Joint project GALILEOnautic 2+Copyright: © Michael Gluch
Networked, cooperative maneuvering in safety-critical areas
- 01.03.2023 to 31.08.2024
MotivationCopyright: © IRT
To increase the productivity of the supplier industry via maritime routes in dense maritime traffic while simultaneously enhancing ship safety, reliability, and efficiency, a modern automation strategy is needed. This strategy relies on the implementation of modern sensors, Galileo satellite signals, network technology, and digital infrastructure to automate maritime operations in inland waters and ports. The project aims to develop a state-of-the-art, interconnected transportation system with three autonomously operating watercraft in safety-critical areas. The primary goal is to enable cooperative maneuvering, enhance navigation safety, and make mobility more sustainable and flexible. The collaboration of autonomous systems and the secure integration of non-networked traffic participants are of paramount importance, aligning with the National Master Plan for Maritime Technologies of the German government.
Project Goals and Methods
The research project is dedicated to developing a cutting-edge interconnected transportation system for watercraft, particularly in safety-critical zones like ports, with a strong focus on autonomous and cooperative maneuvering. This system harnesses advanced technologies such as optical sensors for 360° panoramic visibility and AIS data fusion to ensure precise traffic situation awareness. Networked vessels, equipped with sensors like LiDAR, AIS, IMU, and GNSS receivers, individually establish an environmental understanding. Methods like AI and Deep Learning are employed to detect, localize, and classify static and dynamic objects within LiDAR sensor data, which are then fused with additional data from AIS, IMU, and GNSS and subsequently tracked using an extended Kalman filter. The environmental maps of each intelligent vessel are shared via an LTE interface within the network. A central unit receives a collectively generated traffic situation image and generates secure trajectories for all intelligent vessels through nonlinear optimization, which are then implemented on-site. The algorithms and automation functions are executed on the three test platforms: the research ship DENEB of the Federal Maritime and Hydrographic Agency, called BSH, as well as the two test ships from the University of Rostock, MESSIN and BeLa.
The Institute of Control Engineering is primarily responsible for traffic situation recognition within the project. To achieve this, the three test platforms are equipped with comprehensive modern sensor technology, including Solid-State LiDAR and high-performance computers specialized in AI. This research project aligns with the goals of the German government in the areas of innovation, industrial development, sustainability, and climate change mitigation by enabling a secure, efficient, and sustainable interconnected transportation system.
Innovations and Perspectives
This groundbreaking project focuses on the interconnected, cooperative handling of navigation tasks in highly automated maritime environments, making a crucial contribution to sustainable, flexible, and secure mobility. It is based on precise localization through the Galileo satellite navigation system and cross-sensor integrity checks. The inclusion of non-networked maritime traffic participants is achieved through modern sensor technology and AI to create a common map platform. The nearly unrestricted scalability allows for the integration of the developed algorithms into industrial products. The innovation lies in a cooperative navigation scenario for autonomous watercraft, supported by Galileo, and underscores Germany's leading role in the use of this satellite system, particularly in the integration of additional sensors for the maritime industry. This project contributes to the achievement of national maritime goals and the National Masterplan Maritime Technologies, positioning Germany as a leader in the application of Galileo in the maritime sector.