Pilot Surveys for Adaptive Informative Sampling

Abstract

Adaptive sampling has been shown to be an effective method for modeling environmental fields, such as algae concentrations in the ocean. In adaptive sampling, a robot adapts its sampling trajectory based on data that it is collecting. This data is often aggregated into models, using techniques such as Gaussian Process (GP) regression. The (hyper-)parameters for these models need to be manually set or, ideally, estimated from data. For GP regression, hyperparameters are typically estimated using prior data. This paper addresses the case where initial hyperparameters need to be estimated, but no prior data is available. Without prior data or accurately pre-defined hyperparameters, adaptive sampling techniques may fail, because there is no good model to base path planning decisions on. One method of gathering data is to perform a pilot survey. This survey needs to select informative samples for initiating the model, but without having a model to determine where best to sample. In this work, we evaluate four pilot surveys, which use a softmax function on the distance between waypoints and previously sampled data for waypoint selection. Simulation results show that pilot surveys that maximize waypoint spread over randomization lead to more stable estimation of GP hyperparameters, and create accurate models more quickly