Observability-based optimization of coordinated sampling trajectories for recursive estimation of a strong, spatially varying flowfield

Levi Devries, Sharanya J. Majumdar, Derek A. Paley

Research output: Contribution to journalArticle

17 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)527-544
Number of pages18
JournalJournal of Intelligent and Robotic Systems: Theory and Applications
Volume70
Issue number1-4
DOIs
StatePublished - Apr 1 2013

Keywords

  • Adaptive sampling
  • Bayesian estimation
  • Multi-vehicle control

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

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