Multi-AUV sediment plume estimation using Bayesian optimization

Sediment plumes created by dredging or mining activities have an impact on the
ecosystem in a much larger area than the mining or dredging area itself. It is
therefore important and sometimes mandatory to monitor the developing plume
to quantify the impact on the ecosystem including its spatial-temporal evolution.
To this end, a Bayesian Optimization (BO)-based approach is proposed for plume
monitoring using autonomous underwater vehicles (AUVs), which are used as a
sensor network. Their paths are updated based on the BO, and additionally, a
split-path method and the traveling salesman problem are utilized to account for
the distances the AUVs have to travel and to increase the efficiency. To address
the time variance of the plume, a sliding-window approach is used in the BO and
the dynamics of the plume are modeled by a drift and decay rate of the
suspended particulate matter (SPM) concentration measurements. Simulation
results with SPM data from a simulation of a dredge experiment in the Pacific
Ocean show that the method is able to monitor the plume over space and time
with good overall estimation error.