We present a framework to enable a team of heterogeneous mobile robots to model and sense a multiscale system. Their approach proposes 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. The researchers’ 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. They illustrate the proposed framework by modeling and predicting the evolution of an artificial plasma cloud. They also test the approach using physical marine robots adaptively sampling a process in a water tank.

Figure: Heterogeneous robots collecting different sensing information work to create a cohesive model of a time varying environment. Aerial vehicles collect low-fidelity sensor measurements, such as overhead images, over a wide area, and marine vehicles collect high-fidelity sensor measurements, such as current speeds, over a small area. Sensor measurements are unified into one model for estimation and prediction of a time varying process

Capability: T3C4B – Heterogeneous Multi-Robot Systems for Modeling and Prediction of Multiscale Spatiotemporal Processes

Points of Contact: M. Ani Hsieh (PI) and Tahiya Salam

Paper: https://arxiv.org/pdf/2103.10383.pdf

Citation: T. Salam, M. A. Hsieh “Heterogeneous robot teams for modeling and prediction of multiscale environmental processes.” arXiv preprint arXiv:2103.10383, March 2021.