Projects

OPUS project envisages developing the necessary tools and methodologies to address these challenges and pave the way for the widespread introduction of multi-UAV technologies. OPUS tackles the challenge of persistent multi-UAV flight from the perspective of innovative algorithmic optimization in UAV swarms. Cooperative utilization of multiple UAVs, smart rotation, real-time data fusion and optimal placement of docking stations can greatly enhance continuous operation and extended mission durations. Advanced performance alone, however, cannot permeate the industry. For robotic platforms such as this, familiarity, simplicity and ease of use are required as well, all of which can be achieved through autonomy. Addressing human dependency, the project will introduce an automatic scheduler for UAV rotations and a unified change detection map, significantly reducing the cognitive load on human operators and enhancing scalability. For robust and fault-tolerant missions, OPUS will combine pre-programmed and fully autonomous approaches, delivering dynamic re-routing and task reallocation capabilities that adapt in real-time to unforeseen events. The project's outcomes will significantly benefit applications such as emergency response, environmental monitoring, and public safety.

The CameSense project aims to develop an integrated monitoring and management system for Camelina sativa cultivation, with the primary objective of early weed detection and mapping through artificial intelligence algorithms. It leverages data from UAVs, satellites, and field sensors, and—through augmented and virtual reality tools—provides farmers and agronomists with timely information and decision-support capabilities to optimize production.

Europe's transport system is under pressure from congestion, ageing infrastructure and the need to cut emissions. Digitalisation offers a way forward. With this in mind, the EU-funded SMARTIN project is creating a platform to manage transport infrastructure intelligently. By combining real-time data, AI-driven analytics and predictive tools, SMARTIN helps detect incidents early and optimise maintenance and operation to streamline urban mobility. Its solutions will be tested in four European cities, showcasing how digital tools can enhance accessibility, resilience and multimodality. With SMARTIN, Europe moves closer to a transport system that is safer, smarter and more sustainable for all.

Urban water pollution is a threat to ecosystems and public health. However, traditional monitoring systems often overlook pollution sources and pathways. The EU-funded AQUAMON project will introduce a smarter, more holistic approach. Specifically, it uses advanced sensors and unmanned vehicles across water, land, and air to monitor water quality in real time across eight pilot sites in Europe. The goal is to secure safe drinking water, improve wastewater reuse, protect urban bathing waters, and restore aquatic ecosystems. With integrated data and innovative control systems, AQUAMON supports a cleaner, more resilient water future for Europe.

Indirect fire support observation is the critical process of acquiring, relaying, and utilizing information about enemy targets and the effectiveness of indirect fire support on those targets. VICTORIOUS proposes a Next Generation observation system, dedicated for artillery/indirect fire support. The ultimate goal of VICTORIOUS is to allow defence stakeholders to locate and be informed in a timely manner of the exact location and nature of events occurring in the battlefield.

In this digital age 1, cultural institutions find themselves confronted with unprecedented challenges regarding sharing and digitising CH content, collaborating and connecting with one another. The creation of the European Collaborative Cloud for Cultural Heritage (ECCCH) offers a significant chance to tackle existing challenges due to national, local, legal, and organisational barriers that prevent CH institutions from widely using collaboration platforms and tools. As a part of the ECCCH TEXTaiLES will employ key emerging technologies like Artificial Intelligence (AI), 3D digitization, Internet of Things (IoT), Cloud/Edge Computing, Robotics, Digital Twins (DTs) to capture and visualise the visible and non visible characteristics of textile archeological objects.

Road safety in Europe remains a critical concern, especially in urban areas where interactions between large vehicles and vulnerable road users (VRUs) pose challenges. The introduction of CCA vehicles (connected, cooperative and automated) adds complexity, raising compatibility issues with existing vehicles. Peripheral roads also struggle with maintenance, repair and underinvestment. iDriving is an advanced Vehicle-to-Infrastructure (V2I) prototype that improves road safety on urban and rural roads. It proposes a safety criteria catalogue (SCC) for all road users and, through real-time AI-driven analytics, Digital Twins and predictive technologies, it monitors road conditions, enhances traffic management and delivers early warnings to prevent incidents. Aligning with the EU’s Vision Zero, the EU-funded iDriving project offers a scalable solution adaptable to modern transportation trends.

REACTION aims to integrate, validate and demonstrate a fully functional, next-generation, holistic border surveillance and warning platform that provides situational awareness in remote, border areas as an effective means of early warning of critical situations. REACTION will also be connected to the already installed information platforms at the Reception & Identification Centers (RIC) and Fylakio (Evros) for preventive actions. Based on the results of three successful EU-funded projects (CERETAB, AIDERS and ROBORDER), a variety of unmanned vehicles and related operational equipment will be deployed using a simplified command scheme. Advances in artificial intelligence for detection, recognition and classification will enhance unmanned vehicle operations with a fully automated early detection system based on improved multimodal data fusion models. In addition, innovative solutions will be incorporated to facilitate the collection of information on high-risk incidents involving migrants.

The logistics industry faces challenges in optimising resource usage and reducing its environmental impact. As demands grow, the need for intelligent services becomes critical. With this in mind, the EU-funded TRACE project is developing a universal platform integrated with blockchain technology to revolutionise logistics, decrease energy consumption, and cut emissions while securing resilient, uninterrupted deliveries. Facing increased demand and environmental concerns, the industry is turning to intelligent services to optimise shared resources and reduce its carbon footprint. In this context, TRACE offers a universal platform integrating planning, scheduling, optimisation, and events management as well as the use of blockchain technology to facilitate the real-time conclusion of smart contracts and financial operations.

Intentional dumping of polluting substances and illegal disposal of hazardous waste are examples of organised environmental crime, which can be challenging to detect and difficult to investigate. The EU-funded PERIVALLON project will combat organised environmental crime by developing innovative tools and solutions for detecting and preventing such activities as well as improving international cooperation. The project will leverage advanced technologies such as artificial intelligence, geospatial intelligence, remote sensing and online monitoring to provide enhanced investigation processes and methodologies. Moreover, it will provide a comprehensive intelligence picture of environmental crime in Europe through its Environmental Crime Observatory. The capacity of end users, including police authorities and border guards, will also be improved by innovative training curricula and field tests.

Wildfires are unplanned and usually rage uncontrolled. Managing these fires is also a societal and ecological challenge. The EU-funded TREEADS project will focus on the forests at risk of wildfire to develop new products and integrate them in a holistic Fire Management platform aimed at optimising and reusing the existing socio-technological resources. The project proposes the use of a real-time risk assessment tool that can receive multiple classification inputs and works with an innovative neutral network-powered risk factor indicator. TREEADS will use alkali-activated construction materials integrating post-wildfire wood ashes to create a model of fire-adapted communities and use advanced techniques empowered by AI and diverse toolsets for prevention and preparedness.

ASPiDA is a system that will be able to integrate, articulate and collect innovative technologies from the field of electronics, the Internet of Things, information technology, robotic communications, optimal control

The safety of passengers on ships represents a major concern for a ship’s security officer and crew. The EU-funded ISOLA project will develop, test, deploy, demonstrate and validate a systematic and entirely automated security approach based on the integration of innovative sensing technologies, monitoring, data fusion, real-time alarming and reporting during incidents. The project will establish strategies and methods to easily incorporate solutions that ensure passenger and crew safety within existing ship systems, propose innovative sensor and visual technologies and create a complex collaborative system for monitoring and identifying security risks. The project will also propose early warning methods to prevent security incidents and allow the easy involvement of authorities in case of crisis.

As artificial intelligence expands, there is a growing need to offer value-added earth observation products and services. The EU-funded CALLISTO project plans to integrate Copernicus data, already indexed in DIAS platforms such as ONDA-DIAS, utilising high-performance computing infrastructures for enhanced scalability when needed. Complementary distributed data sources include Galileo positioning data, visual content from unmanned aerial vehicles as well as web and social media data, linked with open geospatial and in situ sensor data. Artificial intelligence methods are then used to extract vital knowledge for the end users.

The main objective of the HAV project is to research and develop the first Greek Autonomous Vehicle for both research and commercial purposes. The project will enable the research entities to expand their current knowledge in developing the necessary autonomous driving subsystems and evaluate them on a real platform in the national road network and conditions, while the associated companies will be able to acquire and expand their know-how in this emerging industry field. The process of developing and evaluating the autonomous vehicle and all its subsystems is expected to contribute in direct industry engagement with research organizations for the benefit of both parties in a technology sector, that although rapidly evolving, has not yet been mapped at a national level. For the first time it will be possible to transfer innovative algorithms of artificial intelligence and deep learning from simulation and laboratory environment to real driving conditions through the developed platform. The transition of research and experimental solutions to commercially viable innovative products will be made possible through the detailed and foreseen testing program. With the successful completion of the project, a purely Greek autonomous vehicle will be accessible for the first time to all national research entities with the corresponding benefits.

Europe’s borders are under significant pressure by migration flows, armed conflicts in surrounding territories, smuggling of goods and humans and transnational crime. However, monitoring the routes used by criminal networks is prevented by geographical challenges, such as dense forests, high mountains, rough lands, sea and river areas. The EU-funded NESTOR project will demonstrate an entirely functional, next-generation, comprehensive border surveillance system offering pre-frontier situational awareness beyond sea and land borders. The system is based on the concept of the European integrated border management and relies on optical, thermal imaging and radio frequency spectrum analysis technologies fed by an interoperable sensors network.

CREST’s overall objective is to improve the effectiveness and efficiency of LEAs intelligence, operation, and investigation capabilities, through the automated detection, identification, assessment, fusion, and correlation of evidence acquired from heterogeneous multimodal data streams. Such data streams include (but are not limited to) Surface/Deep/Dark Web and social media sources and interactions, IoT-enabled devices (including wearable sensors), surveillance cameras (static, wearable, or mounted on UxVs), and seized devices and hard disks. CREST will achieve this objective by developing an innovative prediction, prevention, operation, and investigation platform that will build upon the concept of multidimensional integration and correlation of heterogeneous multimodal data streams and delivery of pertinent information to different stakeholders in an interactive manner tailored to their needs.

For the EU, border security and surveillance is of main interest. The EU-funded ARESIBO project aims to improve border surveillance systems by providing the operational teams and the tactical command and control level with accurate and comprehensive information. The ARESIBO system will be developed in stages during the course of 3 years. Two major versions will lead to sub-versions for land and maritime borders. The system will be tested and assessed in a controlled environment enabling testing at any time without pre-requisite authorisations. It will also be tested in live conditions in Finland, Greece, Romania and Portugal.

The main objective of the CoFly program is to develop innovative functionalities for small-sized aerial vehicles (MAV) systems that aim to reduce operational costs as well as to integrate “smart” modules to facilitate a simple user in handling the vehicle. The proposed architecture aims to optimize the capabilities of a robot through software for a wider range of applications while the requirements of the corresponding hardware are limited to the absolutely necessary ones in order to keep the cost low. The system’s functionalities will be thoroughly tested in various agricultural crops in order to confirm the correct operation in complex environments with different non-uniformities.

Development of a method for separating and mapping varieties of the Greek vineyard, utilizing cutting-edge technologies with the aim of implementing EU Directives, updating the Viticultural Register and supporting PDO specifications

The complexity of threats faced by border authorities and law enforcement agencies across Europe limits their efficiency in patrolling and safeguarding the borders. Although there are several research tools and efforts targeting these areas independently, border authorities lack an intelligent and unique solution. The EU-funded ROBORDER project aims to develop and demonstrate a fully functional autonomous border surveillance system. The system will use unmanned mobile robots, including aerial, water surface, underwater, and ground vehicles that will operate both independently and in swarms, incorporating additional sensors as part of an interoperable network. Moreover, it will utilise adaptable sensing and robotic technologies capable of operating in various settings. The project will focus on developing detection capabilities for illegal activities and hazardous incidents.

The purpose of the RAWFIE initiative is to create a federation of different network testbeds that will work together to make their resources available under a common framework. Specifically, it aims at delivering a unique, mixed experimentation environment across the space and technology dimensions. RAWFIE will integrate numerous testbeds for experimenting in vehicular (road), aerial and maritime environments. A Vehicular Testbed (VT) will deal with Unmanned Ground Vehicles (UGVs) while an Aerial Testbed (AT) and a Maritime Testbed (MT) will deal with Unmanned Aerial Vehicles (UAVs) and Unmanned Surface Vehicles (USVs) respectively. The RAWFIE consortium includes all the possible actors of this highly challenging experimentation domain, from technology creators to integrators and facility owners. The basic idea behind the RAWFIE effort is the automated, remote operation of a large number of robotic devices (UGVs, UAVs, USVs) for the purpose of assessing the performance of different technologies in the networking, sensing and mobile/autonomic application domains. RAWFIE will feature a significant number of UxV nodes for exposing to the experimenter a vast test infrastructure. All these items will be managed by a central controlling entity which will be programmed per case and fully overview/drive the operation of the respective mechanisms (e.g. auto-pilots, remote controlled ground vehicles). Internet connectivity will be extended to the mobile units to enable the remote programming (over-the-air), control and data collection. Support software for experiment management, data collection and post-analysis will be virtualized to enable experimentation from everywhere in the world. The vision of Experimentation-as-a-Service (EaaS) will be promoted through RAWFIE. The IoT paradigm will be fully adopted and further refined for support of highly dynamic node architectures.

Current multi-AUV (autonomous underwater vehicle) systems are far from being capable of fully autonomously taking over real-life complex situation-awareness operations. As such operations require advanced reasoning and decision-making abilities the existing designs have to heavily rely on human operators. But humans can easily be overwhelmed by the information overload, fatigue can act negatively to their performance, properly coordinating vehicles actions is hard and continuous operation is all but impossible. NOPTILUS takes the view that an effective fully-autonomous multi-AUV system is capable of overcoming these shortcomings and replacing human-operated operations. To successfully attain such an objective, significant advances are required in the fields of cooperative/cognitive-based communications, sonars, perceptual sensory-motor, learning motion control and learning/cognitive-based situation understanding.

The Sensor Web Fire Shield (SWeFS) research project aims at delivering: a methodology for developing a novel Sensor Web platform for dynamic data-driven assimilation (DDDAS) for securing the Wildland-Urban Interface (WUI) zones against environmental risks, and, a prototype DDDAS system specifically optimized/tuned for addressing the serious threat of forest fires in Greece. SWeFS calls for multidisciplinary research in the areas of sensor networks, distributed vision systems, remote sensing, geographical information systems (GIS), data stream fusion, space-time predictive modeling and control systems. The main objectives of the proposed research are: Design a novel Sensor Web architecture with heterogeneous sensors, remote sensing and risk prediction models into a closed loop system for the effective and timely detection of environmental risks. Test the proposed architecture through the development of a prototype platform for fire detection in WUI zones in Greece. Improve the prediction of the spatiotemporal evolution of a hazardous phenomenon by adopting a DDDAS approach for calibrating simulation models in real-time.

Micro helicopter design, vision-based motion estimation and 3D reconstruction without GPS. In the sFLY project autonomous micro helicopters are about to play major roles in tasks like search and rescue, security surveillance and law enforcement. The ability to fly allows to avoid obstacles on the ground and to have an excellent bird’s eye view. Their navigational and hovering advantages make them the ideal platform for exploration, mapping and monitoring tasks. Fully autonomous operation in cities or other dense environments involves challenges on all levels of helicopter design, perception, actuation, control, navigation and power supply that have yet to be solved. This project will focus on micro helicopter design, visual 3D mapping and navigation, low power communication and multi-robot control under environmental constraints. It will lead to novel micro flying robots that are: inherently safe due to very low weight; capable of vision-based fully autonomous navigation and mapping; capable of coordinated flight in small swarms in constrained and dense environments.