Vision System Measuring the Position of an Aircraft in Relation to the Runway during Landing Approach

Author:

Kordos Damian1ORCID,Krzaczkowski Paweł1,Rzucidło Paweł1ORCID,Gomółka Zbigniew2ORCID,Zesławska Ewa2ORCID,Twaróg Bogusław2ORCID

Affiliation:

1. Department of Avionics and Control Systems, Faculty of Mechanical Engineering and Aeronautics, Rzeszow University of Technology, al. Powstancow Warszawy 12, 35-959 Rzeszow, Poland

2. College of Natural Sciences, University of Rzeszow, Pigonia St. 1, 35-959 Rzeszow, Poland

Abstract

This paper presents a vision system that measures the position of an aircraft relative to the runway (RWY) during a landing approach. It was assumed that all the information necessary for a correct approach was based entirely on an analysis of the image of the runway and its surroundings. It was assumed that the way the algorithm works, as well as possible, should imitate the pilot’s perception of the runway. Taking into account the above and the fact that the infrastructure at each airport is different, it has been decided to use artificial neural networks with a dedicated learning process for any airport, based on the simulation environments. Such an action will enable the generation of a synthetic video sequence without the need for costly and time-consuming flights. The presented solution was tested in real flight conditions on an experimental aircraft, and the selected test results are presented in this article.

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

Reference26 articles.

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2. (2023, January 30). Safe Automatic Flight Back And Landing of Aircraft, Nr 593/KF/2006, 6-th Framework Program of the European Union, 2006–2009. Available online: https://trimis.ec.europa.eu/project/safe-automatic-flight-back-and-landing-aircraft.

3. Yang, T., Li, P., Zhang, H., Li, J., and Li, Z. (2018). Monocular Vision SLAM-Based UAV Autonomous Landing in Emergencies and Unknown Environments. Electronics, 7.

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5. Saj, V., Lee, B., Kalathil, D., and Benedict, M. (2022). Robust Reinforcement Learning Algorithm for Vision-based Ship Landing of UAVs. arXiv.

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