Path planning for data assimilation in mobile environmental monitoring systems
Name
Hover-2009-Path planning for data assimilation in mobile environmental monitoring systems.pdf
Size
637.32 KB
Format
Adobe PDF
Checksum (MD5)
540b147076d7c484da9d0ba12e075ff9
Author(s)
Hover, Franz S.
Date Issued
December 2009
Journal
IEEE/RSJ International Conference on Intelligent Robots and Systems, 2009, IROS 2009
Publisher
Institute of Electrical and Electronics Engineers
Citation
Hover, F.S. “Path planning for data assimilation in mobile environmental monitoring systems.” Intelligent Robots and Systems, 2009. IROS 2009. IEEE/RSJ International Conference on. 2009. 213-218. ©2009 Institute of Electrical and Electronics Engineers.
Version
Final published version
Abstract
By combining a low-order model of forecast errors, the extended Kalman filter, and classical continuous optimization, we develop an integrated methodology for planning mobile sensor paths to sample continuous fields. Agent trajectories are developed that specifically take into account the fact that data collected will be used for near real-time assimilation with large predictive models. This aspect of the problem has significant implications because the trajectories generated are very different from those which do not take the assimilation step into account, and their performance in controlling error is notably better.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/IROS.2009.5354367
