TY - JOUR
T1 - Cooperative control of mobile sensor networks
T2 - Adaptive gradient climbing in a distributed environment
AU - Ögren, Petter
AU - Fiorelli, Edward
AU - Leonard, Naomi Ehrich
N1 - Funding Information:
Manuscript received July 21, 2003; revised April 6, 2004. Recommended by Associate Editor M. Reyhanoglu. The work of P. Ögren was supported by the Swedish Foundation for Strategic Research through its Center for Autonomous Systems at KTH. The work of E. Fiorelli and N. E. Leonard was supported in part by the Office of Naval Research under Grants N00014-02-1-0826 and N00014-02-1-0861, by the National Science Foundation under Grant CCR-9980058, and by the Air Force Office of Scientific Research under Grant F49620-01-1-0382.
PY - 2004/8
Y1 - 2004/8
N2 - We present a stable control strategy for groups of vehicles to move and reconfigure cooperatively in response to a sensed, distributed environment. Each vehicle in the group serves as a mobile sensor and the vehicle network as a mobile and reconfigurable sensor array. Our control strategy decouples, in part, the cooperative management of the network formation from the network maneuvers. The underlying coordination framework uses virtual bodies and artificial potentials. We focus on gradient climbing missions in which the mobile sensor network seeks out local maxima or minima in the environmental field. The network can adapt its configuration in response to the sensed environment in order to optimize its gradient climb.
AB - We present a stable control strategy for groups of vehicles to move and reconfigure cooperatively in response to a sensed, distributed environment. Each vehicle in the group serves as a mobile sensor and the vehicle network as a mobile and reconfigurable sensor array. Our control strategy decouples, in part, the cooperative management of the network formation from the network maneuvers. The underlying coordination framework uses virtual bodies and artificial potentials. We focus on gradient climbing missions in which the mobile sensor network seeks out local maxima or minima in the environmental field. The network can adapt its configuration in response to the sensed environment in order to optimize its gradient climb.
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U2 - 10.1109/TAC.2004.832203
DO - 10.1109/TAC.2004.832203
M3 - Article
AN - SCOPUS:4344586379
SN - 0018-9286
VL - 49
SP - 1292
EP - 1302
JO - IEEE Transactions on Automatic Control
JF - IEEE Transactions on Automatic Control
IS - 8
ER -