Autonomous underwater vehicles (AUVs) are cost- and time-efficient systems for environmental sampling. Informative adaptive sampling has been shown to be an effective method of sampling a lake or ocean for environmental modeling. In this paper, we focus on multi-robot coordination for informative adaptive sampling. We use a dynamic Voronoi partitioning approach whereby the vehicles, in a decentralized fashion, repeatedly calculate weighted Voronoi partitions for the space. Each vehicle then runs informative adaptive sampling within their partition. The vehicles can request surfacing events to share data between vehicles. Simulation results show that the addition of the coordination with dynamic Voronoi partitioning results in obtaining higher quality models faster. Thus we created a decentralized, multi-robot coordination approach for informative, adaptive sampling of unknown environments.

A study of how different demographic groups find the experience of flexible online learning. A first year course in Internet & Web Design is offered to a diverse range of students in traditional and online formats. We hope to draw conclusions about which groups find online learning works for them.