Skip to main content
×
Home
    • Aa
    • Aa

An intelligent algorithm for autonomous scientific sampling with the VALKYRIE cryobot

  • Evan B. Clark (a1), Nathan E. Bramall (a2), Brent Christner (a3), Chris Flesher (a1), John Harman (a1), Bart Hogan (a1), Heather Lavender (a4), Scott Lelievre (a1), Joshua Moor (a1), Vickie Siegel (a1) and William C. Stone (a1)...
Abstract
Abstract

The development of algorithms for agile science and autonomous exploration has been pursued in contexts ranging from spacecraft to planetary rovers to unmanned aerial vehicles to autonomous underwater vehicles. In situations where time, mission resources and communications are limited and the future state of the operating environment is unknown, the capability of a vehicle to dynamically respond to changing circumstances without human guidance can substantially improve science return. Such capabilities are difficult to achieve in practice, however, because they require intelligent reasoning to utilize limited resources in an inherently uncertain environment. Here we discuss the development, characterization and field performance of two algorithms for autonomously collecting water samples on VALKYRIE (Very deep Autonomous Laser-powered Kilowatt-class Yo-yoing Robotic Ice Explorer), a glacier-penetrating cryobot deployed to the Matanuska Glacier, Alaska (Mission Control location: 61°42′09.3″N 147°37′23.2″W). We show performance on par with human performance across a wide range of mission morphologies using simulated mission data, and demonstrate the effectiveness of the algorithms at autonomously collecting samples with high relative cell concentration during field operation. The development of such algorithms will help enable autonomous science operations in environments where constant real-time human supervision is impractical, such as penetration of ice sheets on Earth and high-priority planetary science targets like Europa.

Copyright
Corresponding author
e-mail: evan.bock.clark@stoneaerospace.com
References
Hide All
BabaioffM., ImmorlicaN., KempeD. & KleinbergR. (2007). A Knapsack Secretary Problem with Applications. Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques, Lecture Notes in Computer Science. Springer, Berlin, Heidelberg, pp. 1628.
BabaioffM., ImmorlicaN., KempeD. & KleinbergR. (2008). Online auctions and generalized secretary problems. SIGecom. Exch. 7(2), 7:17:11.
Bramall et al. (2016). Unpub. data.
CastanoR. et al. (2007 a). Oasis: onboard autonomous science investigation system for opportunistic rover science. J. Field Robot. 24(5), 379397.
CastanoR. et al. (2007 b). Onboard Autonomous Rover Science. In 2007 IEEE Aerospace Conf., pp. 113.
ChienS., KnightR., StechertA., SherwoodR. & RabideauG. (1999). Integrated planning and execution for autonomous spacecraft. In Aerospace Conf., 1999. Proc. 1999 IEEE, vol. 1, pp. 263271.
ChienS. et al. (2014). Agile Science: Using Onboard Autonomy for Primitive Bodies and Deep Space Exploration (SpaceOps). http://sensorweb.jpl.nasa.gov/public/papers/chien_isairas2014_agile.pdf.
ChienS. et al. (2015). Using autonomy flight software to improve science return on earth observing one. J. Aerosp. Comput. Inf. Commun. 2, pp. 196216. http://www-aig.jpl.nasa.gov/public/planning/papers/chien_JACIC2005_UsingAutonomy.pdf.
ChowY.S. & RobbinsH. (1963). On optimal stopping rules. Z Wahrscheinlichkeitstheorie Verwandte Geb. 2(1), 3349.
ChristnerB.C. (2006). Limnological conditions in Subglacial Lake Vostok, Antarctica. Limnol. Oceanogr. 51(6), 24852501.
Christner et al. (2016). Unpub. data.
ClarkE.B. et al. (2017). VALKYRIE: Field Campaign Results and Autonomous Sampling for a Laser-powered Cryobot. In Astrobiology Science Conf. 2017. http://www.lpi.usra.edu/meetings/abscicon2017/pdf/3706.pdf.
DynkinE.B. (1963). The optimum choice of the instant for stopping a Markov process. Soviet Math. Dokl. 4, pp. 627629.
GirdharY., GiguèreP. & DudekG. (2013). Autonomous adaptive exploration using realtime online spatiotemporal topic modeling. Int. J. Robot. Res. 33(4), pp. 645657. doi: 10.1177/0278364913507325.
GulickV.C., MorrisR.L., RuzonM.A. & RoushT.L. (2001). Autonomous image analyses during the 1999 Marsokhod rover field test. J. Geophys. Res. 106(E4), 77457763.
KaeliJ.W. (2013). Computational strategies for understanding underwater optical image datasets. Dissertation, Massachusetts Institute of Technology. http://hdl.handle.net/1721.1/85539.
KleinbergR. (2005). A Multiple-choice Secretary Algorithm with Applications to Online Auctions. In Proc. of the Sixteenth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA ‘05. Society for Industrial and Applied Mathematics, Philadelphia, PA, USA, pp. 630631.
LindleyD.V. (1961). Dynamic programming and decision theory. J. R. Stat. Soc. Ser. C, Appl. Stat. 10(1), 3951.
ManasseM.S. & McGeochL.A. (1988). Competitive Algorithms for On-line Problems. In Proc. ACM Symposium on Theory of Computing, pp. 322333.
SharifH., RalchenkoM., SamsonC. & ElleryA. (2015). Autonomous rock classification using Bayesian image analysis for Rover-based planetary exploration. Comput. Geosci. 83, 153167.
SmithR.N. et al. (2011). Persistent ocean monitoring with underwater gliders: Adapting sampling resolution. J Field Robot. 28(5), 714741.
SosikH.M. & OlsonR.J. (2007). Automated taxonomic classification of phytoplankton sampled with imaging-in-flow cytometry. Limnol. Oceanogr. Methods 5(204), e216.
StoneW.C., HoganB., SiegelV., LelievreS. & FlesherC. (2014). Progress towards an optically powered cryobot. Ann. Glaciol. 55(65), 113.
StoneW.C. et al. (2015). VALKYRIE: Field Campaign Results for a Laser-Powered Cryobot. AbSciCon 2015 (Universities Space Research Association). http://www.hou.usra.edu/meetings/abscicon2015/pdf/7203.pdf.
ThompsonD.R. et al. (2012). Agile science operations: a new approach for primitive bodies exploration. In Proc. of SpaceOps 2012 Conf
WagnerM.D. et al. (2001). The Science Autonomy System of the Nomad robot. In Robotics and Automation, 2001. Proc. 2001 ICRA. IEEE International Conf. on, vol. 2, pp. 17421749.
WoodsM. et al. (2009). Autonomous science for an ExoMars Rover-like mission. J. Field Robot. 26(4), 358390.
Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

International Journal of Astrobiology
  • ISSN: 1473-5504
  • EISSN: 1475-3006
  • URL: /core/journals/international-journal-of-astrobiology
Please enter your name
Please enter a valid email address
Who would you like to send this to? *
×

Keywords:

Metrics

Altmetric attention score

Full text views

Total number of HTML views: 1
Total number of PDF views: 15 *
Loading metrics...

Abstract views

Total abstract views: 124 *
Loading metrics...

* Views captured on Cambridge Core between 25th September 2017 - 19th October 2017. This data will be updated every 24 hours.