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.
In REF , a robotic boat supplements a static sensor network to reduce the field reconstruction error, where the boat's movement is guided by the measurements of the sensor network.
Adaptive Sampling for Estimating a Scalar Field using a Robotic Boat and a Sensor Network
Proceedings 2007 IEEE International Conference on Robotics and Automation
Proceedings 2007 IEEE International Conference on Robotics and Automation
Engineering Computer Science