Jorge Cortés
Professor
Cymer Corporation Endowed Chair
Spatial statistics and distributed estimation by
robotic sensor networks
R. Graham, J. Cortés
Proceedings of the American Control Conference, Baltimore, Maryland, 2010, pp. 2422-2427
Abstract
Networks of environmental sensors are playing an increasingly
important role in scientific studies of the ocean, rivers, and the
atmosphere. Robotic sensors can improve the efficiency of data
collection, adapt to changes in the environment, and provide a
robust response to individual failures. Complex statistical
techniques come into play in the analysis of environmental
processes. Consequently, the operation of robotic sensors must be
driven by statistically-aware algorithms that make the most of the
network capabilities for data collection and fusion. At the same
time, such algorithms need to be distributed and scalable to make
robotic networks capable of operating in an autonomous and robust
fashion. The combination of these two objectives, complex
statistical modeling and distributed coordination, presents grand
technical challenges: traditional statistical modeling and inference
assume full availability of all measurements and central
computation. While the availability of data at a central location
is certainly a desirable property, the paradigm for distributed
motion coordination builds on partial, fragmented information. This
work surveys recent progress at bridging the gap between
sophisticated statistical modeling and distributed motion
coordination.
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Mechanical and Aerospace Engineering,
University of California, San Diego
9500 Gilman Dr,
La Jolla, California, 92093-0411
Ph: 1-858-822-7930
Fax: 1-858-822-3107
cortes at ucsd.edu
Skype id:
jorgilliyo