Environment Reconstruction from Sparse Data for a Moored Profiler Monitoring an Aquaculture Installation

There is burgeoning global demand for marine seafood, and one proposed approach to meeting this is expanding offshore farming practices. When developing such offshore aquaculture farms, it is critical to maintain the health and safety of both the food stock and the environment. To do so, there must be consistent and thorough monitoring of the installations, with data collected over large spatial and temporal ranges, spanning up to hundreds of meters deep for weeks at a time. We focus on using a moored automatic mobile profiler (MAMP) to provide environmental data along the entire spatial range relevant to the farm. However, while MAMPs have much higher spatial range and resolution than fixed sensor arrays, an aquaculture site is dynamic. To capture the shifts in the site’s state, the profiler must take quick, sparse measurements and determine the overall environment from those samples. This paper’s goal is to propose an implementation of a probabilistic algorithm that enables reconstruction and prediction of the farm’s state from sparse samples for use in path planning using Gaussian Regression Processes. The proposed approach is verified against experimental ocean environment data, and shows >95% accuracy in environment reconstruction while requiring only 10% of the number of data samples as the ground truth data set. It is further implemented in conjunction with a Markov Decision Process based planner to demonstrate the algorithm’s use in path planning. This work indicates the high potential of the algorithm for use on dynamic environments like aquaculture farm sites.