Stephanie Kemna

Adaptive informative sampling with autonomous underwater vehicles: Acoustic versus surface communications

Autonomous underwater vehicles (AUVs) are cost- and time-effective platforms for mapping and monitoring of aquatic environments. Previous works have shown the benefits of using informative adaptive sampling approaches for field estimation over running standard surveys. We are interested in extending these works into decentralized multi-robot approaches. Simulation experiments with two AUVs, comparing no data sharing with timed surfacing for data sharing, show that the system performs better when data is shared. We further explore the trade-off between using high-bandwidth surface Wi-Fi communications, at the cost of surfacing, and low-bandwidth underwater acoustic communications (acomms). Our simulation results show that for multi-vehicle decentralized adaptive sampling, we can increase modeling performance by having vehicles share their measurements. Furthermore, zero loss acomms can perform better than data sharing through timed surfacing events. However, when acomms throughput is reduced, modeling uncertainty increases.

Multi-robot coordination through dynamic Voronoi partitioning for informative adaptive sampling in communication-constrained environments

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.