Raza, M.; Prokopova, H.; Huseynzade, S.; Azimi, S.; Lafond, S. Towards Integrated Digital-Twins: An Application Framework for Autonomous Maritime Surface Vessel Development. J. Mar. Sci. Eng.2022, 10, 1469.
Raza, M.; Prokopova, H.; Huseynzade, S.; Azimi, S.; Lafond, S. Towards Integrated Digital-Twins: An Application Framework for Autonomous Maritime Surface Vessel Development. J. Mar. Sci. Eng. 2022, 10, 1469.
Raza, M.; Prokopova, H.; Huseynzade, S.; Azimi, S.; Lafond, S. Towards Integrated Digital-Twins: An Application Framework for Autonomous Maritime Surface Vessel Development. J. Mar. Sci. Eng.2022, 10, 1469.
Raza, M.; Prokopova, H.; Huseynzade, S.; Azimi, S.; Lafond, S. Towards Integrated Digital-Twins: An Application Framework for Autonomous Maritime Surface Vessel Development. J. Mar. Sci. Eng. 2022, 10, 1469.
Abstract
The use of digital twins for the development of Autonomous Maritime Surface Vessels (AMSVs) has enormous potential to resolve the increasing need for water-based navigation and safety at the seas. Aiming at the problem of lack of broad and integrated digital twin implementations with live data along with the absence of a digital twin-driven framework for AMSV design and development, an application framework for the development of a fully autonomous vessel using an integrated digital twin in a 3D simulation environment has been presented. Our framework has four layers to ensure that the simulation and the real-world boat as well as the environment are as close as possible. Åboat, an experimental research platform for maritime automation and autonomous surface ship applications, equipped with two trolling electric motors, cameras, LiDARs, IMU and GPS has been used as the case study to provide a proof of concept. Åboat and its sensors, alongwith the environment have been replicated in a 3D simulation environment. Using the proposed application framework, we develop obstacle detection and path planning systems based on machine learning which leverage live data from a 3D simulation environment to mirror the complex dynamics of the real world.
Keywords
maritime autonomy; autonomous ship; safety; digital twin; deep reinforcement learning; collision avoidance; situational awareness
Subject
Engineering, Marine Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
