The Sea Exploration Problem: Data-driven Orienteering on a Continuous Surface

TL;DR

This paper introduces a novel sea exploration problem combining continuous surface sampling with orienteering, utilizing Gaussian processes to optimize resource estimation through strategic, data-driven expedition planning.

Contribution

It formulates a new problem integrating continuous surface sampling with orienteering, leveraging Gaussian process regression for correlated resource estimation.

Findings

Proposes a new data-driven sea exploration problem.

Integrates Gaussian processes for resource estimation.

Highlights differences from standard orienteering problem.

Abstract

This paper describes a problem arising in sea exploration, where the aim is to schedule the expedition of a ship for collecting information about the resources on the seafloor. The aim is to collect data by probing on a set of carefully chosen locations, so that the information available is optimally enriched. This problem has similarities with the orienteering problem, where the aim is to plan a time-limited trip for visiting a set of vertices, collecting a prize at each of them, in such a way that the total value collected is maximum. In our problem, the score at each vertex is associated with an estimation of the level of the resource on the given surface, which is done by regression using Gaussian processes. Hence, there is a correlation among scores on the selected vertices; this is a first difference with respect to the standard orienteering problem. The second difference is the location of each vertex, which in our problem is a freely chosen point on a given surface.