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A Method for Evaluating and Selecting Suitable Hardware for Deployment of Embedded System on UAVs.

Nicolas Mandel1,2, Michael Milford1,2, Felipe Gonzalez1,2

  • 1Australian Centre of Excellence for Robotic Vision, Queensland University of Technology, Brisbane QLD 4000, Australia.

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Summary

Selecting appropriate hardware for Unmanned Aerial Vehicle (UAV)-based remote sensing is crucial. This study introduces a method combining computational profiling and decision-making models to optimize hardware selection under Size, Weight, Power, and Computational constraints.

Keywords:
UAVcomputer architecturedecision makingnavigationsemantics

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Area of Science:

  • Robotics and Computer Vision
  • Remote Sensing Technology
  • Embedded Systems Engineering

Background:

  • The increasing use of Unmanned Aerial Vehicles (UAVs) for remote sensing is challenged by Size, Weight, Power, and Computational (SWPC) constraints.
  • Deploying advanced computer vision and robotics algorithms on UAVs requires specialized knowledge of system architecture to overcome these limitations.

Purpose of the Study:

  • To develop and demonstrate a systematic method for evaluating and selecting suitable hardware for UAV-based remote sensing applications.
  • To address the challenges posed by SWPC constraints in the deployment of sophisticated algorithms on UAVs.

Main Methods:

  • Integration of computational monitoring (profiling) with ISO 25000 software quality standards.
  • Application of the Analytic Hierarchy Process (AHP) as a decision-making model to fuse profiling data and quality metrics.
  • Hardware-in-the-loop simulations to profile three software-hardware alternatives.
  • Monte Carlo simulations to analyze the impact of decision parameters on alternative preferences.

Main Results:

  • The proposed method provides an informed basis for selecting embedded systems for UAV remote sensing.
  • Results highlight that local weights significantly influence the preference for a specific hardware alternative.
  • The approach effectively relates complex parameters to guide hardware suitability decisions for different deployment scenarios.

Conclusions:

  • The developed method offers a robust framework for optimizing hardware selection in resource-constrained UAV systems.
  • Informed decisions regarding hardware suitability can be made by systematically evaluating complex parameters.
  • This approach facilitates the effective deployment of advanced algorithms in UAV-based remote sensing applications.