An energy-aware airborne dynamic data-driven application system for persistent sampling and surveillance

Eric W. Frew, Brian Argrow, Adam Houston, Chris Weiss, Jack Elston

Research output: Contribution to journalConference articlepeer-review

15 Scopus citations


This paper describes an energy-aware, airborne, dynamic data-driven application systems for persistent sensing in complex atmospheric conditions. The work combines i.) new onboard and remote real-time, wind sensing capabilities; ii.) online models for planning based on Gaussian processes for onboard data and dynamic atmospheric models that assimilate Doppler radar data; and iii.) a hierarchical guidance and control framework with algorithms that can adapt to environmental, sensing, and computational resources. The novel aspects of this work include real-time synthesis of multiple Doppler radar data into wind field measurements; creation of atmospheric models for online planning that can be run inside guidance loops; guidance algorithms based on stochastic dynamic programming and ordered upwind methods that can adapt planning horizons, cost function approximations, and mesh representations of the environment; and throttling algorithms that manage the adaptation of the models and guidance algorithms in response to computational resources.

Original languageEnglish
Pages (from-to)2008-2017
Number of pages10
JournalProcedia Computer Science
StatePublished - 2013
Event13th Annual International Conference on Computational Science, ICCS 2013 - Barcelona, Spain
Duration: Jun 5 2013Jun 7 2013


  • Atmospheric models
  • Dynamic data-driven application system
  • Planning and control
  • Unmanned aircraft system


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