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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2014/42376
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| Title: | Autonomous onboard science data analysis for comet missions |
| Authors: | Thompson, David R.
Tran, Daniel Q.
McLaren, David
Chien, Steve A.
Bergman, Larry
Castaño, Rebecca
Doyle, Richard
Estlin, Tara
Lenda, Matthew |
| Keywords: | comets
plume detection
computer vision |
| Issue Date: | 4-Sep-2012 |
| Publisher: | Pasadena, CA : Jet Propulsion Laboratory, National Aeronautics and Space Administration, 2012. |
| Citation: | International Symposium on Artificial Intelligence Robotics and Automation in Space, Turnin, Italy, September 4-6, 2012 |
| Abstract: | Coming years will bring several comet rendezvous missions. The Rosetta spacecraft arrives at Comet 67P/Churyumov–Gerasimenko in 2014. Subsequent rendezvous might include a mission such as the proposed Comet Hopper with multiple surface landings, as well as Comet Nucleus Sample Return (CNSR) and Coma Rendezvous and Sample Return (CRSR). These encounters will begin to shed light on a population that, despite several previous flybys, remains mysterious and poorly understood. Scientists still have little direct knowledge of interactions between the nucleus and coma, their variation across different comets or their evolution over time. Activity may change on short timescales so it is challenging to characterize with scripted data acquisition. Here we investigate automatic onboard image analysis that could act faster than round-trip light time to capture unexpected outbursts and plume activity. We describe one edge-based method for detect comet nuclei and plumes, and test the approach on an existing catalog of comet images. Finally, we quantify benefits to specific measurement objectives by simulating a basic plume monitoring campaign. |
| URI: | http://hdl.handle.net/2014/42376 |
| Appears in Collections: | JPL TRS 1992+
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