Autonomy for Ocean Worlds Exploration
Abstract
One of the most profound discoveries in planetary exploration is the evidence for large quantities of liquid water on several bodies in our Solar System, aptly named "Ocean Worlds". In an effort to extrapolate our understanding of life on Earth to the cosmos, "go to the water" has become the guiding principle in our search for evidence of extraterrestrial life. Thus, Ocean Worlds have become key astrobiology targets, and many outstanding questions can only be answered through direct contact with their subsurface liquid water.
The challenges posed by robotic subsurface missions on Ocean Worlds are immense, and advanced Autonomy may be among the most demanding technology developments that will be required. The current state of practice for autonomous operations of Mars rovers and distant spacecraft, for example, are highly robust, deliberative, and protective; that is, the system makes a plan that is "safe" with respect to known uncertainties and promptly triggers a "safe mode" in the event of any anomalies. Ocean Worlds, however, present an environment that is far more uncertain, dynamic, and communication-constrained, which will require autonomy that is adaptive, reactive, and resilient. For example, the dynamic nature of plume ejecta on Enceladus or the harsh radiation of Europa prohibit human-in-the-loop control, especially during long-duration communication blackouts such as the two-week period during solar conjunction. Ocean World probes must be equipped with the ability to learnfrom their interactions with the environment, react to imminent hazards, and make real-time decisions to respond to anomalies. Information for the Design Reference Mission report was synthesized additionally by Hiro Ono, Kalind Carpenter, Ben Hochman, Michael Wolf, John-Pierre de la Croix and John-Pierre Fleurial.- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2019
- Bibcode:
- 2019AGUFMIN34A..05C
- Keywords:
-
- 0394 Instruments and techniques;
- ATMOSPHERIC COMPOSITION AND STRUCTURE;
- 0520 Data analysis: algorithms and implementation;
- COMPUTATIONAL GEOPHYSICS;
- 0555 Neural networks;
- fuzzy logic;
- machine learning;
- COMPUTATIONAL GEOPHYSICS;
- 0594 Instruments and techniques;
- COMPUTATIONAL GEOPHYSICS