Heterogeneous robot teams for modeling and prediction of multiscale environmental processes

TL;DR

This paper introduces a framework for heterogeneous robot teams to model and predict multiscale environmental processes by fusing high- and low-fidelity measurements, enabling adaptive sensing and online model updates.

Contribution

It presents a novel coupled strategy for integrating diverse measurements, optimizing sensing locations, and adapting models in real-time for complex environmental monitoring.

Findings

Successful modeling of an artificial plasma cloud

Effective adaptive sampling with marine robots

Real-time model updates for dynamic environments

Abstract

This paper presents a framework to enable a team of heterogeneous mobile robots to model and sense a multiscale system. We propose a coupled strategy, where robots of one type collect high-fidelity measurements at a slow time scale and robots of another type collect low-fidelity measurements at a fast time scale, for the purpose of fusing measurements together. The multiscale measurements are fused to create a model of a complex, nonlinear spatiotemporal process. The model helps determine optimal sensing locations and predict the evolution of the process. Key contributions are: i) consolidation of multiple types of data into one cohesive model, ii) fast determination of optimal sensing locations for mobile robots, and iii) adaptation of models online for various monitoring scenarios. We illustrate the proposed framework by modeling and predicting the evolution of an artificial plasma cloud. We test our approach using physical marine robots adaptively sampling a process in a water tank.