., An Improved Particle Swarm Optimization (IPSO) Approach for Identification and Control of Stable and Unstable Systems, (2017) International Review of Automatic Control (IREACO), 10 (3), pp. 229-239.
https://doi.org/10.15866/ireaco.v10i3.11857

Jean-Philippe Condomines, Nonlinear Kalman Filtering for Multi-Sensor Navigation of Unmanned Aerial Vehicles (Elsevier Ltd, 2018).

Kamel, M.; Burri, M.; Siegwart, R. Linear vs Nonlinear MPC for Trajectory Tracking Applied to Rotary Wing Micro Aerial Vehicles. IFAC (2017), 50, 3463-3469.
https://doi.org/10.1016/j.ifacol.2017.08.849

Feng Y, Zhang C, Baek S, Rawashdeh S, Mohammadi A. Autonomous Landing of a UAV on a Moving Platform Using Model Predictive Control. Drones. 2018; 2(4):34.
https://doi.org/10.3390/drones2040034

Grillo, C., Montano, F., Automatic EKF Tuning for UAS Path Following in Turbulent Air, (2018) International Review of Aerospace Engineering (IREASE), 11 (6), pp. 241-246.
https://doi.org/10.15866/irease.v11i6.15122

W. Gai, Y. Zhou, M. Zhong, C. Sheng and J. Zhang, Simple Adaptive Control With an Adaptive Anti-Windup Compensator for the Unmanned Aerial Vehicle Attitude Control, in IEEE Access, vol. 8, (2020) pp. 52323-52332.
https://doi.org/10.1109/ACCESS.2020.2979741

J.B. Sharma, Applications of Small Unmanned Aircraft Systems (CRC Press, 2020).
https://doi.org/10.1201/9780429244117

Iswanto, I., Mujaahid, F., Rohmansyah, R., Ardi Nugraha, T., Shekher, V., Quadrotor Tracking Control Based on Optimized Fuzzy Logic Controller, (2019) International Review of Aerospace Engineering (IREASE), 12 (6), pp. 261-270.
https://doi.org/10.15866/irease.v12i6.16666

Habib, T., Replacement of In-Orbit Modern Spacecraft Attitude Determination and Estimation Algorithms with Neural Networks, (2021) International Review of Aerospace Engineering (IREASE), 14 (3), pp. 166-172.
https://doi.org/10.15866/irease.v14i3.19687

Geethapriya. S, N. Duraimurugan, S.P. Chokkalingam, Real-Time Object Detection with Yolo, International Journal of Engineering and Advanced Technology (IJEAT) (2019) ISSN: 2249 - 8958, Volume-8, Issue-3S.

Nguyen T., Park EA. Han J., Park DC. Min SYObject Detection Using Scale Invariant Feature Transform. In: Pan JS., Krömer P., Snášel V. (eds) Genetic and Evolutionary Computing. Advances in Intelligent Systems and Computing, (2014) vol 238. Springer, Cham.
https://doi.org/10.1007/978-3-319-01796-9_7

Żejmo M., Kowal M., Korbicz J., Monczak R. (2018) Nuclei Recognition Using Convolutional Neural Network and Hough Transform. In: Kościelny J., Syfert M., Sztyber A. (eds) Advanced Solutions in Diagnostics and Fault Tolerant Control. DPS 2017. Advances in Intelligent Systems and Computing, vol 635. Springer, Cham.
https://doi.org/10.1007/978-3-319-64474-5_26

Zhou, H., Yuan, Y., Shi, Y.: Object tracking using SIFT features and mean shift. Journal on Computer Vision and Image Understanding (2009) 113(3), 345-352.
https://doi.org/10.1016/j.cviu.2008.08.006

CANARD DRONES. Canard Drones - Smart Drones for Smart Airports (Canard S.L., 2018).

Herrera-Rubio, Jorge & Parra-Prada, Sergio. (2019). Experimental prototype for visual support in the calibration of the precision indicator lights of approach slope, for a landing track using a drone (Universidad de Pamplona, 2019).
https://doi.org/10.22463/0122820X.1795

AESA. Technical instruction for the maintenance of the movement area (AESA pub, 2019).

Refbacks

There are currently no refbacks.

Username
Password
Remember me