Border authorities and Law Enforcement Agencies (LEAs) across Europe face important challenges in how they patrol and protect the borders. Their work becomes more problematic considering the heterogeneity of threats, the wideness of the surveyed area, the adverse weather conditions and the wide range of terrains. Although there are several research tools and works targeting these areas independently for border surveillance, nowadays border authorities do not have access to an intelligent holistic solution providing all aforementioned functionalities. Towards delivering such a solution, ROBORDER aims at developing and demonstrating a fully-functional autonomous border surveillance system with unmanned mobile robots including aerial, water surface, underwater and ground vehicles, capable of functioning both as standalone and in swarms, which will incorporate multimodal sensors as part of an interoperable network. The system will be equipped with adaptable sensing and robotic technologies that can operate in a wide range of operational and environmental settings. To provide a complete and detailed situational awareness picture that supports highly efficient operations, the network of sensors will include static networked sensors such as border surveillance radars, as well as mobile sensors customised and installed on board unmanned vehicles. To succeed implementing an operational solution, a number of supplementary technologies will also be applied that will enable the establishment of robust communication links between the command and control unit and the heterogeneous robots. On top of this, detection capabilities for early identification of criminal activities and hazardous incidents will be developed. This information will be forwarded to the command and control unit that will enable the integration of large volumes of heterogeneous sensor data and the provision of a quick overview of the situation at a glance to the operators, supporting them in their decisions.

The METRICS project will organise challenge-led robotics competitions inThe METRICS project will organise challenge-led robotics competitions in the four Priority Areas (PAs) identified in the topic ICT-09-2019-2020: Healthcare, Inspection and Maintenance, Agri-Food, and Agile Production. METRICS is designed to organise competitions as reproducible and objective evaluation campaigns and aims to structure in a sustainable way the European robotics and Artificial Intelligence (AI) community around the four PAs. In order to assess the reliability of the different competing robots in a rigorous and unbiased way, METRICS will develop an evaluation framework based on metrological principles. For each competition, METRICS will organize 3 field evaluation campaigns (in physical environments) and 3 cascade evaluation campaigns (on datasets) to engage with the AI community. The competitions are designed to draw attention from the entire economic sector, the academia and Digital Innovation Hubs (DIH) in Europe, while stimulating public engagement. Indeed, METRICS will implement a dedicated strategy to mobilize external partners and sponsors to support the organization of the competitions or ensure their industrial relevance. A specific outreach strategy will be developed to promote robotics systems towards the general public and ensure the compliance of robots with ethical and socio-economic principles. METRICS brings together 17 partners, all expert in robotics competition and metrology, with highly complementary testing facilities and networks in the four PAs. This experienced consortium will ensure that METRICS benefits from the feedback of past and current competitions and projects and is highly complementary with European initiatives and networks (e.g. DIH). Thus, METRICS will contribute to structuring a sustainable network of European robotics stakeholders in the four PAs through the organisation of industry-relevant competitions based on robust evaluations methods.

ARESIBO aims at improving the efficiency of the border surveillance systems by providing the operational teams and the tactical command and control level with an accurate and comprehensive information. The pillars of research in ARESIBO are three-fold: 1. Set-up a complete configuration at tactical and execution level to optimise the collaboration between human and sensors (fixed and mobile), 2. Improve situation awareness by enhancing the understanding of the situation through adapted processing of sensor data, correlation between heterogeneous data and information and creation of knowledge through deep learning techniques and 3. Create a situation awareness capability at C2 level that will combine reports on previous missions, real time situation understanding and threat analysis for future actions. This capability will be used to optimise the operations (teams deployment and sensor positioning) as well as an online briefing tool for the teams that will be able to access to the results of the previous missions while in the field. ARESIBO integrates research activities in the domain of 1. surveillance platforms (air, ground, surface, underwater) to optimise the collaborative capabilities of the platforms and their positioning (between themselves and with the teams), 2. Sensor processing to interpret, fuse and correlate all the data to produce information and knowledge and 3. Augmented reality techniques to elaborate and provide to the operators a situation awareness picture which is fit for their missions (minimum information for maximal understanding) both as team level and tactical C2 level. The ARESIBO system will be developed incrementally during the 3 years with two major versions that will lead to sub-versions for land and maritime borders. The system will be tested and assessed in 1. a controlled environment enabling testing at any time without pre-requisite authorisations and 2. in real conditions in Finland, Greece, Romania and Portugal for the 2 versions.