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

The use of UAVs for remote sensing is increasing. In this paper, we demonstrate a method for evaluating and selecting suitable hardware to be used for deployment of algorithms for UAV-based remote sensing under considerations of <i>Size</i>, <i>Weight</i>, <i>Power</...

Full description

Bibliographic Details
Main Authors: Nicolas Mandel, Michael Milford, Felipe Gonzalez
Format: Article
Language:English
Published: MDPI AG 2020-08-01
Series:Sensors
Subjects:
UAV
Online Access:https://www.mdpi.com/1424-8220/20/16/4420
id doaj-5a32cb3ac0d743138962c9abe91a2319
record_format Article
spelling doaj-5a32cb3ac0d743138962c9abe91a23192020-11-25T03:21:29ZengMDPI AGSensors1424-82202020-08-01204420442010.3390/s20164420A Method for Evaluating and Selecting Suitable Hardware for Deployment of Embedded System on UAVsNicolas Mandel0Michael Milford1Felipe Gonzalez2Australian Centre of Excellence for Robotic Vision, Queensland University of Technology, Brisbane, QLD 4000, AustraliaAustralian Centre of Excellence for Robotic Vision, Queensland University of Technology, Brisbane, QLD 4000, AustraliaAustralian Centre of Excellence for Robotic Vision, Queensland University of Technology, Brisbane, QLD 4000, AustraliaThe use of UAVs for remote sensing is increasing. In this paper, we demonstrate a method for evaluating and selecting suitable hardware to be used for deployment of algorithms for UAV-based remote sensing under considerations of <i>Size</i>, <i>Weight</i>, <i>Power</i>, and <i>Computational</i> constraints. These constraints hinder the deployment of rapidly evolving computer vision and robotics algorithms on UAVs, because they require intricate knowledge about the system and architecture to allow for effective implementation. We propose integrating computational monitoring techniques—profiling—with an industry standard specifying software quality—ISO 25000—and fusing both in a decision-making model—the analytic hierarchy process—to provide an informed decision basis for deploying embedded systems in the context of UAV-based remote sensing. One software package is combined in three software–hardware alternatives, which are profiled in hardware-in-the-loop simulations. Three objectives are used as inputs for the decision-making process. A Monte Carlo simulation provides insights into which decision-making parameters lead to which preferred alternative. Results indicate that local weights significantly influence the preference of an alternative. The approach enables relating complex parameters, leading to informed decisions about which hardware is deemed suitable for deployment in which case.https://www.mdpi.com/1424-8220/20/16/4420UAVcomputer architecturedecision makingnavigationsemantics
collection DOAJ
language English
format Article
sources DOAJ
author Nicolas Mandel
Michael Milford
Felipe Gonzalez
spellingShingle Nicolas Mandel
Michael Milford
Felipe Gonzalez
A Method for Evaluating and Selecting Suitable Hardware for Deployment of Embedded System on UAVs
Sensors
UAV
computer architecture
decision making
navigation
semantics
author_facet Nicolas Mandel
Michael Milford
Felipe Gonzalez
author_sort Nicolas Mandel
title A Method for Evaluating and Selecting Suitable Hardware for Deployment of Embedded System on UAVs
title_short A Method for Evaluating and Selecting Suitable Hardware for Deployment of Embedded System on UAVs
title_full A Method for Evaluating and Selecting Suitable Hardware for Deployment of Embedded System on UAVs
title_fullStr A Method for Evaluating and Selecting Suitable Hardware for Deployment of Embedded System on UAVs
title_full_unstemmed A Method for Evaluating and Selecting Suitable Hardware for Deployment of Embedded System on UAVs
title_sort method for evaluating and selecting suitable hardware for deployment of embedded system on uavs
publisher MDPI AG
series Sensors
issn 1424-8220
publishDate 2020-08-01
description The use of UAVs for remote sensing is increasing. In this paper, we demonstrate a method for evaluating and selecting suitable hardware to be used for deployment of algorithms for UAV-based remote sensing under considerations of <i>Size</i>, <i>Weight</i>, <i>Power</i>, and <i>Computational</i> constraints. These constraints hinder the deployment of rapidly evolving computer vision and robotics algorithms on UAVs, because they require intricate knowledge about the system and architecture to allow for effective implementation. We propose integrating computational monitoring techniques—profiling—with an industry standard specifying software quality—ISO 25000—and fusing both in a decision-making model—the analytic hierarchy process—to provide an informed decision basis for deploying embedded systems in the context of UAV-based remote sensing. One software package is combined in three software–hardware alternatives, which are profiled in hardware-in-the-loop simulations. Three objectives are used as inputs for the decision-making process. A Monte Carlo simulation provides insights into which decision-making parameters lead to which preferred alternative. Results indicate that local weights significantly influence the preference of an alternative. The approach enables relating complex parameters, leading to informed decisions about which hardware is deemed suitable for deployment in which case.
topic UAV
computer architecture
decision making
navigation
semantics
url https://www.mdpi.com/1424-8220/20/16/4420
work_keys_str_mv AT nicolasmandel amethodforevaluatingandselectingsuitablehardwarefordeploymentofembeddedsystemonuavs
AT michaelmilford amethodforevaluatingandselectingsuitablehardwarefordeploymentofembeddedsystemonuavs
AT felipegonzalez amethodforevaluatingandselectingsuitablehardwarefordeploymentofembeddedsystemonuavs
AT nicolasmandel methodforevaluatingandselectingsuitablehardwarefordeploymentofembeddedsystemonuavs
AT michaelmilford methodforevaluatingandselectingsuitablehardwarefordeploymentofembeddedsystemonuavs
AT felipegonzalez methodforevaluatingandselectingsuitablehardwarefordeploymentofembeddedsystemonuavs
_version_ 1724614323884523520