Skip to main content

Frontiers of Engineering Reports on Leading-Edge Engineering from the 2016 Symposium (2017) / Chapter Skim
Currently Skimming:

Autonomy Under Water: Ocean Sampling by Autonomous Underwater Vehicles - Derek A. Paley
Pages 43-50

The Chapter Skim interface presents what we've algorithmically identified as the most significant single chunk of text within every page in the chapter.
Select key terms on the right to highlight them within pages of the chapter.


From page 43...
... The capacity to maneuver relative to the flow field gives rise to challenges in cooperative control and adaptive sampling with the following recursive property: vehicles collect measurements of the ocean currents in order to estimate it; the estimate is used to guide the collection of subsequent measurements along sampling trajectories subject to currents that may be as large as the platform's through-water speed. Approaches to adaptive sampling of continuous environmental processes are distinguished by the characterization of the estimated process as statistical or dynamical.
Read the entire page →
From page 44...
... multivehicle control to stabilize the desired trajectories, and environmental process that maximizes the information collection across a mobile (right) nonlinear filtering to assimilate data; adaptive sampling refers to the reoptimization sensor network by adapting sampling trajectories using measurement data of sensor routes that occurs after data assimilation.
Read the entire page →
From page 45...
... Perhaps it is not surprising, given the range of these applications, that there are a variety of approaches advocated in the literature, including adaptation based on maximum a posteriori estimation, stochastic deployment policies, information-based methods, learning and artificial intelligence, deterministic methods with heuristic metrics, bioinspired source localization and gradient climbing, and nonparametric Bayesian models. The results described here differ from prior work on adaptive sampling of dynamical systems and random processes in the novel application of nonlinear observability and control coupled with recursive Bayesian filtering to optimize sensor routing for environmental sampling.
Read the entire page →
From page 46...
... A stochastic process whose variability changes when shifted in time or space is called nonstationary, and methods exist to parameterize nonstationary processes in oceanography and geostatistics. Indeed, nonstationary-based strategies have been applied to mobile sensor networks, though not based on a principled control design.
Read the entire page →
From page 47...
... For a stationary field the decorrelation scales are constant, whereas for a nonstationary field they may vary in space and time. The covariance function is used to derive a coordinate transformation that clusters measurements in space-time regions with short decorrelation scales and spreads measurements elsewhere, where the measurement demand is lower.
Read the entire page →
From page 48...
... . Figure 3 depicts the mapping error for a stationary field with a correlation scale estimated by a Bayesian filter.
Read the entire page →
From page 49...
... 2014. Multivehicle coverage control for nonstationary spatiotemporal fields.
Read the entire page →

Key Terms

  • adaptive sampling
  • covariance function
  • observability gramian
  • decorrelation scales
  • sampling trajectories

  • data assimilation
  • bayesian filter
  • recursive bayesian
  • empirical observability
  • spatiotemporal field

  • stationary field
  • nonstationary processes
  • mobile sensor
  • sensor routes
  • sensor routing

  • sampling platforms
  • sensor networks
  • multiple sampling
  • trajectories using
  • methods exist

  • random process
  • autonomous underwater
  • unknown process
  • estimated process
  • optimal interpolation

  • nominal value
  • spatiotemporal fields
  • rank condition
  • vertical motion
  • underwater vehicles


    • This material may be derived from roughly machine-read images, and so is provided only to facilitate research.
    More information on Chapter Skim is available.