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Maritime LAseR for collisIon avoidance in high speed shippiNg and vessel traffic mAnagement

Periodic Reporting for period 1 - MARINA (Maritime LAseR for collisIon avoidance in high speed shippiNg and vessel traffic mAnagement)

Reporting period: 2021-01-01 to 2022-03-31

The international shipping industry is responsible for transporting around 90% of world trade (Safety and Shipping Review 2021). The release of a major new analysis, ‘Maritime Safety 2012-2021: a decade of progress’, from Lloyd’s List Intelligence and DNV that shows a significant decline in casualties, losses and detentions over the past decade, which they put it down to technological advancements, improved ship design, implementation of regulations and risk management systems. However, despite noteworthy improvement in the safety of shipping, navigational accidents remain a frequent and almost daily occurrence.

Thus, there is a clear market need to increase efficiency, security and safety of the global maritime transport industry, to reduce its environmental impact and to prepare for a future of autonomous shipping. MARINA was therefore conceptualized in response to an increasing demand in the maritime industry for solutions which achieve efficiencies, bolster safety and security measures and reduce impact on the environment, helping organizations make better strategic decisions while preparing for a future of autonomous shipping. By offering our cutting-edge solution which delivers important benefits to operational activities, the MARINA project promises to fill a gap in the maritime transport market by offering the system that detects, characterises, classifies and tracks various types of objects (e.g. floating shipping containers, ice floes, floating debris, mammals, plastic waste, navigational hazards/other vessels/ships etc.), allowing a ship, offshore platform and/or port operator to use this information in order to take the appropriate and correct decision, and make a quick decision to ensure safety and security in high-pressure navigational or environmentally stressed situations.
At the beginning of the MARINA project, in WP1, the Consortium held 3 workshops focusing on collision avoidance and autonomous sailing applications relevant for shipping safety. Well respected professionals such as Professor Egil Eide from Norwegian University of Science and Technology talked to us about the latest developments in autonomous shipping and Dr Bert van Bavel from Norwegian Institute of Water Research (NIVA) talked to us about marine pollution. Discussions were held during these workshops and feedback was collected from various stakeholders and end-users which resulted in an updated system requirements, design and performance specifications for the LADAR sensor suite which resulted in the production of the Deliverable D1.1 Report on final product specification. Following this, from Month 4 of the project, specific tasks began on hardware upgrades, software upgrades, GUI upgrades, integration of artificial intelligence and machine learning as well technical adaptation of the test vessel and planning for in-house testing. Once technology upgrades have been implemented, piloting and validation of incremental improvements of the technology will be carried out in the Project under WP3.

In WP2, the Consortium started developing a Digital Training Academy to provide access to the relevant information and training modules for the end-users, ship and port operators and fleet management so that they can acquire fundamental information about the LADAR system being developed in the MARINA project, how it functions, its use case scenarios and applications. As part of the Task 2.1 we have developed content of the digitally enabled e-learning training program, including materials and a live VR/AR headset demo, whereas in Task 2.2 the basic framework for the web-based training portal has been set up using the Moodle platform and development continues to host and deliver the digital training program content for training and demonstration activities.

In WP 3, the Project partners had several meetings to discuss and agree on the plan for preparations of a large-scale piloting and validation in real life settings.

Additionally, in order to support the prospects of successful commercial uptake of the LADAR Sensor Suite, following the updated system specifications, a Network Patent Analysis (NPA) was carried out to confirm Freedom-to-Operate (FTO) for our planned technology development in relation to our existing patents and competing technologies in the market. Furthermore, during Month 1 – Month 15 of the project, as part of WP4 and WP5, we have also ramped up our commercialisation and dissemination activities, which included launch of the MARINA project website to share project developments, frequent posts on company LinkedIn and Twitter pages to disseminate information about the LADAR system and participation in various events. Despite various restrictions caused by the pandemic, our strategic dissemination and exploitation plan help us increase our networks interest in the MARINA project and the LADAR Sensor Suite which now includes greater number of potential clients such as ship operators, port infrastructure, port security, shipyards and autonomous shipping projects. Moreover, we have also noticed significant interest from some of the biggest companies in the industry.
The LADAR Sensor Suite system that is being developed in the MARINA project will not only improve the navigational crew’s decision-making by providing them with real-time information in reference to potential hazards, consequently reducing the number of accidents, injuries, and fatalities that occur at sea, but it will also reduce marine pollution caused by accidents (e.g. oil spill, vessel debris, leakage from vessels /containers carrying polluting cargo etc.), avoid disruption of marine ecosystem and prevent negative impacts caused by marine pollution on coastal communities. This capability is facilitated by the unique sensor configuration of the system, using a combination of high fidelity multispectral and thermal imaging along with the use of multibeam laser imaging system, along with Artificial Intelligence processing and automatic learning methods.