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</...
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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 |
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