Gregory Murad Reis
Florida International University
Gregory Murad Reis is a Ph.D. candidate in Computer Science and a Science without Borders fellow working under Dr. Leonardo Bobadilla’s supervision at the MoRA Lab. His research interests span the fields of Underwater Robotics, Data Analysis, Mathematical Modeling, Artificial Intelligence, STEM Outreach and Equity in STEM Education. He has published papers on localization and navigation of underwater robots in GPS-denied environments, analysis of the spatio-temporal dynamics of the ocean, persistent monitoring of the ocean using drifters, among others. He has also worked with instrumentation, mathematical modeling and artificial intelligence applied to agricultural development (animal welfare and cooling systems) in Brazil for five years.
Gregory holds a Bachelor of Science in Computer Science and a Master of Science in Systems Engineering at Federal University of Lavras, Brazil. He also worked as a Professor in the Exact Sciences Department at Federal University of Lavras, Brazil, teaching undergraduate Math courses for Computer Science and Engineering majors. At FIU, Gregory worked as a research mentor for several undergraduate students in their summer research programs (such as Science without Borders, NSF-RET, NSF-REU); an academic mentor for FIU students with intellectual disabilities; a student assistant at SCIS and he has led the Robotics Workshop for high school teachers and students sponsored by The Ultimate Software Academy for Computer Science.
Aquatic robots, such as Autonomous Underwater Vehicles (AUVs), play a major role in the study of ocean processes that require long-term sampling efforts and commonly perform navigation via dead-reckoning using an accelerometer, a magnetometer, a compass, an IMU and a depth sensor for feedback. However, these instruments are subjected to large drift, leading to unbounded uncertainty in location. Moreover, the spatio-temporal dynamics of the ocean environment, coupled with limited communication capabilities, make navigation and localization difficult, especially in coastal regions where the majority of interesting phenomena occur. To add to this, the interesting features are themselves spatio-temporally dynamic, and effective sampling requires a good understanding of vehicle localization relative to the sampled feature.
Therefore, our work is motivated by the desire to enable intelligent data collection of complex dynamics and processes that occur in coastal ocean environments to further our understanding and prediction capabilities. The study originated from the need to localize and navigate aquatic robots in a GPS-denied environment and examine the role of the spatio-temporal dynamics of the ocean into the localization and navigation processes. The methods and techniques needed range from the data collection to the localization and navigation algorithms used on-board of the aquatic vehicles. The focus of this work is to develop algorithms for localization and navigation of AUVs in GPS-denied environments. We developed an Augmented terrain-based framework that incorporates physical science data, i.e., temperature, salinity, pH, etc., to enhance the topographic map that the vehicle uses to navigate. In this navigation scheme, the bathymetric data are combined with the physical science data to enrich the uniqueness of the underlying terrain map and increase the accuracy of underwater localization.