Understanding Autonomous Underwater Vehicles

'Uncrewed underwater vehicles (UUVs) are underwater robots that operate without humans inside. Early use cases for the vehicles have included jobs like deep-sea exploration and the disabling of underwater mines. However, UUVs suffer from poor communication and navigation control because of water’s distorting effect. So researchers have begun to develop machine learning techniques that can help UUVs navigate better autonomously. Perhaps the biggest challenge the researchers are grappling with is the absence of GPS signals, which can’t penetrate beneath the water’s surface. Other types of navigational techniques that rely on cameras are also ineffective, because underwater cameras suffer from low visibility... ...In the study, which was published last month in the journal IEEE Access, researchers from Australia and France used a type of machine learning called deep reinforcement learning to teach UUVs to navigate more accurately under difficult conditions. In reinforcement learning, UUV models start by performing random actions, then observe the results of those actions and compare them to the goal—in this case, navigating as closely as possible to the target destination. Actions that lead to positive results are reinforced, while actions that lead to poor results are avoided.' https://lnkd.in/e8rEHH7U

“In December, Norwegian energy giant Equinor won a 2-gigawatt (GW) lease in Morro Bay, California, in the first-ever offshore wind lease sale on the US West Coast. It was also the first US sale to support commercial-scale floating offshore wind development. The Morro Bay project has the potential to generate enough energy to power around 750,000 US households. But first steps first: Equinor just signed a contract with marine robotics company Ocean Infinity to conduct a site investigation survey using multiple AUVs (autonomous underwater vehicles) at the same time at Equinor’s floating offshore wind lease area. AUVs are underwater robots that operate independently of humans, and they can go to depths that human divers can’t. They’re modular so they can be bespoke and feature such things as sonar, depth sensors, and cameras. They’re either programmed or controlled by remote operators that tell the AUV where, when, and what they should sample. The data that AUVs collect is retrieved by the operators when they resurface. The data that Ocean Infinity’s AUVs collect at Morro Bay will help Equinor develop and refine its floating offshore wind farm design and will also inform the site assessment and construction and operations plans. Shawntel Johnson, director of business development at Ocean Infinity, said, “AUVs in scale are the perfect tool for [the US West Coast], providing not only great data quality advantages over towed arrays, in water depths spanning from 974 to 1317 meters (about 4,507 feet), but also huge efficiency over wide areas.” https://lnkd.in/gafabpTd

🤖 AI Navigation for Underwater Robots! 🚤 Researchers developed a biologically-inspired AI system to improve control of unmanned underwater vehicles (UUVs) in rough seas. It uses two memory buffers - focusing on recent experiences and positive rewards. This lets UUVs quickly adapt to varying conditions. In simulations, the new approach stabilized UUV maneuvering twice as fast as standard methods! AI continues to push autonomy further, enabling robots to tackle unpredictable real-world environments! 🌊 Stay tuned! More ocean exploration innovations ahead! #AI #robotics #oceans authors and affiliations Thomas Chaffre, PhD Flinders University, Adelaide, SA, Australia ARC Training Centre For Biofilm Research and Innovation, Paulo E. Santos College of Science and Engineering, Flinders University CNRS International - NTU - Thales Research Alliance Gilles LE CHENADEC ENSTA Bretagne Researcher at the Naval Group Research Center, Ollioules, France Karl Sammut College of Science and Engineering, Flinders University Crossing Centre for Defense Engineering Research and Training, Flinders University Theme Leader for the Maritime Autonomy Group Clément BENOIT College of Science and Engineering, Flinders University Department, ENSTA Bretagne Published in IEEE Access, DOI: 10.1109/ACCESS.2023.3329136 https://lnkd.in/g3_fjVE2 https://lnkd.in/gr662MdX