Download - Cyborg Astrobiologist -- Basic Idea:
Question: “Can an autonomous Robotic Astrobiologist someday be taught to understand a geological scene like the one pictured below?”
“The Cyborg Astrobiologist: Teaching Computers to Find Uncommon or Novel
Areas of Geological Scenery in Real-time”Network Conference of the Alexander von Humboldt Foundation, Muenster, Germany, November 2008.Authors: Patrick C. McGuire1,5,6,*, Enrique Díaz Martínez1,3, Jens Ormö1, Javier Gómez Elvíra1, Virginia Souza-Egipsy1, Helge Ritter2, Markus Oesker2, Robert Haschke2, Jörg Ontrup2, Florian Schmidt2, Alexandra Bartolo4, Richard Bose5, Lorenz Wendt6
Institutions:
1Centro de Astrobiología (CAB, INTA/CSIC), Instituto Nacional Técnica Aeroespacial, Torrejón de Ardoz, Madrid, Spain: Robotics Laboratory, Planetary Geology Laboratory, Transdisciplinary Laboratory
2Universität Bielefeld, Germany, Neuroinformatics Group, Computer Science Department, Technische Fakultät3(currently at) Dirreción de Geología y Geofísica, Instituto Geológico y Minero de España, Tres Cantos, Madrid, Spain4Engineering Faculty, University of Malta, Malta 5McDonnell Center for the Space Sciences, Washington University, St. Louis, Missouri, USA 6Institute for Geosciences, Freie Universität Berlin, Department of Planetary Science and Remote Sensing, Berlin, Germany
*(corresponding author) Email: [email protected]
Cyborg Astrobiologist -- Basic Idea:
Real-time Image-segmentation
Real-time Selection of Interest PointsWearable
Computer
Human Mobility
Geologists’ intuition & high-level planning
Video Camera, Firewire communication
The Cyborg Astrobiologist:Geological Field Missions to
Rivas Vaciamadrid, Guadalajara and Malta
McGuire, Ormö et al.,Int’l Journal of Astrobiology(2004)
McGuire, Diaz-Martinez et al.,Int’l Journal of Astrobiology(2005)
Algorithm: Simple Uncommon Map
Simple Image Uncommon MapBrighter = “more uncommon”(For the image on the left)
Mission Riba1: Tripod Position “#2”,Inside the Cyborg Astrobiologist’s ‘Brain’
Cooccurence
2D Histogram
for Saturation
Sat(pix[i][j])
Sat
(pix
[i][
j+1]
)
McGuire, Diaz-Martinez et al.,Int’l Journal of Astrobiology(2005)
Not Yet
Not Yet
Novelty Detection with the FamE (FamiliarityEnergy) Hopfield Neural Net (after R. Bogacz, U. Bristol)
Compute:
averages: <H>,< S>, <I> for each segment
x = (<H>,< S>, <I> )T
If (Energy_Hopfield( x ) < -N/4 then
Novel = FALSE;
Else {
Novel = TRUE;
Store Pattern x in Hopfield Net; }
Repeat for all segments;
Repeat for all incoming images;
Novelty Detection:Cyborg Astrobiologist gets a memory (of Familiar patterns)
The Cyborg Astrobiologist:Ported from a wearable computer to the Astrobiology Phone-cam
Before:
Now:
Bartolo, McGuire et al.,International Journal of Astrobiology(2007)
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(Cyborg Astrobiologist)++tested at Rivas Vaciamadrid (Sept 6, 2005)
#1(Cam)
#19(Micro)
#23(Micro)
Lichen=WhiteSporing = RedRock=Brown
Lichen = Black, OrangeGypsum Crystal = SmoothGray
Novel SegmentsOrig (Cam or Micro)
Full-colorImage Segmentation
UncommonMap
Uncommonness
Lichen=Algae + Fungus(in Symbiosis)
Microscope in Field!
Head-mounted Display failed in field
Pictured here: tablet display
Tripod!!
Cyborg Astrobiologist, with microscope, studying lichens
#18(Micro)Lichen=WhiteSporing = RedRock=Brown
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Summary of Concept: Developing computer-vision technologies to automate some of a Human Geologist’s low-level thought processes. We are testing these computer vision technologies in the field in real-time with low-cost platforms, with the intention of later putting these algorithms on robotic platforms for exploring the Moon and Mars.
Currently working with collaborators in Madrid, Malta, St. Louis, Bielefeld and Berlin, in order to:
a) Do more comprehensive field tests at sites of geological and astrobiological interest using both the uncommon-map and novelty-detection systems.
b) Make the phone-cam system work with a field laptop enabled with Bluetooth, for automatic transfer of the images from the phone to the laptop and (marked-
up) back to the phone. This should speed up the testing.
Future: 0) Improved image-segmentation (to include texture) 1) Improved Novelty Detection (better statistical measures) 2) Improved cameras (hyperspectral cameras) 3) Improve technological readiness for planetary robotics missions.
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