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An Energy-Aware Airborne Dynamic Data-Driven Application System for Persistent Sampling and Surveillance

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Handbook of Dynamic Data Driven Applications Systems

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Abstract

Fixed-wing aerial robotic technology has advanced to the point where platforms fly persistent surveillance missions far from remote operators. Likewise, complex atmospheric phenomena can be simulated in near real time with increasing levels of fidelity. Furthermore, cloud computing technology enables distributed computation on large, dynamic datasets. Combining autonomous airborne sensors with environmental models dispersed over multiple communication and computation channels enables the collection of information essential for examining the fundamental behavior of atmospheric phenomena. This chapter describes progress toward the development of an autonomous airborne scientist using the dynamic data-driven application system (DDDAS) paradigm.

The chapter describes the five components of the energy-aware DDDAS (EA-DDDAS) system: (1) dual-Doppler synthesis, (2) atmospheric models for online planning (AMOP), (3) a wind field database, (4) a lattice planner, and (5) a trajectory optimization layer (TOL). EA-DDDAS combines unmanned aircraft systems, meshed networked communication, dynamic data-driven application system techniques, a cloud computing infrastructure, numerical weather models, and onboard sensors. The chapter describes the existing DDDAS system architecture along with results from recent field deployments validating and assessing various subsystems.

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Notes

  1. 1.

    http://recuv.colorado.edu/~elstonj/met_map/.

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Author information

Authors and Affiliations

  1. University of Colorado, Boulder, CO, USA

    Eric W. Frew & Brian Argrow

  2. University of Nebraska, Lincoln, NE, USA

    Adam Houston

  3. Texas Tech University, Lubbock, TX, USA

    Chris Weiss

Authors
  1. Eric W. Frew
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  2. Brian Argrow
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  3. Adam Houston
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  4. Chris Weiss
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Corresponding author

Correspondence to Eric W. Frew .

Editor information

Editors and Affiliations

  1. InfoSymbiotic Systems Society, Bethesda, MD, USA

    Frederica Darema

  2. MOVEJ Analytics, Dayton, OH, USA

    Erik P. Blasch

  3. Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA

    Sai Ravela

  4. Information Directorate, Air Force Laboratory, Rome, NY, USA

    Alex J. Aved

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Frew, E.W., Argrow, B., Houston, A., Weiss, C. (2023). An Energy-Aware Airborne Dynamic Data-Driven Application System for Persistent Sampling and Surveillance. In: Darema, F., Blasch, E.P., Ravela, S., Aved, A.J. (eds) Handbook of Dynamic Data Driven Applications Systems. Springer, Cham. https://doi.org/10.1007/978-3-031-27986-7_16

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  • DOI: https://doi.org/10.1007/978-3-031-27986-7_16

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