Ancient hydrothermal systems are a high-priority target for a future Mars sample return mission because they contain energy sources for microbes and can preserve organic materials (Farmer, 2000; MEPAG Next Decade Science Analysis Group, 2008; McLennan et al.,2012; Michalski et al.,2017). Characterizing these large, heterogeneous systems with a remote explorer is difficult due to communications bandwidth and latency; such a mission will require significant advances in spacecraft autonomy. Science autonomy uses intelligent sensor platforms that analyze data in real-time, setting measurement and downlink priorities to provide the best information toward investigation goals. Such automation must relate abstract science hypotheses to the measurable quantities available to the robot. This study captures these relationships by formalizing traditional “science traceability matrices” into probabilistic models. This permits experimental design techniques to optimize future measurements and maximize information value toward the investigation objectives, directing remote explorers that respond appropriately to new data. Such models are a rich new language for commanding informed robotic decision making in physically grounded terms. We apply these models to quantify the information content of different rover traverses providing profiling spectroscopy of Cuprite Hills, Nevada. We also develop two methods of representing spatial correlations using human-defined maps and remote sensing data. Model unit classifications are broadly consistent with prior maps of the site's alteration mineralogy, indicating that the model has successfully represented critical spatial and mineralogical relationships at Cuprite. Key Words: Autonomous science—Imaging spectroscopy—Alteration mineralogy—Field geology—Cuprite—AVIRIS-NG—Robotic exploration. Astrobiology 18, 934–954.
Spatial spectroscopic models for remote exploration
|Spatial spectroscopic models for remote exploration
|Mary Ann Liebert, Inc.
|Crustal Geophysics and Geochemistry Science Center
|Google Analytic Metrics