Hostname: page-component-586b7cd67f-t7czq Total loading time: 0 Render date: 2024-11-24T05:24:14.775Z Has data issue: false hasContentIssue false
  • English
  • Français

An intelligent algorithm for autonomous scientific sampling with the VALKYRIE cryobot

Published online by Cambridge University Press:  25 September 2017

Evan B. Clark Open the ORCID record for Evan B. Clark [Opens in a new window] ,
Nathan E. Bramall ,
Brent Christner ,
Chris Flesher ,
John Harman ,
Bart Hogan ,
Heather Lavender ,
Scott Lelievre ,
Joshua Moor  and
Vickie Siegel
...Show all authors
Evan B. Clark*
Affiliation:
Stone Aerospace Inc., Del Valle, TX 78617, USA
Nathan E. Bramall
Affiliation:
Leiden Measurement Technology, Sunnyvale, CA 94089, USA
Brent Christner
Affiliation:
University of Florida, Gainesville, FL 32611, USA
Chris Flesher
Affiliation:
Stone Aerospace Inc., Del Valle, TX 78617, USA
John Harman
Affiliation:
Stone Aerospace Inc., Del Valle, TX 78617, USA
Bart Hogan
Affiliation:
Stone Aerospace Inc., Del Valle, TX 78617, USA
Heather Lavender
Affiliation:
Louisiana State University, Baton Rouge, LA 70803, USA
Scott Lelievre
Affiliation:
Stone Aerospace Inc., Del Valle, TX 78617, USA
Joshua Moor
Affiliation:
Stone Aerospace Inc., Del Valle, TX 78617, USA
Vickie Siegel
Affiliation:
Stone Aerospace Inc., Del Valle, TX 78617, USA
William C. Stone
Affiliation:
Stone Aerospace Inc., Del Valle, TX 78617, USA
*
e-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

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.

Keywords

agile scienceanalog environmentsastrobiologyautonomous scienceautonomycryobotEnceladusEuropafield roboticsVALKYRIE
Type
Research Article
Information
International Journal of Astrobiology , Volume 17 , Special Issue 3: Robotic Astrobiology , July 2018 , pp. 247 - 257
Copyright
Copyright © Cambridge University Press 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Babaioff, M., Immorlica, N., Kempe, D. & Kleinberg, R. (2007). A Knapsack Secretary Problem with Applications. Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques, Lecture Notes in Computer Science. Springer, Berlin, Heidelberg, pp. 16–28.Google Scholar
Babaioff, M., Immorlica, N., Kempe, D. & Kleinberg, R. (2008). Online auctions and generalized secretary problems. SIGecom. Exch. 7(2), 7:1–7:11.CrossRefGoogle Scholar
Bramall, et al. (2016). Unpub. data.Google Scholar
Castano, R. et al. (2007 a). Oasis: onboard autonomous science investigation system for opportunistic rover science. J. Field Robot. 24(5), 379–397.CrossRefGoogle Scholar
Castano, R. et al. (2007 b). Onboard Autonomous Rover Science. In 2007 IEEE Aerospace Conf., pp. 1–13.CrossRefGoogle Scholar
Chien, S., Knight, R., Stechert, A., Sherwood, R. & Rabideau, G. (1999). Integrated planning and execution for autonomous spacecraft. In Aerospace Conf., 1999. Proc. 1999 IEEE, vol. 1, pp. 263–271.CrossRefGoogle Scholar
Chien, S. 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.Google Scholar
Chien, S. et al. (2015). Using autonomy flight software to improve science return on earth observing one. J. Aerosp. Comput. Inf. Commun. 2, pp. 196–216. http://www-aig.jpl.nasa.gov/public/planning/papers/chien_JACIC2005_UsingAutonomy.pdf.CrossRefGoogle Scholar
Chow, Y.S. & Robbins, H. (1963). On optimal stopping rules. Z Wahrscheinlichkeitstheorie Verwandte Geb. 2(1), 33–49.CrossRefGoogle Scholar
Christner, B.C. (2006). Limnological conditions in Subglacial Lake Vostok, Antarctica. Limnol. Oceanogr. 51(6), 2485–2501.CrossRefGoogle Scholar
Christner, et al. (2016). Unpub. data.Google Scholar
Clark, E.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.Google Scholar
Dynkin, E.B. (1963). The optimum choice of the instant for stopping a Markov process. Soviet Math. Dokl. 4, pp. 627–629.Google Scholar
Girdhar, Y., Giguère, P. & Dudek, G. (2013). Autonomous adaptive exploration using realtime online spatiotemporal topic modeling. Int. J. Robot. Res. 33(4), pp. 645–657. doi: 10.1177/0278364913507325.CrossRefGoogle Scholar
Gulick, V.C., Morris, R.L., Ruzon, M.A. & Roush, T.L. (2001). Autonomous image analyses during the 1999 Marsokhod rover field test. J. Geophys. Res. 106(E4), 7745–7763.CrossRefGoogle Scholar
Kaeli, J.W. (2013). Computational strategies for understanding underwater optical image datasets. Dissertation, Massachusetts Institute of Technology. http://hdl.handle.net/1721.1/85539.CrossRefGoogle Scholar
Kleinberg, R. (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. 630–631.Google Scholar
Lindley, D.V. (1961). Dynamic programming and decision theory. J. R. Stat. Soc. Ser. C, Appl. Stat. 10(1), 39–51.Google Scholar
Manasse, M.S. & McGeoch, L.A. (1988). Competitive Algorithms for On-line Problems. In Proc. ACM Symposium on Theory of Computing, pp. 322–333.CrossRefGoogle Scholar
Sharif, H., Ralchenko, M., Samson, C. & Ellery, A. (2015). Autonomous rock classification using Bayesian image analysis for Rover-based planetary exploration. Comput. Geosci. 83, 153–167.CrossRefGoogle Scholar
Smith, R.N. et al. (2011). Persistent ocean monitoring with underwater gliders: Adapting sampling resolution. J Field Robot. 28(5), 714–741.CrossRefGoogle Scholar
Sosik, H.M. & Olson, R.J. (2007). Automated taxonomic classification of phytoplankton sampled with imaging-in-flow cytometry. Limnol. Oceanogr. Methods 5(204), e216.CrossRefGoogle Scholar
Stone, W.C., Hogan, B., Siegel, V., Lelievre, S. & Flesher, C. (2014). Progress towards an optically powered cryobot. Ann. Glaciol. 55(65), 1–13.CrossRefGoogle Scholar
Stone, W.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.Google Scholar
Thompson, D.R. et al. (2012). Agile science operations: a new approach for primitive bodies exploration. In Proc. of SpaceOps 2012 ConfCrossRefGoogle Scholar
Wagner, M.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. 1742–1749.CrossRefGoogle Scholar
Woods, M. et al. (2009). Autonomous science for an ExoMars Rover-like mission. J. Field Robot. 26(4), 358–390.CrossRefGoogle Scholar