{"events":[{"id":232356,"global_id":"spacenews.com?id=232356","global_id_lineage":["spacenews.com?id=232356"],"author":"10908","status":"publish","date":"2024-01-31 11:11:29","date_utc":"2024-01-31 16:11:29","modified":"2024-01-31 11:11:30","modified_utc":"2024-01-31 16:11:30","url":"https:\/\/spacenews.com\/event\/ai-for-space-workshop-3rd-workshop\/","rest_url":"https:\/\/spacenews.com\/wp-json\/tribe\/events\/v1\/events\/232356","title":"AI for Space Workshop – 3rd workshop","description":"
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The general emphasis of AI4Space is vision and learning algorithms for autonomous space systems, which operate in the Earth\u2019s orbital regions, cislunar orbit, planetary bodies (e.g., the moon, Mars and asteroids), and interplanetary space. Emphasis is also placed on novel sensors and processors for vision and learning in space, mitigating the challenges of the space environment towards vision and learning (e.g., radiation, extreme temperatures), and fundamental difficulties in vision and learning for space (e.g., lack of training data, unknown operating environments).<\/p>\n

A specific list of topics is as follows:<\/p>\n

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  • Vision and learning for spacecraft navigation and operations (e.g., rendezvous, proximity operations, docking, space maneuvers, entry descent landing).<\/li>\n
  • Vision and learning for space robots (e.g., rovers, UAVs, UGVs, UUWs) and multi-agent systems.<\/li>\n
  • Mapping and global positioning on planetary bodies (moon, Mars, asteroids), including celestial positioning.<\/li>\n
  • Onboard AI for Earth observation applications (e.g. near-real-time disaster monitoring, distributed learning on satellites, tip and cue satellite-based systems).<\/li>\n
  • Onboard AI for satellite operations (e.g. AI-based star trackers, fault detection isolation and recovery).<\/li>\n
  • Space debris monitoring and mitigation.<\/li>\n
  • Sensors for space applications (e.g., optical, multispectral, lidar, radar, neuromorphic).<\/li>\n
  • Onboard compute hardware for vision and learning (e.g., neural network accelerators, neuromorphic processors).<\/li>\n
  • Mitigating challenges of the space environment (e.g., radiation, thermal) to vision and learning systems.<\/li>\n
  • Datasets, transfer learning and domain gap.<\/li>\n<\/ul>\n\n\t
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