Analysis of adaptive sampling techniques for underwater vehicles

Andres Mora, Colin Ho, Srikanth Saripalli

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


A critical problem in planning sampling paths for autonomous underwater vehicles (AUVs) is correctly balancing two issues. First, obtaining an accurate scalar field estimation and second, efficiently utilizing the stored energy capacity of the sampling vehicle. Adaptive sampling approaches can only provide solutions when real time and a priori environmental data is available. In this paper we present an analysis of adaptive sampling methodologies for AUVs. In particular, we analyze various sampling path strategies including systematic and stratified random patterns within a wide range of sampling densities and their impact in the energy consumption of the vehicle through a cost-evaluation function. Our study demonstrates that a systematic spiral sampling path strategy is optimal for high-variance scalar fields for all sampling densities and low-variance scalar fields when sampling is sparse. In addition, our results show that the random spiral sampling path strategy is found to be optimal for low-variance scalar fields when sampling is dense.

Original languageEnglish (US)
Pages (from-to)111-122
Number of pages12
JournalAutonomous Robots
Issue number2-3
StatePublished - Oct 2013


  • Adaptive sampling
  • Autonomous underwater vehicles
  • Experimental
  • Kriging
  • Optimal

ASJC Scopus subject areas

  • Artificial Intelligence


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