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

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.