Abstract
This talk will describe ongoing research efforts in Dr. Girdhar's lab aimed at developing robotics and machine learning-based techniques to enable search, discovery, and mapping of hard to observe underwater natural phenomena. The focus will be on visual observations, which complicates the adaptive data collection process in many ways, some of which Dr. Girdhar will address in this talk. Furthermore, he will discuss an approach to modeling spatial distribution of high dimensional observations such as the distribution of phytoplankton taxa, while automatically discovering the community structure, and how such an approach could be used by future robots to better sample microscopic organisms in the ocean.
Presenter Bio
Yogesh Girdhar is a computer scientist and the PI of the WARP Lab (http://warp.whoi.edu) at Woods Hole Oceanographic Institution (WHOI), and an Associate Scientist (without Tenure) in the Applied Ocean Physics & Engineering department. He received his BS and MS from Rensselaer Polytechnic Institute in Troy, NY; and his Ph.D. from McGill University in Montreal, Canada. During his Ph.D. Girdhar developed an interest in ocean exploration using autonomous underwater vehicles, which motivated him to come to WHOI, initially as a postdoc, and then later continue as a scientist to start WARPLab. Girdhar’s research has since then focused on developing smarter autonomous exploration robots that can accelerate the scientific discovery process in extreme and challenging environments, such as the deep sea. Some notable recognition of his work includes the Best Paper Award in Service Robotics at ICRA 2020, a finalist for Best Paper Award at IROS 2018, and honorable mention for the 2014 CIPPRS Doctoral Dissertation Award
Publication Date
3-5-2021
Document Type
Presentation
Recommended Citation
Girdhar, Yogesh, "Enabling Vision Guided Robotic Ocean Exploration" (2021). Seminars. 334.
https://scholars.unh.edu/ccom_seminars/334