 |
|
|
UMD doctoral student Kaustabh Joshi guides an underwater robot using frameworks developed by members of a research team working with Professor Nikhil Chopra. |
|
A paper by a team from the University of Maryland (UMD) Department of Mechanical Engineering’s Semiautonomous Systems laboratory, "3D Water Quality Mapping using Invariant Extended Kalman Filtering for Underwater Robot Localization," was awarded the Best Control Framework for Autonomous Navigation and Control on Oct. 14, 2024 at the Autonomous Robotic Systems in Aquaculture: Research Challenges and Industry Needs workshop, held at IROS 2024 in Abu Dhabi, United Arab Emirates.
The team, comprising Kaustubh Joshi, Tianchen Liu, Xiaomin Lin (College of Computer, Mathematical, and Natural Sciences), Alan Williams (University of Maryland Center for Environmental Science), Matthew Gray (University of Maryland Center for Environmental Science), and Nikhil Chopra (University of Maryland) received a prize award of $300 for their paper.
This project is part of the U.S. Department of Agriculture (USDA)-funded Smart, Sustainable Shellfish Aquaculture Management (S3AM) initiative, focusing on using autonomous robots to enhance oyster aquaculture. By developing advanced underwater robot localization techniques, the project enables real-time 3D mapping of critical water quality parameters in farming environments.
Their research uses control theory, specifically state estimation, for underwater robotics to design autonomous systems that can accurately localize themselves in challenging aquatic environments. Using an Invariant Extended Kalman Filter (InEKF), their robot can navigate underwater, even in conditions with low visibility and environmental noise, while collecting vital data such as temperature, salinity, and oxygen levels. This helps optimize aquaculture practices, save resources, and reduce environmental impact.
This research directly supports USDA's goals for sustainable food production by delivering real-time data for innovative and efficient aquaculture. The localization system reduces resource usage and labor in oyster farming while promoting environmentally friendly practices. The results are shared with partner institutions, minority-serving universities, and oyster farmers across the U.S., furthering the S3AM project's economic and environmental sustainability mission.
For more details about the workshop, visit this link. Access the paper here.
November 25, 2024
|
