Ocean processes are dynamic and complex events that occur on multiple different spatial and temporal scales. To obtain a synoptic view of such events, ocean scientists focus on the collection of long-term time series data sets. Measurements are continually provided by fixed sensors, e.g., moorings, or gathered from ships. Recently, an increase in the utilization of Autonomous Underwater Vehicles has enabled dynamic data acquisition. However, we are not utilizing the full capabilities of these vehicles. Here, we provide an extension to persistent monitoring of a designated region by providing the ability to adapt a mission to increase information gain in areas of greater scientific interest. We first develop a path planner to compute a single, repeatable path to be continually traversed by an underwater vehicle to generate a long time-series data set. The path is optimized for visiting regions of high interest, penalized for navigating through waters of large magnitude ocean currents, and is length constrained. Along this predefined path, we implement a velocity control algorithm to increase or decrease spatiotemporal sampling resolution to satisfy the sampling requirements necessary to resolve oceanic phenomena at multiple scales. The application of these tools is motivated by ongoing research to understand oceanic processes, and specifically algal bloom ecology, in southern California. The computed paths are implemented onto autonomous underwater gliders for data collection during sea trials. Experimental results from this data collection are presented to demonstrate the ability of the proposed method to perform persistent monitoring for resolving large-scale events, while simultaneously collecting high-resolution data for smaller-scale processes.
Ryan N. Smith received B.S. degrees in both Mathematics and Engineering Physics from Miami University (Oxford, OH) in 1998, the M.A. degree in Mathematics, and the PhD in Ocean & Resources Engineering from the University of Hawai`i at Manoa in 2002 and 2008, respectively. Ryan is a recent alumni of the Robotics Embedded Systems Laboratory at the University of Southern California, and is now a faculty member in the School of Electrical Engineering at the Queensland University of Technology. His current research interests include nonlinear dynamics, control and coordination of single and multi-robot systems, persistent and adaptive ocean sampling, autonomous underwater vehicles, geometric control on manifolds and ocean modelling.