Hierarchical Online Learning for Adaptive Sampling of Discrete Species Distributions with an AUV

Abstract

Autonomous robots are increasingly being used in the field of scientific exploration and data acquisition. In particular, the use of robotic systems for mapping and sampling of species is becoming widespread in both aerial and underwater domains, however the problem of choosing where to sample is challenging when the phenomena of interest are discrete and sparsely distributed in space or time, such as when mapping a particular benthic species. In this paper we present a hierarchical online learning framework for reasoning about species distribution in realtime, in order to inform sampling decisions. Drawing inspiration from the Species Distribution Modelling community, a hierarchical probabilistic model is developed using the Integrated Nested Laplace Approximation framework, that enables online inference about expected target hotspots using predicted substrate distributions. Model parameters are learned online to build a prediction over the discrete targets, and the model is integrated into an anytime online planner to enable adaptive path planning. The hierarchical learning approach is demonstrated on simulated synthetic environments and shown to consistently outperform baseline methods such as Gaussian Process regression and boustrophedon coverage approaches, when robot resources are constrained.