ARC-LIGHT: Algorithm for Robust Characterization of Lunar Surface Imaging for Ground Hazards and Trajectory
Safe and reliable lunar landings are crucial for future exploration of the Moon. The regolith ejected by a lander’s rocket exhaust plume represents a significant obstacle in achieving this goal. It prevents spacecraft from reliably utilizing their navigation sensors to monitor their trajectory and s...
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MDPI AG
2025-02-01
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| author | Alexander Cushen Ariana Bueno Samuel Carrico Corrydon Wettstein Jaykumar Ishvarbhai Adalja Mengxiang Shi Naila Garcia Yuliana Garcia Mirko Gamba Christopher Ruf |
| author_facet | Alexander Cushen Ariana Bueno Samuel Carrico Corrydon Wettstein Jaykumar Ishvarbhai Adalja Mengxiang Shi Naila Garcia Yuliana Garcia Mirko Gamba Christopher Ruf |
| author_sort | Alexander Cushen |
| collection | DOAJ |
| description | Safe and reliable lunar landings are crucial for future exploration of the Moon. The regolith ejected by a lander’s rocket exhaust plume represents a significant obstacle in achieving this goal. It prevents spacecraft from reliably utilizing their navigation sensors to monitor their trajectory and spot emerging surface hazards as they near the surface. As part of NASA’s 2024 Human Lander Challenge (HuLC), the team at the University of Michigan developed an innovative concept to help mitigate this issue. We developed and implemented a machine learning (ML)-based sensor fusion system, ARC-LIGHT, that integrates sensor data from the cameras, lidars, or radars that landers already carry but disable during the final landing phase. Using these data streams, ARC-LIGHT will remove erroneous signals and recover a useful detection of the surface features to then be used by the spacecraft to correct its descent profile. It also offers a layer of redundancy for other key sensors, like inertial measurement units. The feasibility of this technology was validated through development of a prototype algorithm, which was trained on data from a purpose-built testbed that simulates imaging through a dusty environment. Based on these findings, a development timeline, risk analysis, and budget for ARC-LIGHT to be deployed on a lunar landing was created. |
| format | Article |
| id | doaj-art-e04b51dc9de147aa8c1aa1f07eec6afb |
| institution | Kabale University |
| issn | 2226-4310 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
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| series | Aerospace |
| spelling | doaj-art-e04b51dc9de147aa8c1aa1f07eec6afb2025-08-20T03:40:41ZengMDPI AGAerospace2226-43102025-02-0112317710.3390/aerospace12030177ARC-LIGHT: Algorithm for Robust Characterization of Lunar Surface Imaging for Ground Hazards and TrajectoryAlexander Cushen0Ariana Bueno1Samuel Carrico2Corrydon Wettstein3Jaykumar Ishvarbhai Adalja4Mengxiang Shi5Naila Garcia6Yuliana Garcia7Mirko Gamba8Christopher Ruf9Climate and Space Sciences and Engineering Department, University of Michigan, Ann Arbor, MI 48109, USAClimate and Space Sciences and Engineering Department, University of Michigan, Ann Arbor, MI 48109, USADepartment of Aerospace Engineering, University of Michigan, Ann Arbor, MI 48109, USAElectrical Engineering and Computer Science Department, University of Michigan, Ann Arbor, MI 48109, USAClimate and Space Sciences and Engineering Department, University of Michigan, Ann Arbor, MI 48109, USAElectrical Engineering and Computer Science Department, University of Michigan, Ann Arbor, MI 48109, USADepartment of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USADepartment of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USADepartment of Aerospace Engineering, University of Michigan, Ann Arbor, MI 48109, USAClimate and Space Sciences and Engineering Department, University of Michigan, Ann Arbor, MI 48109, USASafe and reliable lunar landings are crucial for future exploration of the Moon. The regolith ejected by a lander’s rocket exhaust plume represents a significant obstacle in achieving this goal. It prevents spacecraft from reliably utilizing their navigation sensors to monitor their trajectory and spot emerging surface hazards as they near the surface. As part of NASA’s 2024 Human Lander Challenge (HuLC), the team at the University of Michigan developed an innovative concept to help mitigate this issue. We developed and implemented a machine learning (ML)-based sensor fusion system, ARC-LIGHT, that integrates sensor data from the cameras, lidars, or radars that landers already carry but disable during the final landing phase. Using these data streams, ARC-LIGHT will remove erroneous signals and recover a useful detection of the surface features to then be used by the spacecraft to correct its descent profile. It also offers a layer of redundancy for other key sensors, like inertial measurement units. The feasibility of this technology was validated through development of a prototype algorithm, which was trained on data from a purpose-built testbed that simulates imaging through a dusty environment. Based on these findings, a development timeline, risk analysis, and budget for ARC-LIGHT to be deployed on a lunar landing was created.https://www.mdpi.com/2226-4310/12/3/177Plume Surface Interaction (PSI)lunarlandingnavigationmachine learningArtemis |
| spellingShingle | Alexander Cushen Ariana Bueno Samuel Carrico Corrydon Wettstein Jaykumar Ishvarbhai Adalja Mengxiang Shi Naila Garcia Yuliana Garcia Mirko Gamba Christopher Ruf ARC-LIGHT: Algorithm for Robust Characterization of Lunar Surface Imaging for Ground Hazards and Trajectory Aerospace Plume Surface Interaction (PSI) lunar landing navigation machine learning Artemis |
| title | ARC-LIGHT: Algorithm for Robust Characterization of Lunar Surface Imaging for Ground Hazards and Trajectory |
| title_full | ARC-LIGHT: Algorithm for Robust Characterization of Lunar Surface Imaging for Ground Hazards and Trajectory |
| title_fullStr | ARC-LIGHT: Algorithm for Robust Characterization of Lunar Surface Imaging for Ground Hazards and Trajectory |
| title_full_unstemmed | ARC-LIGHT: Algorithm for Robust Characterization of Lunar Surface Imaging for Ground Hazards and Trajectory |
| title_short | ARC-LIGHT: Algorithm for Robust Characterization of Lunar Surface Imaging for Ground Hazards and Trajectory |
| title_sort | arc light algorithm for robust characterization of lunar surface imaging for ground hazards and trajectory |
| topic | Plume Surface Interaction (PSI) lunar landing navigation machine learning Artemis |
| url | https://www.mdpi.com/2226-4310/12/3/177 |
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