Adaptive Decentralized Control of Mobile Underwater Sensor Networks and Robots for Modeling Underwater Phenomena

Understanding the dynamics of bodies of water and their impact on the global
environment requires sensing information over the full volume of water. In this article,
we develop a gradient-based decentralized controller that dynamically adjusts the depth
of a network of underwater sensors to optimize sensing for computing maximally detailed
volumetric models. We prove that the controller converges to a local minimum and show
how the controller can be extended to work with hybrid robot and sensor network systems.
We implement the controller on an underwater sensor network with depth adjustment
capabilities. Through simulations and in-situ experiments, we verify the functionality and
performance of the system and algorithm.