Planning a Flight Task System and the Principle of its Construction

Authors

DOI:

https://doi.org/10.18372/1990-5548.73.17013

Keywords:

unmanned aerial vehicle, Kalman method, Dijkstra algorithm, user interface, satellite image processing

Abstract

This work is devoted to the development of a system for automatic determination of the flight task for unmanned aerial vehicles. To implement such a system, the QT framework (a library of C++ classes and a set of tools for creating cross-platform applications) was used. It is proposed to introduce forecasting and routing modules into the system of automated determination of the flight task. An interface is developed for building a flight task that can be used to download, track key metrics from the unmanned aerial vehicles, and adjust the mission of the flight task. Advanced QT/Qml frameworks were used, which will allow the software product to be used on different operating systems, which will add flexibility in the use of system components.

Author Biographies

Victor Sineglazov , National Aviation University, Kyiv, Ukraine

Doctor of Engineering Science. Professor. Head of the Department

Aviation Computer-Integrated Complexes Department

Faculty of Air Navigation, Electronics and Telecommunications

Serhii Savchuk , National Aviation University, Kyiv, Ukraine

Bachelor

Aviation Computer-Integrated Complexes Department

Faculty of Air Navigation, Electronics and Telecommunications

References

B. Paden, M. Cap, S. Z. Yong, D. Yershow, and E. Frazzolo, “A survey of motion planning and control techniques for self-driving urban vehicles,” IEEE Transactions on Intelligent Vehicles, 1(1), 2016, p. 33–55. https://doi.org/10.1109/TIV.2016.2578706

M. Buehler, K. Lagnemma, and S. Sanjiv, (eds.). “The DARPA Urban Chal-lenge: Autonomous Vehicles in City Traffic,” Springer Tracts in Advanced Ro-botics. 2009. https://doi.org/10.1007/978-3-642-03991-1

H. Sak, A. Senior, and F. Beaufays, “Long short-term memory recurrent neural network architectures for large scale acoustic modeling,” In Fifteenth Annual Conference of the International Speech Communication Association, 2014. https://doi.org/10.21437/Interspeech.2014-80

https://en.wikipedia.org/wiki/Dijkstra's algorithm.

http://web.mit.edw/eranki/www/tutorials/search

C. E. Rasmussen, Processes for Machine Learning. The MIT Press. 2006. https://doi.org/10.7551/mitpress/3206.001.0001

L. R. Medsker, and L. C. Jain, Recurrent neural networks. Design and Ap-plications, 2001, 5.

S. Bonnin, T. H. Weisswange, F. Kummert, and J. Schmuedderich, “General behavior prediction by a combination of scenario-specific models,” IEEE Transactions on Intelligent Transportation Systems, 15(4), 2014, pp. 1478–1488. https://doi.org/10.1109/TITS.2014.2299340.

C. W. Hsu, C. C. Chang, and C. J. Lin, A Practical Guide to Support Vector Classification. Department of Computer Science, National Taiwan University, 2003.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” In Advances in Neural Information Processing Systems. 2012, pp. 1097–1105).

Downloads

Published

2022-11-24

Issue

Section

AUTOMATION AND COMPUTER-INTEGRATED TECHNOLOGIES