  We present the results of experiments performed using a small autonomous
underwater vehicle to determine the location of an isobath within a bounded
area. The primary contribution of this work is to implement and integrate
several recent developments real-time planning for environmental mapping, and
to demonstrate their utility in a challenging practical example. We model the
bathymetry within the operational area using a Gaussian process and propose a
reward function that represents the task of mapping a desired isobath. As is
common in applications where plans must be continually updated based on
real-time sensor measurements, we adopt a receding horizon framework where the
vehicle continually computes near-optimal paths. The sequence of paths does
not, in general, inherit the optimality properties of each individual path. Our
real-time planning implementation incorporates recent results that lead to
performance guarantees for receding-horizon planning.
