Validating Terrain Models in Digital Twins for Trustworthy sUAS Operations

Fuente: arXiv
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Main Authors: Bernal, Arturo Miguel Russell, Petterson, Maureen, Granadeno, Pedro Antonio Alarcon, Murphy, Michael, Mason, James, Cleland-Huang, Jane
Format: Preprint
Published: 2025
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author Bernal, Arturo Miguel Russell
Petterson, Maureen
Granadeno, Pedro Antonio Alarcon
Murphy, Michael
Mason, James
Cleland-Huang, Jane
author_facet Bernal, Arturo Miguel Russell
Petterson, Maureen
Granadeno, Pedro Antonio Alarcon
Murphy, Michael
Mason, James
Cleland-Huang, Jane
contents With the increasing deployment of small Unmanned Aircraft Systems (sUAS) in unfamiliar and complex environments, Environmental Digital Twins (EDT) that comprise weather, airspace, and terrain data are critical for safe flight planning and for maintaining appropriate altitudes during search and surveillance operations. With the expansion of sUAS capabilities through edge and cloud computing, accurate EDT are also vital for advanced sUAS capabilities, like geolocation. However, real-world sUAS deployment introduces significant sources of uncertainty, necessitating a robust validation process for EDT components. This paper focuses on the validation of terrain models, one of the key components of an EDT, for real-world sUAS tasks. These models are constructed by fusing U.S. Geological Survey (USGS) datasets and satellite imagery, incorporating high-resolution environmental data to support mission tasks. Validating both the terrain models and their operational use by sUAS under real-world conditions presents significant challenges, including limited data granularity, terrain discontinuities, GPS and sensor inaccuracies, visual detection uncertainties, as well as onboard resources and timing constraints. We propose a 3-Dimensions validation process grounded in software engineering principles, following a workflow across granularity of tests, simulation to real world, and the analysis of simple to edge conditions. We demonstrate our approach using a multi-sUAS platform equipped with a Terrain-Aware Digital Shadow.
format Preprint
id arxiv_https___arxiv_org_abs_2508_16104
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Validating Terrain Models in Digital Twins for Trustworthy sUAS Operations
Bernal, Arturo Miguel Russell
Petterson, Maureen
Granadeno, Pedro Antonio Alarcon
Murphy, Michael
Mason, James
Cleland-Huang, Jane
Software Engineering
Robotics
With the increasing deployment of small Unmanned Aircraft Systems (sUAS) in unfamiliar and complex environments, Environmental Digital Twins (EDT) that comprise weather, airspace, and terrain data are critical for safe flight planning and for maintaining appropriate altitudes during search and surveillance operations. With the expansion of sUAS capabilities through edge and cloud computing, accurate EDT are also vital for advanced sUAS capabilities, like geolocation. However, real-world sUAS deployment introduces significant sources of uncertainty, necessitating a robust validation process for EDT components. This paper focuses on the validation of terrain models, one of the key components of an EDT, for real-world sUAS tasks. These models are constructed by fusing U.S. Geological Survey (USGS) datasets and satellite imagery, incorporating high-resolution environmental data to support mission tasks. Validating both the terrain models and their operational use by sUAS under real-world conditions presents significant challenges, including limited data granularity, terrain discontinuities, GPS and sensor inaccuracies, visual detection uncertainties, as well as onboard resources and timing constraints. We propose a 3-Dimensions validation process grounded in software engineering principles, following a workflow across granularity of tests, simulation to real world, and the analysis of simple to edge conditions. We demonstrate our approach using a multi-sUAS platform equipped with a Terrain-Aware Digital Shadow.
title Validating Terrain Models in Digital Twins for Trustworthy sUAS Operations
topic Software Engineering
Robotics
url https://arxiv.org/abs/2508.16104