A Data-Driven Exploratory Approach for Level Curve Estimation With Autonomous Underwater Agents

This contribution presents a method to estimate environmental boundaries with mobile agents. The agents sample a concentration field of interest at their respective positions and infer a level curve of the unknown field. The presented method is based on support vector machines (SVMs), whereby the concentration level of interest serves as the decision boundary. The field itself does not have to be estimated in order to obtain the level curve which makes the method computationally very appealing. A myopic strategy is developed to pick locations that yield most informative concentration measurements. Cooperative operations of multiple agents are demonstrated by dividing the domain in Voronoi tessellations. Numerical studies demonstrate the feasibility of the method on a real data set of the California coastal area. The exploration strategy is benchmarked against random walk which it clearly outperforms.