TY - JOUR
T1 - Collective motion, sensor networks, and ocean sampling
AU - Leonard, Naomi Ehrich
AU - Paley, Derek A.
AU - Lekien, Francois
AU - Sepulchre, Rodolphe
AU - Fratantoni, David M.
AU - Davis, Russ E.
N1 - Funding Information:
Manuscript received July 20, 2005; revised September 2, 2006. This work was supported in part by the Office of Naval Research under Grant N0014-02-1-0826, Grant N00014-02-1-0861, and Grant N00014-04-1-0534 and in part by the Belgian Program on Inter-university Poles of Attraction, initiated by the Belgian State, Prime Minister’s Office for Science, Technology and Culture. The work of D. A. Paley was supported by a National Science Foundation Graduate Research Fellowship, the Princeton Gordon Wu Graduate Fellowship, and the Pew Charitable Trust Grant 2000-002558. N. E. Leonard, D. A. Paley, and F. Lekien are with the Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, NJ 08544 USA (e-mail: [email protected]; [email protected]; [email protected]). R. Sepulchre is with the Department of Electrical Engineering and Computer Science, Université de Liège, Institut Montefiore B28, B-4000 Liège, Belgium (e-mail: [email protected]). D. M. Fratantoni is with the Physical Oceanography Department, Woods Hole Oceanographic Institution, Woods Hole, MA 02543 USA (e-mail: [email protected]). R. E. Davis is with the Physical Oceanography Research Division, Scripps Institution of Oceanography, University of California at San Diego, La Jolla, CA 92093-0230 USA (e-mail: [email protected]).
PY - 2007/1
Y1 - 2007/1
N2 - This paper addresses the design of mobile sensor networks for optimal data collection. The development is strongly motivated by the application to adaptive ocean sampling for an autonomous ocean observing and prediction system. A performance metric, used to derive optimal paths for the network of mobile sensors, defines the optimal data set as one which minimizes error in a model estimate of the sampled field. Feedback control laws are presented that stably coordinate sensors on structured tracks that have been optimized over a minimal set of parameters. Optimal, closed-loop solutions are computed in a number of low-dimensional cases to illustrate the methodology. Robustness of the performance to the influence of a steady flow field on relatively slow-moving mobile sensors is also explored
AB - This paper addresses the design of mobile sensor networks for optimal data collection. The development is strongly motivated by the application to adaptive ocean sampling for an autonomous ocean observing and prediction system. A performance metric, used to derive optimal paths for the network of mobile sensors, defines the optimal data set as one which minimizes error in a model estimate of the sampled field. Feedback control laws are presented that stably coordinate sensors on structured tracks that have been optimized over a minimal set of parameters. Optimal, closed-loop solutions are computed in a number of low-dimensional cases to illustrate the methodology. Robustness of the performance to the influence of a steady flow field on relatively slow-moving mobile sensors is also explored
KW - Adaptive sampling
KW - Autonomous underwater vehicles
KW - Cooperative control
KW - Coordinated dynamics
KW - Mobile sensor networks
KW - Ocean sampling
KW - Underwater gliders
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U2 - 10.1109/JPROC.2006.887295
DO - 10.1109/JPROC.2006.887295
M3 - Article
AN - SCOPUS:33947394634
SN - 0018-9219
VL - 95
SP - 48
EP - 74
JO - Proceedings of the IEEE
JF - Proceedings of the IEEE
IS - 1
M1 - 4118466
ER -