Adaptive Sampling of Latent Phenomena using Heterogeneous Robot Teams (ASLaP-HR)

TL;DR

This paper introduces an adaptive planning method for heterogeneous robot teams to efficiently sample latent phenomena by leveraging learned models that estimate information gain from observable fields, considering sensor correlations.

Contribution

It presents a novel online adaptive sampling strategy that incorporates learned models and sensor correlations for latent phenomena using heterogeneous robots.

Findings

Learned correlation models improve sampling efficiency.

The approach effectively estimates information gain for latent phenomena.

Simulations on real sensor data validate the method's effectiveness.

Abstract

In this paper, we present an online adaptive planning strategy for a team of robots with heterogeneous sensors to sample from a latent spatial field using a learned model for decision making. Current robotic sampling methods seek to gather information about an observable spatial field. However, many applications, such as environmental monitoring and precision agriculture, involve phenomena that are not directly observable or are costly to measure, called latent phenomena. In our approach, we seek to reason about the latent phenomenon in real-time by effectively sampling the observable spatial fields using a team of robots with heterogeneous sensors, where each robot has a distinct sensor to measure a different observable field. The information gain is estimated using a learned model that maps from the observable spatial fields to the latent phenomenon. This model captures aleatoric uncertainty in the relationship to allow for information theoretic measures. Additionally, we explicitly consider the correlations among the observable spatial fields, capturing the relationship between sensor types whose observations are not independent. We show it is possible to learn these correlations, and investigate the impact of the learned correlation models on the performance of our sampling approach. Through our qualitative and quantitative results, we illustrate that empirically learned correlations improve the overall sampling efficiency of the team. We simulate our approach using a data set of sensor measurements collected on Lac Hertel, in Quebec, which we make publicly available.