Journal of Intelligent & Robotic Systems

, Volume 70, Issue 1–4, pp 527–544 | Cite as

Observability-based Optimization of Coordinated Sampling Trajectories for Recursive Estimation of a Strong, Spatially Varying Flowfield

  • Levi DeVries
  • Sharanya J. Majumdar
  • Derek A. Paley
Article

Abstract

Autonomous vehicles are effective environmental sampling platforms whose sampling performance can be optimized by path-planning algorithms that drive vehicles to specific regions of the operational domain containing the most informative data. In this paper, we apply tools from nonlinear observability, nonlinear control, and Bayesian estimation to derive a multi-vehicle control algorithm that steers vehicles to an optimal sampling formation in an estimated flowfield. Sampling trajectories are optimized using the empirical observability gramian, which quantifies the sensitivity of output measurements to variations of the flowfield parameters. We reconstruct the parameters of the flowfield from noisy flow measurements collected along the sampling trajectories using a recursive Bayesian filter.

Keywords

Multi-vehicle control Adaptive sampling Bayesian estimation 

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Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  • Levi DeVries
    • 1
  • Sharanya J. Majumdar
    • 2
  • Derek A. Paley
    • 3
  1. 1.Department of Aerospace EngineeringUniversity of MarylandCollege ParkUSA
  2. 2.Rosenstiel School of Marine and Atmospheric ScienceUniversity of MiamiMiamiUSA
  3. 3.Department of Aerospace Engineering and Institute for Systems ResearchUniversity of MarylandCollege ParkUSA

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