A Design-Agnostic Framework for the Salvage and Reverse-Engineering of Unmanned Aerial Vehicles (UAV)

This paper presents an Explorative Reverse Engineering (ERE) framework for the analysis and reverse-engineering of unmanned aerial vehicles (UAV). The framework is based on the life cycle theory and customized for the re-engineering of UAVs. It starts by identifying the UAVs' subsystems and key components, analyzing their functions, and exploring opportunities to replace the components with alternatives that serve similar functionalities. The procedure is tested for a black box aerial UAV with an unknown brand and performance. The components: airframe, propulsion system, avionics, sensors, communication systems, and payload modules are among the components that have been identified. Each part is carefully and accurately described, emphasizing its significance in the overall operation of the UAV. Different devices that can take the role of the identified components have been investigated. Using regression Machine Learning algorithm, two novel formula is established to calculate the thrust needed to take off and payload capacity. As the result of a successful implementation of the proposed farmwork, the identified elements have been used successfully to re-engineer a full-functioning real UAV.

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Publication Details

Published
2025-02-18
DOI
https://doi.org/10.31972/iceit2024.077
Primary Topic
Manufacturing Process and Optimization
Type
article
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0.00
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article

A Design-Agnostic Framework for the Salvage and Reverse-Engineering of Unmanned Aerial Vehicles (UAV)

Thoalfaqqar Sabah Saleh, Ibrahim Ismael Hamarash
Manufacturing Process and Optimization
article

A Design-Agnostic Framework for the Salvage and Reverse-Engineering of Unmanned Aerial Vehicles (UAV)

Thoalfaqqar Sabah Saleh, Ibrahim Ismael Hamarash
article en

Abstract

This paper presents an Explorative Reverse Engineering (ERE) framework for the analysis and reverse-engineering of unmanned aerial vehicles (UAV). The framework is based on the life cycle theory and customized for the re-engineering of UAVs. It starts by identifying the UAVs' subsystems and key components, analyzing their functions, and exploring opportunities to replace the components with alternatives that serve similar functionalities. The procedure is tested for a black box aerial UAV with an unknown brand and performance. The components: airframe, propulsion system, avionics, sensors, communication systems, and payload modules are among the components that have been identified. Each part is carefully and accurately described, emphasizing its significance in the overall operation of the UAV. Different devices that can take the role of the identified components have been investigated. Using regression Machine Learning algorithm, two novel formula is established to calculate the thrust needed to take off and payload capacity. As the result of a successful implementation of the proposed farmwork, the identified elements have been used successfully to re-engineer a full-functioning real UAV.

Openalex Percentile: Top 97%
Manufacturing Process and Optimization
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