{"id":14517,"date":"2025-02-13T11:22:14","date_gmt":"2025-02-13T10:22:14","guid":{"rendered":"https:\/\/www.uni.lu\/snt-en\/?post_type=news&p=14517"},"modified":"2025-12-11T16:37:56","modified_gmt":"2025-12-11T15:37:56","slug":"helping-satellites-have-the-perfect-rendezvous","status":"publish","type":"news","link":"https:\/\/www.uni.lu\/snt-en\/news\/helping-satellites-have-the-perfect-rendezvous\/","title":{"rendered":"Helping satellites have the perfect rendezvous"},"content":{"rendered":"\n
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Researchers at SnT developed a dual-camera system combining visual and thermal imaging to improve autonomous satellite rendezvous in space.<\/strong><\/p>\n\n\n\n


Satellites must connect in space to repair craft and remove debris. Using a dual-camera system and AI, researchers at SnT have found a way to make these orbital rendezvous safer and more autonomous. Their research not only solved a key challenge for the space industry but also led to the creation of a new company that makes space system testing more accessible and affordable.<\/p>\n\n\n\n

\nThe challenge of autonomous satellite rendezvous<\/h2>\n\n\n\n

Space is getting crowded. Satellites need to meet up to perform maintenance and repairs, and there is an increasing need to clean up debris. All this demands exact navigation. That’s why spacecraft are equipped with cameras that guide them to their targets, which start as distant points of light. As satellites move closer, tracking becomes difficult: the sun’s position changes quickly in orbit, creating extreme shifts between light and shadow that affect visibility.<\/p>\n\n\n\n


The International Space Station demonstrates these challenges through its regular spacecraft visits. It receives about 8-10 spacecraft per year for crew rotations and supplies. Yet missions to repair satellites in orbit remain rare. Each approach requires detailed programming specific to that target satellite. This makes operations slow and limits what we can do in space. Automated systems would make these operations more efficient.
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\nA multi-modal sensing solution: visual cameras and thermal imaging<\/h2>\n\n\n\n

In the MEET-A project, researchers at SnT partnered up with LMO, a company based in Luxembourg that developed space observation technology. This successful collaboration paved the way for numerous follow-up projects.<\/p>\n\n\n\n


Mohamed Ali, Vincent Gaudilli\u00e8re, and Arun Rathinam<\/a> developed a solution using two types of cameras. Standard visual cameras work in normal lighting, while thermal cameras can track objects in low lighting conditions. Their multimodal system fuses data from both sources for better navigation.
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The project achieved three key objectives. First, they enhanced spacecraft recognition. Before calculating position, the sensing system must identify the target accurately to ensure successful rendezvous. The team developed new detection algorithms<\/a> that perform better than traditional methods at recognizing different types of spacecraft and debris.<\/p>\n\n\n\n


Second, they improved tracking accuracy. The system uses knowledge about orbital paths to predict movement. Since spacecraft follow specific trajectories, past positions help predict future locations. This temporal knowledge<\/a> makes tracking more reliable than systems that treat each image independently.<\/p>\n\n\n\n

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\n \u201f<\/span>\n Using both thermal and standard cameras gave us a significant advantage in tracking spacecraft\u201d\n<\/blockquote>\n
\n \n\"Arunkumar <\/figure><\/div>\n\n
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Arun Rathinam<\/p>\n\n\n

Postdoctoral researcher<\/p>\n\n

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Third, they improved the feature extraction and data fusion from both camera types: the multimodal system combines the best data from each source. “Using both thermal and standard cameras gave us a significant advantage in tracking spacecraft,” notes Rathinam. “While standard cameras work well in bright conditions, thermal imaging allows us to maintain tracking even in low-lighting conditions which are regularly encountered in orbit.\u201d<\/p>\n\n\n\n

\nBuilding space solutions with synthetic data<\/h2>\n\n\n\n

Testing space technology presents unique challenges. Getting real data from satellites thousands of kilometers away is difficult and expensive. Even when real data exists, it needs manual labeling – marking the exact position and details of objects in each image (picture number 1 below).
The team solved this by creating synthetic data – computer-generated information that mimics real conditions (picture number 2 below). Their simulator creates detailed space scenes, complete with accurate lighting and movement. The key advantage is that this data comes pre-labeled with exact positions and orientations, saving countless hours of manual work.<\/p>\n\n\n\n

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