A Systematic Review of Methods for Cleaning FOD on Runways

Foreign objects (FOD) that can be found on runways represent a great danger for aircraft on take-off or landing; FOD cleaning systems for runways fulfill two basic tasks: "Detection" and "Cleaning"; which have been developed in different works and researches in the last years, ge...

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Veröffentlicht in:2023 17th International Conference on Engineering of Modern Electric Systems (EMES) S. 1 - 4
Hauptverfasser: Vidal, Yuri Lester Silva, Arapa, Christofer Alex Diaz, De Jesus Huamani Mejia, Alvaro, Supo, Elvis, Gutierrez, Jorge Luis Apaza, Salas, Paul Eduardo Pinto
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 09.06.2023
Schlagworte:
Cleaning
detection
FOD walk
Legged locomotion
methods
Radar
Radar detection
Robot vision systems
runway
Systematics
Visualization
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Zusammenfassung:Foreign objects (FOD) that can be found on runways represent a great danger for aircraft on take-off or landing; FOD cleaning systems for runways fulfill two basic tasks: "Detection" and "Cleaning"; which have been developed in different works and researches in the last years, generally treating both tasks as one. Over time, a great advance in detection systems was obtained, starting at first from the traditional method, commonly called "FOD Walk", employing visual inspection by human personnel, to the use of radars and cameras that employ complex detection algorithms. On the other hand, in the cleaning task, the progress is not the same, since in many airports and air bases around the world the inefficient FOD Walk is still used, however, in some places there are automated cleaning systems that become efficient, using robots or vehicles that clean the FOD. In this paper we will review all these methods, classifying them and analyzing their operation, the quality with which they perform the work, and the opportunities they offer when using a specific method. In the end, a discussion and conclusions are offered that will help future researchers on the subject.
DOI:10.1109/EMES58375.2023.10171778