{"id":1067861,"date":"2024-05-25T02:44:28","date_gmt":"2024-05-25T06:44:28","guid":{"rendered":"https:\/\/www.immortalitymedicine.tv\/tricorder-tech-a-i-machine-learning-adaptive-sampling-with-pixl-on-mars-perseverance-astrobiology-astrobiology-news\/"},"modified":"2024-08-18T11:40:12","modified_gmt":"2024-08-18T15:40:12","slug":"tricorder-tech-a-i-machine-learning-adaptive-sampling-with-pixl-on-mars-perseverance-astrobiology-astrobiology-news","status":"publish","type":"post","link":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/machine-learning\/tricorder-tech-a-i-machine-learning-adaptive-sampling-with-pixl-on-mars-perseverance-astrobiology-astrobiology-news.php","title":{"rendered":"Tricorder Tech: A.I. \/ Machine Learning: Adaptive Sampling With PIXL On Mars Perseverance &#8211; Astrobiology &#8211; Astrobiology News"},"content":{"rendered":"<p><p>          Model of the Mars Perseverance rover showing the location          of PIXL at the end of the robotic arm.  NASA        <\/p>\n<p>    Editors note: From the papers Introduction:    At the time of this writing, PIXLs adaptive sampling    capability has been operating on Mars for over 951 sols    (martian days), with over 52 scans analyzed. To our knowledge,    it represents the first case of autonomous decision-making    through compositional analysis performed by a spacecraft on    another planet. And from the Conclusion: We have    successfully demonstrated new adaptive sampling technology with    PIXL on the Mars Perseverance rover. To our knowledge, this has    enabled the first autonomous decision-making based on real-tie    compositional analysis by an exploration spacecraft. Almost all    the rules were implemented through machine learning, trained    with compositional analysis of PIXL Mars data taken earlier in    the mission.  <\/p>\n<p>    As we send our droids to other worlds to study local geology    and search for biosignatures well need to have them equipped    with as much autonomy as we can provide  autonomy that we can    upgrade based on mission experience  as well as autonomy that    can be improved by the droid itself based on its own    experience. When we join our robotic partners on site well    need to adopt the same approach with the tools that we bring    with us. Well need the latest analytical tools in our Base    Camp laboratory and out in the field on Away Team sorties    embedded inside our tricorders and other sensors that we bring    along.  <\/p>\n<p>    These tools will need to be forward\/backward compatible so as    to be upgradeable. Even with fast communication back to Earth,    the combination of varying message lags and inevitable    bandwidth constraints, an emphasis will be placed on in situ    capabilities  as will creativity on the part of both humans    and robots.  <\/p>\n<p>    If we are going to expend the large amount of financial and    national resources to send these missions  eventually with    humans  we need to equip them as best we can  with tools that    learn  just like human crews do. Our droids are leading the    way  programmed by smart humans back home. Here is one example    already at work in the field- on Mars.  <\/p>\n<p>    Planetary rovers can use onboard data analysis to adapt their    measurement plan on the fly, improving the science value of    data collected between commands from Earth.  <\/p>\n<p>    This paper describes the implementation of an adaptive sampling    algorithm used by PIXL, the X-ray fluorescence spectrometer of    the Mars 2020 Perseverance rover.  <\/p>\n<p>    PIXL is deployed using the rover arm to measure X-ray spectra    of rocks with a scan density of several thousand points over an    area of typically 5 x 7 mm. The adaptive sampling algorithm is    programmed to recognize points of interest and to increase the    signal-to-noise ratio at those locations by performing longer    integrations.  <\/p>\n<p>    Preparations for the PIXL scan on sol 294    (Quartier). Most PIXL scans (about 60%) have been conducted on    an abraded patch that exposes the sub-surface of the rock (an    additional 32% are on natural surfaces and 8% on regolith). In    this example an abraded patch of 50-mm diameter and    approximately 7-mm deep is produced using the Sampling and    Caching Subsystem (Moeller et al., 2021). This patch has been    cleared of dust to roughly 40-mm diameter using the Gas Dust    Removal Tool. The PIXL scan on sol 294 is 77 mm (placement not    shown), about one seventh the diameter of the abraded patch,    and has 3299 points spaced 0.125 mm apart.     astro-ph.EP  <\/p>\n<p>    PIXLISE view of carbonates detected on sol 879    (Gabletop Mountain). Shown here is a compositional map of    carbonates derived from PIXLISE expressions. The expressions    are equations that can use counts from PIXLs two detectors to    estimate weight percents of compositions of interest while    taking into account the effects of diffraction. The    grey-colored points are where divide-by-zero errors have    occurred and should be ignored.  astro-ph.EP  <\/p>\n<p>    5 x 7 mm PIXL scan of target Lake Haiyaha (sol 851)    displaying elemental abundances of Cr2O3 in green. The scan    only contains 3 Cr-rich grains (chromites) comprising 9 PMCs    total (0.4% of the map scan [9 PMCs\/2346 PMCs]). The    Chromite-bearing PMCS are defined here as PMCs with >2 wt%    Cr2O3.  astro-ph.EP  <\/p>\n<p>    Two approaches are used to formulate the sampling rules based    on past quantification data: 1) Expressions that isolate    particular regions within a ternary compositional diagram, and    2) Machine learning rules that threshold for a high weight    percent of particular compounds.  <\/p>\n<p>    The design of the rulesets are outlined and the performance of    the algorithm is quantified using measurements from the surface    of Mars.  <\/p>\n<p>    To our    knowledge, PIXLs adaptive sampling represents the first    autonomous decision-making based on real-time compositional    analysis by a spacecraft on the surface of another    planet.  <\/p>\n<p>    Peter R. Lawson (1), Tanya V. Kizovski (2), Michael M. Tice    (3), Benton C. Clark III (4), Scott J. VanBommel (5), David R.    Thompson (6), Lawrence A. Wade (6), Robert W. Denise (1),    Christopher M. Heirwegh (6), W. Timothy Elam (7), Mariek E.    Schmidt (2), Yang Liu (6), Abigail C. Allwood (6), Martin S.    Gilbert (6), Benjamin J. Bornstein (6) ((1) Retired  Jet    Propulsion Laboratory, California Institute of Technology,    Pasadena, CA, USA (2) Brock University, St. Catherines, ON,    Canada (3) Texas A&M University, College Station, TX, USA    (4) Space Sciences Institute, Boulder, CO, USA (5) Washington    University in St. Louis, St. Louis, MO, USA (6) Jet Propulsion    Laboratory, California Institute of Technology, Pasadena, CA,    USA (7) University of Washington, Seattle, WA, USA)  <\/p>\n<p>    Comments: 24 pages including 11 figures and 7 tables. Submitted    for publication to the journal Icarus    Subjects: Earth and Planetary Astrophysics (astro-ph.EP);    Instrumentation and Methods for Astrophysics (astro-ph.IM)    Cite as: arXiv:2405.14471 [astro-ph.EP] (or arXiv:2405.14471v1    [astro-ph.EP] for this version)    <a href=\"https:\/\/doi.org\/10.48550\/arXiv.2405.14471\" rel=\"nofollow\">https:\/\/doi.org\/10.48550\/arXiv.2405.14471<\/a>    Focus to learn more    Submission history    From: Peter Lawson    [v1] Thu, 23 May 2024 11:57:02 UTC (17,776 KB)    <a href=\"https:\/\/arxiv.org\/abs\/2405.14471\" rel=\"nofollow\">https:\/\/arxiv.org\/abs\/2405.14471<\/a>    Astrobiology,  <\/p>\n<p><!-- Auto Generated --><\/p>\n<p>Read more:<br \/>\n<a target=\"_blank\" href=\"https:\/\/astrobiology.com\/2024\/05\/tricorder-tech-a-i-machine-learning-adaptive-sampling-with-pixl-on-mars-perseverance.html\" title=\"Tricorder Tech: A.I. \/ Machine Learning: Adaptive Sampling With PIXL On Mars Perseverance - Astrobiology - Astrobiology News\" rel=\"noopener\">Tricorder Tech: A.I. \/ Machine Learning: Adaptive Sampling With PIXL On Mars Perseverance - Astrobiology - Astrobiology News<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p> Model of the Mars Perseverance rover showing the location of PIXL at the end of the robotic arm. NASA Editors note: From the papers Introduction: At the time of this writing, PIXLs adaptive sampling capability has been operating on Mars for over 951 sols (martian days), with over 52 scans analyzed. To our knowledge, it represents the first case of autonomous decision-making through compositional analysis performed by a spacecraft on another planet.  <a href=\"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/machine-learning\/tricorder-tech-a-i-machine-learning-adaptive-sampling-with-pixl-on-mars-perseverance-astrobiology-astrobiology-news.php\">Continue reading <span class=\"meta-nav\">&rarr;<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"limit_modified_date":"","last_modified_date":"","_lmt_disableupdate":"","_lmt_disable":"","footnotes":""},"categories":[1231415],"tags":[],"class_list":["post-1067861","post","type-post","status-publish","format-standard","hentry","category-machine-learning"],"modified_by":null,"_links":{"self":[{"href":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/wp-json\/wp\/v2\/posts\/1067861"}],"collection":[{"href":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/wp-json\/wp\/v2\/comments?post=1067861"}],"version-history":[{"count":0,"href":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/wp-json\/wp\/v2\/posts\/1067861\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/wp-json\/wp\/v2\/media?parent=1067861"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/wp-json\/wp\/v2\/categories?post=1067861"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/wp-json\/wp\/v2\/tags?post=1067861"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}