Collaborative Robot Tracking of Geophysical Flows: How Local Measurements Discover Global Structures
Add to Google Calendar
Geophysical fluid dynamics (GFD) is the study of natural large-scale fluid flows, such as oceans, the atmosphere, and rivers. Recent years have seen the use of autonomous underwater and surface vehicles (AUVs and ASVs) to study various physical phenomena in geophysical fluid environments. These include mapping of ocean temperature and salinity profiles, understanding the distribution of plankton assemblages, and tracking harmful algal blooms. However, operating AUVs and ASVs in geophysical fluid environments poses significant challenges since GFD flows are naturally stochastic and aperiodic. Nevertheless, GFD flows exhibit dynamical coherent structures which are important for the estimation of the underlying geophysical fluid dynamics and thus, the prediction of the various physical, chemical, and biological processes in them.
In this talk, I will discuss strategies for distributed autonomous sensing and tracking of a class of coherent structures that coincide with minimum energy and time trajectories in the ocean and are important for quantifying transport phenomena in flows. I will present a collaborative control framework that enables teams of AUVs/ASVs to collectively harvest coherent structure information on flows using on-board sensing capabilities. Since these coherent structures delineate boundaries of regions with distinct flow dynamics and correspond to regions which is unstable, I will also present a framework that enables the control of the spatial distribution of sensing resources in time-varying and uncertain environments. I will conclude with a description of our preliminary efforts in building a large scale indoor facility capable of generating realistic ocean dynamics to enable experimental validation of the proposed distributed coordination strategies for AUVs/ASVs. The goal of this work is to synthesize ideas from nonlinear dynamical systems, transport theory, and robotics to improve the performance of collaborative unmanned systems in geophysical flow environments.
M. Ani Hsieh is an Assistant Professor in the Mechanical Engineering & Mechanics Department and the Director of the Robotics Program at Drexel University. She received a B.S. in Engineering and B.A. in Economics from Swarthmore College in 1999 and her PhD in Mechanical Engineering from the University of Pennsylvania in 2007. Her current work in the Scalable Autonomous System Laboratory at Drexel University focuses on bridging the gap between statistical physics, nonlinear dynamics and control, and distributed multi-agent robotic systems. She was a 2011 ONR Summer Faculty Fellow and received the 2012 Office of Naval Research (ONR) Young Investigator Award.