Jorge Cortés
Professor
Cymer Corporation Endowed Chair
Distributed gradient ascent of random fields by robotic sensor networks
J. Cortés
Proceedings of the IEEE Conference on Decision and Control, New Orleans, Louisiana, USA, pp. 3120-3126
Abstract
This paper considers robotic sensor networks performing
spatially-distributed estimation tasks. A robotic network equipped
with footprint sensors is deployed in an environment of interest,
and takes successive measurements of a physical process modeled as a
spatial random field. Taking a Bayesian perspective on the kriging
interpolation technique from geostatistics, we design the \algoDK to
estimate the distribution of the random field and of its gradient.
The proposed algorithm makes use of a novel distributed strategy to
compute weighted least squares estimates when measurements are
spatially correlated. This strategy results from the combination of
the Jacobi overrelaxation method with dynamic consensus algorithms.
The network agents use the information gained on the spatial field
to implement a gradient ascent coordination algorithm, whose
convergence is analyzed via stochastic Lyapunov functions in the
absence of measurement errors. We illustrate our results in
simulation.
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Mechanical and Aerospace Engineering,
University of California, San Diego
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cortes at ucsd.edu
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