Robotized device designed to autonomously, efficiently and safely carry out container uncrating operations on ships.

It consists of a modular frame that is attached to the main spreader of the Ship To Shore crane and as it passes through the row of containers it can detect and identify the different types of twistlocks by means of artificial vision cameras and give an instruction to an articulated robotic arm specially designed for this purpose, so that it makes a movement to grip and open the twistlocks without human intervention, so that the containers are released and ready to be unstretched.

Design, validation and demonstration in a port terminal of a pre-commercial prototype RTG crane with an electric generation system (HYDROGEN-HFCT), based on the ULPHE-PEM hydrogen fuel cell technology, developed by one of the companies that is part of the applicant group, advancing in the decarbonization of port terminals.

The objective of the project is to demonstrate the feasibility of the proposed technological solution of replacing the current power supply system of the RTG equipment by means of a Diesel-generator set installed on the RTG crane gantry inside a container with a hydrogen fuel cell power generation system installed in similar containers placed on the crane gantry. In addition, the project includes developing a power management system capable of supplying the same three-phase supply voltage and at the same frequency as the previous system, so that there are no problems with the current crane interface.

Autonomous system based on low-cost physical sensors in the diffuse particle emission zone, including meteorological conditions monitoring, and virtual sensors based on Machine Learning techniques (soft sensing) in the immission or impact zone.

This will allow real-time monitoring and control through a digital platform of diffuse emissions produced by the handling of bulk solids (PM10) and operational decision making in relation to the estimation of the impacts or immissions produced by the emissions of these particles in other areas of the environment, especially in urban areas, reducing the costs associated with a particle monitoring network of high spatial resolution and allowing the implementation of predictive techniques.

A system for actively wind-assisted propulsion of merchant ships through a non-folding suction/suction sail system, capable of producing very high lift coefficients with minimum power consumption and minimum possible moving parts, thus reducing fuel consumption and polluting emissions from ships. It is based on aeronautical technologies, such as suction and active boundary layer control, applied to the naval sector. The main features of the system are:

• Compact system of reduced size in relation to the thrusts offered, being able to be incorporated in ships with limited deck space and with little impact on visibility and loading capacity.
• 100 % autonomous, fully electric, vertical axis, steerable system, with no need for training or crew intervention for its operation, reducing complexity and maintenance.
• Lower weight than folding sail and Flettner rotor sail systems.
• Lower acquisition cost (50% less than Flettner rotors and 30% less than folding sails) and reduced OPEX, with payback periods of less than four years.

Evolution of an existing PaaS (Platform as a Service) solution to enable security management, both safety and security, from an integrated and automated view through Machine Learning and Artificial Intelligence techniques, allowing 1) to anticipate incidents intelligently (0 % incident trend) and 2) in the event of an incident, to respond and recover as soon as possible (100 % resilience trend).

The proposed use case will be that of a system that will control the security status of the information managed for the logistic-port environment, implementing and validating pilots in two port authorities, as well as its extension to port terminals for its final scaling. It proposes the evolution of the SECUREPORT software that defines assets, analyzes risks and proposes plans, currently used in Spanish ports, moving from an off-line to an on-line solution, providing:

• Real-time data collection (IoT Risk) for analysis.
• Process automation with Robot Process Automation.
• Contribution of AI to enable predictive governance.
• Securization of all sensor data and communication networks.

Development of a “Plug and play” software for intelligent identification of intermodal transport through the use of artificial intelligence, Machine and/or Deep Learning able to detect and read in a single shot a relevant structured alphanumeric text or the symbols of any image, with high accuracy and drastically reducing hardware needs and manual interventions, representing a total disruption with the current OCR systems.

It provides “as a service” and “on the Edge” OCR capabilities, making it possible to effectively track cargo assets anywhere with any ordinary CCTV camera and thus have virtually ubiquitous OCR tracking capability. The goal of the project is to achieve for different cases of reading and identification in unlimited conditions:

• Detect and identify within the same image:
• License plates for vehicles and trailers, including those using non-Latin characters (Chinese, Arabic, Russian)
• BIC codes of shipping containers according to international standard ISO 6346.
• Dangerous goods pictograms of the ADR Treaty
• Standard numbering of UIC locomotives and wagons.
• Work with still images, video files or streaming video.
• Identify moving vehicles and trains without stopping or slowing down.

Development of an integrated system for intelligent and continuous monitoring of environmental noise caused by the different operations in port areas, which identifies its causes and, through the application of advanced computer models, responds to the growing needs for effective evaluation and management of this variable in ports. The project includes partial objectives such as the development of new measurement systems, the creation of intelligent monitoring systems or advanced models for the evaluation of environmental noise, which will allow the promotion of other applications that will contribute to improve the profitability of the project and its scalability to other areas and sectors.

The project proposes a methodological development that combines the application of artificial intelligence and IoT technologies to be able to have more complete and continuous evaluations that solve the needs that have been detected in the evaluation and management of noise in ports, which have a great diversity of noise sources, in general not continuous and where the evaluation of average levels established by legislation, the identification of the causes and the control of noise sources do not depend on the Port Authority.

The project will make it possible to move from a punctual evaluation to a continuous noise assessment that really supports the levels and presence of the different noise sources, making it possible to respond to complaints or additional requirements of the regulations, incorporating the consideration of environmental variables.

Integral solution for telemonitoring of physical parameters and possible interrelation between people, machines and interpersonal distances, with the objective of providing capabilities of:

• Advanced monitoring and anticipation of potential risk situations.
• Immediate detection.
• Early warning and preventive action.

The aim is to minimize the consequences of accidents, for which it is necessary to prevent them and maximize the probability of anticipating them through 1) traceability of people, vehicles and hazardous assets, 2) predictive analysis tools that identify potential interrelationships, their probabilities and associated risks, and 3) strategies and mechanisms that help to improve prevention and worker protection.

The adjective integral implies the development of a series of devices and tools to cover the entire information chain, from the physical collection of data to their visualization, processing and correlation.

Software tool located in the cloud, evolutionary of the Miplanning product marketed since 2016, for the digitization of the strategic planning of the workforce of the stevedoring sector in the Port Employment Centers and the relationship with its customers, the port terminals, assigning workers according to their availability and the existing demand at any given time, with the aim of facilitating, making more efficient and reducing costs of the stevedoring and unstowing processes.

It will involve the complete digitization of personnel management in the stevedoring sector, automating the assignment of tasks and shifts, dealing with unexpected events and incorporating variables such as collective bargaining agreements, predictability as a factor for improving forecasting in the face of uncertain demand, optimization of talent management and certification of contractual relationships. The evolution will incorporate new digital technologies such as Artificial Intelligence, Big Data, Blockchain and Analytics.

Device for rapid, innocuous and non-invasive inspection of containers, land transport elements and vehicles for the detection of threats, trafficking of illegal goods and people hidden inside them through the application of muon tomography technology. This technology makes use of particles called muons that are generated naturally in the upper layers of the atmosphere and reach the earth’s surface at a rate of 10,000 per minute per square meter, with great power to penetrate matter. When these particles pass through an object, their energy and trajectory are modified according to the nature (density, composition and geometry) of that object.

Muon tomography aims to measure these trajectory deviations by placing muon detectors upstream and downstream of the object under study. The measured deviations can be used through reconstruction algorithms to obtain a density map of the object, including its interior.

The components of the device are:

• Detection hardware based on multithreaded cameras.
• Threat and anomaly detection software, developed through AI algorithms.

The project is aimed at developing a complete device capable of detecting in terms of resolution and inspection time any type of goods, regardless of their density, surpassing the capacity of some existing products on the market with this technology that only have the capacity to detect high density materials such as fissile or nuclear material.

Platform for detection and management of security threats, conventional but also APTs (Advanced Persistent Threats), as well as anomalous (suspicious) behavior in IoT ecosystems and infrastructures (OT or industrial networks) deployed in ports that reflect any security or operational problem in the same, offering a preventive cybersecurity solution, so that it is possible to react to certain threats in an early manner avoiding security problems associated with the activities and the port environment, with special attention within the Smart Port concept.

The project includes the implementation of industrial cybersecurity probes capable of detecting any event occurring in the different assets available within a smart port, which listen to all the traffic of the port infrastructure and are able to understand the conversations between all IoT devices to be connected with business information and port information systems that make sense of the assets and alarms detected, even having a Machine Learning engine to generate new alerts by crossing IoT information with business information. It includes the design, development and configuration of specific dashboards or detection of advanced persistent threats orchestrated by third parties.

Intelligent virtual assistant platform that allows, through natural language by voice, to automate tasks and interconnect different software tools used in the logistics-port environment such as TOS (Terminal Operating Systems) to incorporate:

• Machine-human interaction for voice command management of the systems connected to the platform.
• Process automation, allowing the definition of processes that include actions in the different systems and tools integrated in the platform.

The developments are mainly based on natural language processing (NLP) applied to speech recognition and the detection of intentions in conversations. The most relevant technological base is the use of Machine Learning techniques in the area of natural language processing.

Open source platform focused on the end user for the control of the complete cycle of intralogistics assets through 4.0 technologies and proprietary methodology. Through these technologies it seeks to manage assets to maximize their utilization and extend their useful life with a digital twin.

The structure of the platform will be based on the collection of data from three main sources: theoretical machine data from the manufacturers themselves, machine operation data with specific sensors and data from maintenance operations. These data will be processed in the cloud by means of statistical modeling techniques that will allow visualizing the state of the machines through a digital twin model, allowing advanced management of the maintenance of the asset fleet, enabling predictive maintenance.

A tool for monitoring the vulnerability of port infrastructure from space to support decision-making in port management and operation in the areas of infrastructure reliability (risk management), conservation and maintenance, design and execution of maritime and land works, capacity analysis, investment justification and indirect monitoring of concessions and activities in concessioned areas.

The tool mainly integrates three key technologies such as Differential Interferometry with Synthetic Aperture Radar (DInSAR) and its integration with Artificial Intelligence and Data Management Platforms, oriented to its direct consolidation in port IT architectures or its publication in existing visualization and added value platforms known by the port community. Other technologies such as Cloud Computing or Web development will also be used to enable process automation and facilitate such integration.

The aforementioned satellite technology makes it possible to obtain millimeter-accurate time series of historical and current ground and infrastructure deformation data without the need for ground-based instrumentation. To improve the process of analysis and interpretation of the time series of ground and infrastructure deformation generated with DInSAR, provided by the Sentinel 1A and 1B satellites of ESA’s Copernicus program, AI algorithms will be developed in a cloud solution, which will allow the early detection of anomalous behavior in infrastructures.

Platform for monitoring emissions in port areas in real time, using SIMAS technology (High Sensitivity Multispectral Imaging System) and the integration of other data sources (meteorological, ships, scales, …), which will allow measuring ship emissions in real time in order to provide maritime and port authorities with the appropriate information for the development of efficient environmental policies.

It will make it possible to assess the composition of the smoke plume of the ships, allowing to identify the most polluting ones according to the emission factors. The specific solution is based on a measuring instrument consisting of 5 highly sensitive chambers, each optimized for the detection and quantification of one type of gas (CO2, HC, SOx and NOx), as well as RedLook Analytics software that allows monitoring of the gas plume emitted by ships, vehicles and machinery or other types of combustion.

The project will include a dashboard in service format, allowing any port facility and its agents (maritime captains, shipping companies, terminal operators, etc.) to monitor, monitor and catalog the emissions produced and even obtain a carbon footprint inventory of their activity.

Development and implementation of a system based on the use of RPAS (unmanned aerial vehicles) with the aim of improving security in seaports, complementing the functions of ground equipment and amplifying its monitoring and continuous surveillance capabilities in real time through artificial intelligence tools, computer vision and Deep Learning.

The system includes the following activities:

• Monitoring of the port area through an efficient deployment of the RPAS system, coordinating the different foot and aerial patrols and randomizing the trajectories of the aerial patrols within a series of pre-programmed maps or routes.
• Surveillance of the port perimeter and its accesses by land and sea.
• Intruder tracking to keep track of intruders so that they can be located by ground teams.
• Detection of suspicious behavior to detect possible theft or damage to goods or infrastructure.

Development of a key maritime radio communication system, providing two-way communication between ships, shore stations and satellites in the VHF marine frequency band and integrating existing AIS services.

The general objective of the project is to evaluate the feasibility of a complete solution for satellite communications and services under the VDES (VHF Data Exchange System) standard in order to maintain connectivity at long distances from the coast, given that current systems such as the classic AIS only allow communication at a local level and within a few miles of coastal base stations (on the order of 30 nautical miles).

The new AIS developments allow AIS messages to be received from satellite, but are not designed to be received from satellite. Therefore, it does not allow the provision of two-way communications. VDES will allow the provision of two-way digital VHF communications with ships, buoys and other equipment anywhere on the globe. The VDES standard is mature enough to develop technology that can reach the market, although apart from laboratory prototypes, there are no shore stations or VDES terminals on the market, either terrestrial or satellite, and there are no satellites for VDES yet.

The architecture of the complete system is composed of three functional segments:

• Space segment (satellite constellation).
• Terrestrial segment (elements and infrastructure to provide communication with satellites).
• User segment (users of the system and elements and applications that make use of it. This includes on-board VDESAT terminals).

Tool that provides a solution to the difficulty that a pleasure boat has when arriving to port and finding the mooring that corresponds to it, more accentuated in cases of low visibility, ignorance of the configuration of the port or confusing indications by those responsible for the port. It involves the introduction of new functionalities in the ShoreView product, already commercialized and with more than 35,000 users, which will allow to be guided by augmented reality within a port through the concordance between the 2D map and its real vision using augmented reality, recognizing dangers for navigation, obstacles, etc, optimizing and improving the safety of navigation of recreational boats within the maritime ports.

The developed tool will improve the augmented reality component, increasing accuracy and reducing response time. It will also incorporate new components: map integration, map component, routing or guiding within the port and port information. The tool incorporates the following technologies:

• Image recognition based on the cell phone camera.
• Augmented Reality using the cell phone camera to display guiding instructions over the image.
• Big Data and Machine Learning for information management.
• Integration of vector maps.