  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.
