DMCA
Adaptive Sampling for Estimating a Scalar Field using a Robotic Boat and a Sensor Network
| Citations: | 22 - 3 self |
BibTeX
@MISC{Zhang_adaptivesampling,
author = {Bin Zhang and et al.},
title = {Adaptive Sampling for Estimating a Scalar Field using a Robotic Boat and a Sensor Network},
year = {}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
This paper introduces an adaptive sampling algorithm for a mobile sensor network to estimate a scalar field. The sensor network consists of static nodes and one mobile robot. The static nodes are able to take sensor readings continuously in place, while the mobile robot is able to move and sample at multiple locations. The measurements from the robot and the static nodes are used to reconstruct an underlying scalar field. The algorithm presented in this paper accepts the measurements made by the static nodes as inputs and computes a path for the mobile robot which minimizes the integrated mean square error of the reconstructed field subject to the constraint that the robot has limited energy. We assume that the field does not change when robot is taking samples. In addition to simulations, we have validated the algorithm on a robotic boat and a system of static buoys operating in a lake over several km of traversed distance while reconstructing the temperature field of the lake surface.
Keyphrases
static node scalar field robotic boat sensor network mobile robot static buoy adaptive sampling algorithm integrated mean square error several km temperature field multiple location lake surface traversed distance underlying scalar field sensor reading reconstructed field subject mobile sensor network
