  This paper reports an investigation into the problem of rapid identification
of a channel that crosses a body of water using one or more Unmanned Surface
Vehicles (USV). A new algorithm called Proposal Based Adaptive Channel Search
(PBACS) is presented as a potential solution that improves upon current
methods. The empirical performance of PBACS is compared to lawnmower surveying
and to Markov decision process (MDP) planning with two state-of-the-art reward
functions: Upper Confidence Bound (UCB) and Maximum Value Information (MVI).
The performance of each method is evaluated through comparison of the time it
takes to identify a continuous channel through an area, using one, two, three,
or four USVs. The performance of each method is compared across ten simulated
bathymetry scenarios and one field area, each with different channel layouts.
The results from simulations and field trials indicate that on average
multi-vehicle PBACS outperforms lawnmower, UCB, and MVI based methods,
especially when at least three vehicles are used.
