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L2M – Steel Catenary Riser Tracking
This project aims to develop an AI-enhanced autonomous underwater vehicle (AUV) system designed to track Steel Catenary Risers (SCRs) in real time. Accurate tracking of SCRs is critical for maintaining the integrity of subsea infrastructure in the offshore oil and gas industry. The AUV will leverage advanced computer vision and machine learning techniques to continuously track the riser’s position and adjust its own movement to follow the structure precisely.
Faculty Supervisor:
Hodjat Shiri
Student:
Partner:
Springboard Atlantic Inc.
Discipline:
Engineering
Sector:
Oil and Gas; Artificial Intelligence; Ocean Tech
University:
Memorial University of Newfoundland
Program:
Business Strategy Internship
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