학술논문

Opportunistic (Re)planning for Long-Term Deep-Ocean Inspection: An Autonomous Underwater Architecture
Document Type
Periodical
Source
IEEE Robotics & Automation Magazine IEEE Robot. Automat. Mag. Robotics & Automation Magazine, IEEE. 31(1):72-83 Mar, 2024
Subject
Robotics and Control Systems
Aerospace
Components, Circuits, Devices and Systems
Computing and Processing
Engineering Profession
General Topics for Engineers
Signal Processing and Analysis
Transportation
Power, Energy and Industry Applications
Robots
Planning
Inspection
Task analysis
Real-time systems
Autonomous underwater vehicles
Oceans
Safety
Underwater navigation
Petroleum industry
Gas industry
Offshore installations
Sea floor
Language
ISSN
1070-9932
1558-223X
Abstract
Robots are increasingly used in subsea environments because of their positive impact on human safety and operational capabilities in the deep ocean. However, achieving full autonomy remains challenging because of the extreme conditions they encounter. In this context, we propose an autonomous underwater architecture for long-term deep-ocean inspection that robustly plans activities and efficiently deliberates with no human help. It combines the innovative Saipem’s Hydrone-R subsea vehicle with an advanced planning architecture, resulting in a robot that autonomously perceives its surroundings, plans a mission, and adapts in real time to contingencies and opportunities. After describing the robot hardware, we present the technological advancements achieved in building its software, along with several compelling use cases. We explore scenarios where the robot conducts long-term underwater missions operating under resource constraints while remaining responsive to opportunities, such as new inspection points. Our solution gained significant reliability and acceptance within the oil and gas community as evidenced by its current deployment on a real field in Norway.