Multi-Robot Strategies for Adaptive Informative Sampling with Autonomous Underwater Vehicles
Stephanie Kemna

Biologists and oceanographers are sampling lakes and oceans worldwide, to obtain data on natural phenomena therein. For example, measuring the abundance of algae to understand and explain potentially harmful algal blooms. We hypothesize that the use of robot teams could significantly improve cost- and time-efficiency of lake and ocean sampling, allowing persistent and efficient mapping of the water column at finer spatial and temporal resolution. Additionally, these systems may be able to intelligently gather data without needing significant amounts of prior information. We envision a scenario where one or two groups of biologists or oceanographers come together for monitoring a lake, bringing their autonomous vehicles with biological sensors. Our focus is on improving sampling efficiency and environmental modeling performance through the use of (decentralized) coordination approaches for multi-robot sampling systems. This poster briefly recaps my work on multi-robot strategies for adaptive informative sampling with autonomous underwater vehicles. In adaptive informative sampling, the robots adapt their trajectory online, in response to sampled data, while utilizing information-theoretic metrics to find informative sampling locations. We explore what benefits can be obtained from adding data sharing and coordination algorithms between vehicles. We focus on two main strategies: active coordination through dynamic estimation of Voronoi partitions, and implicit coordination through asynchronous surfacing with a surface-based data hub. The results show the benefits and potential of incorporating data sharing and coordination strategies into adaptive sampling routines for multi-robot systems.