The objective of the project ‘PREParE SHIPS’ is the development and demontration of a collaborative resilience navigation solution. It aims to develop and enhance existing software solutions by exploiting the distinguished features of the Galilieo signals as well as combining it with other nautical information on internal as well as external parameters and sensor technologies. The final navigation decision support tool implemented, consists of collaborative exchange by ship2ship communication of dynamically predicted future position based on resilient position from Galileo recievers. This will significantly increase safety and efficiency and will be the base of future autonomous operations.
Besides the use of vessel on board sensors, ‘PREParE SHIPS’ will also make use of data learning of earlier ship behaviour to exchange near -future positions with vessels in the vicinity and VTS centers (Vessel Traffic Services) to increase safety and improve decision making. In order to define the correct requirements for the PREParE SHIPS combined positioning solutions, a collaborative automated vessel application will be defined and developed. The vessel application will rely on the high availibility positioning solution and use it to couple its various navigational systems with ship2ship/ship2shore and aggregate information recieved from other connected vessels.
As there will be a transition period where a lot of vessels are neither connected nor automated, solutions having high impact during low penetration are in focus. The project will implement and demonstrate a fairway geo-fencing with high precision positioning taking into account various data sources (e.g wind and current) as well as a traffic monitoring and predicted positions so it can allow for safe decisions based on robust data. This means that PREParE SHIPS also will implement perception layer sensor fusion that uses information collected historically in similiar conditions based on machine learning-hybrid models.
The PREParE SHIPS consortium proposes to integrate a new precise positioning system based on the features of Galileo and EGNSS signals, within merchant ships, to enable ships to plan and execute a safe and automated ship passage function of other vessels and challenging fairways.
Increasing the safety, energy efficiency and security for vessels in today’s industry, which is challenging related to increased automation and exposure of safety critical systems.
Together with partners RISE will present and validate a positioning solution. This will be done through development of existing software using Galileo-signals and combining it with nautical information regarding internal and external parameters and sensor technology. The project is expected to develop a navigation decision support system within shipping which will contain the following:
- EGNSS resilience positioning:
The possibility of dynamically predict future positions of other vessels based on the positions reported from the Galileo receiver/transmitter.
- Real-time dynamic predictor:
Use data learning in order to obtain information of earlier ship behaviour to exchange near-future positions with vessels in the vicinity and VTS centers (Vessel Traffic Services) to increase safety and improve decision making.
- Ship-to-ship / ship-to-shore interaction
In order to define the correct requirements for PREParE SHIPS combined positioning solution, a collaborative automated vessel application will be defined and developed. The vessel application will rely on the high availability positioning solution and use it to couple its various navigational systems with ship2ship/ ship2shore and aggregate information received from other connected vessels by using the next generation AIS – VDES.
Implement and demonstrate a fairway geo-fencing with high precision positioning, utilizing various data sources (e.g. wind and current) as well as a traffic monitoring and predicted positions so it can allow for safe decisions based on robust data. This means that ‘PREParE SHIPS’ also will implement perception layer sensor fusion that uses information collected historically in similar conditions based on machine learning-hybrid models.
The project work is divided into a series of technical and business related workpackes:
WP 1 – Definition of Concept, Use Cases & System Requirements
WP 2 – High accuracy Positioning
WP 3 – Dynamic Real Time Prediction
WP 4 – Ship-to-ship and Ship-to-shore Communication
WP5 – Navigation decision support sub-system & HMI
WP6 – Test and Demonstration
WP 7 – Analysis, Commercialisation and Impact
WP 8 – Project Coordination