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An intelligent algorithm for autonomous scientific sampling with the VALKYRIE cryobot

Published online by Cambridge University Press:  25 September 2017

Evan B. Clark*
Stone Aerospace Inc., Del Valle, TX 78617, USA
Nathan E. Bramall
Leiden Measurement Technology, Sunnyvale, CA 94089, USA
Brent Christner
University of Florida, Gainesville, FL 32611, USA
Chris Flesher
Stone Aerospace Inc., Del Valle, TX 78617, USA
John Harman
Stone Aerospace Inc., Del Valle, TX 78617, USA
Bart Hogan
Stone Aerospace Inc., Del Valle, TX 78617, USA
Heather Lavender
Louisiana State University, Baton Rouge, LA 70803, USA
Scott Lelievre
Stone Aerospace Inc., Del Valle, TX 78617, USA
Joshua Moor
Stone Aerospace Inc., Del Valle, TX 78617, USA
Vickie Siegel
Stone Aerospace Inc., Del Valle, TX 78617, USA
William C. Stone
Stone Aerospace Inc., Del Valle, TX 78617, USA


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.

Research Article
Copyright © Cambridge University Press 2017 

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