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Machine Learning for Unmanned Aerial Vehicle Routing on Rough Terrain

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Advances in Computer Science for Engineering and Education VI (ICCSEEA 2023)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 181))

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Abstract

The paper considers the main methods of machine learning for unmanned aerial vehicle (drone) routing, simulates an environment for testing the flight of a drone, as well as a model with a neural network for the unmanned routing of a drone on rough terrain. The potential use of unmanned aerial vehicles is limited because today the control of drone flight is carried out in a semi-automatic mode on the operator's commands, or in remote mode using a control panel. Such a system is unstable to the human factor because it depends entirely on the operator. The relevance of the work is to use machine learning methods for drone routing, which will provide stable control of the unmanned aerial vehicle to perform a specific task. As a result of the work, a neural network architecture was developed, which was successfully implemented in a test model for routing an unmanned aerial vehicle on rough terrain. The test results showed that the unmanned aerial vehicle successfully avoids obstacles in the new environment.

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Author information

Authors and Affiliations

  1. Petro Mohyla Black Sea National University, 68th Desantnykiv Street, 10, Mykolaiv, 54000, Ukraine

    Ievgen Sidenko, Artem Trukhov, Galyna Kondratenko & Yuriy Kondratenko

  2. C-Job Nikolayev, Artyleriyska Street, 17/6, Mykolaiv, 54006, Ukraine

    Yuriy Zhukov

  3. Institute of Artificial Intelligence Problems, Mala Zhytomyrs’ka Street, 11/5, Kyiv, 01001, Ukraine

    Yuriy Kondratenko

Authors
  1. Ievgen Sidenko
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  2. Artem Trukhov
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  3. Galyna Kondratenko
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  4. Yuriy Zhukov
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  5. Yuriy Kondratenko
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Corresponding author

Correspondence to Ievgen Sidenko .

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Editors and Affiliations

  1. Faculty of Applied Mathematics, National Technical University of Ukraine “Igor Sikorsky Kiev Polytechnic Institute”, Ukraine, Kyiv, Ukraine

    Zhengbing Hu

  2. Faculty of Applied Mathematics, National Technical University of Ukraine “Igor Sikorsky Kiev Polytechnic Institute”, Ukraine, Kyiv, Ukraine

    Ivan Dychka

  3. Halmos College of Arts and Sciences, Nova Southeastern University, Fort Lauderdale, FL, USA

    Matthew He

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Sidenko, I., Trukhov, A., Kondratenko, G., Zhukov, Y., Kondratenko, Y. (2023). Machine Learning for Unmanned Aerial Vehicle Routing on Rough Terrain. In: Hu, Z., Dychka, I., He, M. (eds) Advances in Computer Science for Engineering and Education VI. ICCSEEA 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 181. Springer, Cham. https://doi.org/10.1007/978-3-031-36118-0_56

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