Tarah Aniya ME Spring 2021 OGS Acceptance Speech My name is Tarah Aniya and I…
Assistant Professor Zhuoyuan Song was awarded a grant of $394,750 from the National Science Foundation (NSF) National Robotics Initiative 2.0: Ubiquitous Collaborative Robots (NRI-2.0) program to support the project titled “Collaborative Navigation, Learning, and Collaboration in Fluids with Application to Ubiquitous Marine Co-Robots”. The original National Robotics Initiative (NRI) was initially unveiled by President Obama in 2011 to keep the United States at the cutting-edge of robotics technology. The NRI-2.0 program builds upon the original NRI program to support fundamental research in the United States that will accelerate the development and use of collaborative robots (co-robots).
As the lifeblood of Earth, the ocean shapes and regulates global weather patterns, maintaining the perfect balance of chemistry and temperature to allow all Earth’s life-forms to survive and thrive. Nonetheless, the current understanding of global ocean activities and ocean health is extremely inadequate due to the lack of sufficient observation data below the ocean surface. The gap between the large ocean volumes to explore and the number of existing sensors in subsurface regions remains astonishingly huge, leaving the majority of the oceans unexplored. Small-size autonomous underwater vehicles are becoming essential elements in persistent and pervasive ocean sensing and monitoring. Accurate localization is of utmost importance for these vehicles to perform intelligent sensing and control as well as for the users to properly interpret the vehicles’ measurements. However, underwater localization is notoriously challenging since the ocean is opaque to radio frequency signals, rendering the satellite-based positioning systems unavailable underwater.
The objective of Professor Song’s project is to develop and test algorithms that enable teams of marine robots to persistently and collaboratively navigate the under-explored ocean volumes by utilizing ocean flows as localization references. These algorithms will enable underwater robots to jointly map the flow fields of an unknown fluidic environment (e.g., oceans under ice shelves or extraterrestrial fluid bodies) and, at the same time, localize themselves with these flow maps. This project will fundamentally increase the footprint and autonomy of mobile robots in fluid environments, benefiting several pertinent research areas including oceanography, marine ecology, and meteorology.