Neural Network-Aided Optical Navigation for Precise Lunar Descent Operations Article Swipe
Simone Andolfo
,
Antonio Genova
,
Fabio Valerio Buonomo
,
Anna Maria Gargiulo
,
Mohamed El Awag
,
Pierluigi Federici
,
Riccardo Teodori
,
Riccardo La Grassa
,
Cristina Re
,
G. Cremonese
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/aerospace12030195
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/aerospace12030195
Advanced navigation capabilities are essential for precise landing operations, enabling access to critical lunar sites and supporting future lunar infrastructure. To achieve accurate positioning, innovative navigation methods leveraging neural network frameworks are being developed to detect distinctive lunar surface features, such as craters, from imaging data. By matching detected features with known landmarks stored in an onboard reference database, key navigation measurements are retrieved to refine the spacecraft trajectory, enabling real-time planning for hazard avoidance. This work presents a crater-based navigation system for planetary descent operations, which leverages a robust machine learning approach for crater detection in optical images. A thorough analysis of the attainable detection accuracies was performed by evaluating the network performance on diverse sets of synthetic images rendered at different illumination conditions through a custom Blender-based pipeline. Simulation campaigns, based on the JAXA Smart Lander for Investigating Moon mission, were then carried out to demonstrate the system’s performance, achieving final position errors consistent with 3 − σ uncertainties lower than 100 m on the horizontal plane at altitudes as low as 10 km. This level of accuracy is key to achieving enhanced control during the approach and vertical descent phases, thereby ensuring operational safety and facilitating precise landing.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/aerospace12030195
- https://www.mdpi.com/2226-4310/12/3/195/pdf?version=1740738581
- OA Status
- gold
- Cited By
- 1
- References
- 63
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4408057469
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4408057469Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/aerospace12030195Digital Object Identifier
- Title
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Neural Network-Aided Optical Navigation for Precise Lunar Descent OperationsWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-02-27Full publication date if available
- Authors
-
Simone Andolfo, Antonio Genova, Fabio Valerio Buonomo, Anna Maria Gargiulo, Mohamed El Awag, Pierluigi Federici, Riccardo Teodori, Riccardo La Grassa, Cristina Re, G. CremoneseList of authors in order
- Landing page
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https://doi.org/10.3390/aerospace12030195Publisher landing page
- PDF URL
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https://www.mdpi.com/2226-4310/12/3/195/pdf?version=1740738581Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2226-4310/12/3/195/pdf?version=1740738581Direct OA link when available
- Concepts
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Descent (aeronautics), Artificial neural network, Computer science, Celestial navigation, Artificial intelligence, Aerospace engineering, Engineering, Astronomy, PhysicsTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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- Related works (count)
-
10Other works algorithmically related by OpenAlex
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