Turku Harbour and the archipelago are a unique environment for testing maritime autonomous systems and applications.

By converting a sea vessel into a sensing platform, the ARPA project supports the research and studies that relate to remotely operated, automated and autonomous machinery in a maritime environment. The sea vessel sensing platform enables the evaluation of sensor performance, edge computing and wireless connectivity as well as human-machine interaction studies.

The sensing platform has an important role in fulfilling the safety of autonomous systems, since sensors and their related data communication channels create a cyberattack surface for adversary actors. The platform enables safe and thorough cybersecurity risk assessments and testing methodologies in a real environment. 

Project Outputs:  

• A new platform for on-sea autonomous operation sensor tests with a data acquisition and wireless data communication infrastructure
• Flexible data connection to the ARPA data platform
• A cybersecurity testing methodology for remotely operated, automated and autonomous systems 

The ARPA project develops a collaborative smart manufacturing unit for remote, automated or autonomous operation. The unit offers new research possibilities for industrial-level data handling, controlling and safety related issues in a factory environment. 

Like its maritime sibling, the factory environment unit enables the evaluation of sensor performance, edge computing and wireless connectivity in addition to human-machine interaction studies. The platform can be utilized by universities and companies to enhance the digitalization of manufacturing in the industry 4.0 setting.  

Project outputs: 

• A smart manufacturing unit with human-machine interface operation and unmanned logistic robots
• Flexible data connection to the ARPA data platform
• A cybersecurity testing methodology for remotely operated, automated and autonomous systems

A digital twin is a living model that communicates information in real time with its physical replica. They are an essential part of operating remote, automated and autonomous vehicles. With the help of a digital twin, engineers and operators can analyse and predict performance of vehicles over their whole life cycle from an early-stage digital prototype to the dismantling of the physical twin.  

The ARPA project defines the most relevant areas of digital twins required for product and process testing in the maritime sector. Test models and a technical roadmap will help in developing maritime digital twins by balancing technical and scientific advantages with the needs of the industry.  

To maximize the effectiveness and productivity of autonomous vehicles, ARPA focuses on data visualization and advanced user interfaces utilizing latest interactive technologies like augmented and virtual reality. Carefully designed user interfaces and experiences will add to the stability of autonomous vehicles and minimize human errors. 

Project outputs: 

• A maritime digital twin roadmap to support and facilitate both the research and business aspects of the topic.
• A simulator environment for the creation and testing of maritime digital twins with testing tools and methods for product development
• User input data to the backend system to be used in Human-Machine-Interaction
• Tools and services for data visualization
• Solutions for augmented and virtual reality user interfaces for remote operations

To facilitate intelligent decision-making and the efficient management of remotely operated, automated and autonomous systems, the ARPA project develops a data platform combining flexible cloud and edge-computing environments for data acquisition and storage as well as AI-powered capabilities.  

The data platform is easy-to-use, collaborative, adaptive, scalable and virtualized. A graphical interface with dynamic and interactive data visualizations enables applied RDI work independent from the physical location of the researcher or the company.  

The development of the data platform involves determining different options for opening and anonymizing data as well as supporting international data standardization by extensive process documentation. A solid legal framework will support fluent cooperation with external actors within the data platform. 

Project outputs: 

• Realistic multi-facet measurements for experimentation and for evaluating AI algorithms and data fusion technique
• A highly customizable and flexible cloud infrastructure to interconnect, configure, control and monitor all testbed nodes
• Efficient, flexible and powerful software architecture to enable high-level and homogeneous access to each element of the testbed (nodes, sensors, servers and services)
• An AI-powered testbed including intuitive user interface and visualization