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The IFAFRI R&D repository collects and provides information on ongoing and past research projects that focus on innovative first responder technology and concepts. The technology and concepts are linked to the IFAFRI 10 common capability gaps.

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IFAFRI‘S R&D repository is a repository for academia, industry and government representatives to be informed about R&D activities for first responders financed and executed by different stakeholders worldwide. 

The listed projects will be linked to the IFAFRI 10 common global capability gaps. The database will show government representatives at which gaps lots of R&D is ongoing or has been finished. On the other hand, SME’s could pick up innovative concepts from open public R&D projects and develop market-ready solutions. The repository will only collect a minimum of data and will not duplicate existing databases if your project is already listed in a database, e.g. CORDIS; please fill only the mandatory fields and link the project to the original database. The submission process takes, on average, 3 min.


The AIFER project aims to provide targeted, dynamic decision support to the BOS. Information from Earth Observation and internet data is analysed and fused by using AI algorithms in order to support the protection and rescue of people as well as the protection of critical infrastructures.

Information from Earth Observation and internet data (mainly Social Media posts) are analysed and fused by using AI algorithms in order to support the protection and rescue of people as well as the protection of critical infrastructure. End users determine their requirements, discuss them with developers to then integrate them directly into technical research. Comprehensive consideration of ethical, legal and sociological aspects is highly relevant to the project. Validation and integrability into existing operational processes are tested in a practical way together with end users. AIFER primarily addresses the disaster scenario of flooding, whereby the transferability to other disaster scenarios is demonstrated.

The CURSOR proposal aims at developing new and innovative ways of detecting victims under debris. The system compromises UAVs, 3D modelling, and transportation of disposable miniaturised robots that are equipped with advanced sensors.

The CURSOR proposal aims at developing new and innovative ways of detecting victims under debris. This includes the coordinated use of miniaturized robotic equipment and advanced sensors for achieving significant improvements in search and rescue operations with respect to (a) the time used to detect trapped victims after a building structure has collapsed, and (b) an informed and accelerated decision making by first responders during rescue operations allowing for the deployment of expert personnel and, in particular for operations in hazardous environments, suitable equipment at prioritized locations. CURSOR is proposing a system consisting of several integrated technological components. It includes Unmanned Aerial Vehicles (UAVs) for command & control, 3D modelling and transportation of disposable miniaturized robots, that are equipped with advanced sensors for the sensitive detection of volatile chemical signatures of human beings. Information and data collected are transferred in real time to a handheld device operated by first responders at the disaster site.

DeeperSense will develop an acoustic-to-visual system that can transfer low resolution acoustic sonar data into representations and visualizations of the environment that surrounds an underwater robot.

The main objective of DeeperSense is to significantly improve the environment perception capabilities of service robots and therefore improving their performance and reliability, achieving new functionality, and opening up new applications for robotics. DeeperSense adopts a novel approach of using Artificial Intelligence and data-driven Machine Learning / Deep Learning to combine the capabilities of non-visual and visual sensors with the objective to improve their joint capability of environment perception beyond the capabilities of the individual sensors. DeeperSense will focus on underwater robotics as a domain to demonstrate and verify this approach as it is considered one of the most challenging application areas for robot operation and environment perception. The project implements Deep Learning solutions for three use cases that were selected for their societal and environmental relevance and are driven by concrete end-user and market needs. During the project, comprehensive training data will be generated, where the developed algorithms will be trained upon and verified both in the lab and in extensive field trials. The trained algorithms will be optimized to run on the on-board hardware of underwater vehicles, thus enabling real-time execution in support of the autonomous robot behaviour. Both, the algorithms and the data will be made publicly available through online repositories embedded in European research infrastructures. The DeeperSense consortium consists of renowned experts in robotics and marine robotics, artificial intelligence, and underwater sensing. The research and technology partners are complemented by end-users from the three use case application areas. Among others, the dissemination strategy of DeeperSense has the objective to bridge the gap between the European robotics and AI communities and thus strengthen European science and technology.

Biological hazards such as MRSA ("hospital germ") and Ebola endanger emergency forces. For their safety, the DEFERM project targets biological hazard management.

New pathogens heighten the risk for future outbreaks. Ebola, MRSA and the Corona virus underline how highly transmissible and resistant viruses can constrain decontamination. They cause large sample sizes, are difficult to disinfect from the surroundings and easily cross borders. First responders and societies thus face new risks. In order to protect them better, the whole decontamination process is currently being studied in the French-German research project DEFERM.
As part of this project a novel qPCR-detection system is being developed. It will be automated so that it may be used to analyse multiple samples for up to 12 pathogens in parallel and within 45 minutes. The results will allow first-responders to choose their decontamination set-up accordingly. Disinfection steps target resistant and contagious pathogens and are designed to be deployed in a contactless manner. Enriched by an analysis of crisis management in France and Germany, the project is expected to strengthen international cooperation in the face of novel pandemics.


The PAIRS research project develops a platform for crisis management that iteratively learns to identify crises in their formative stages and provides data-driven recommendations for action.

With the establishment and continuous further development, a data-based information basis is created in order to be best prepared for crisis situations and thus strengthen the resilience of Germany as a business location. The Supply Chain Radar obtains its necessary data for creating transparency in crisis situations from a wide variety of domains, such as the health or energy sector and other external data sources from production, supply chain and logistics. With this information, the impact of crisis events on the value chain is made visible to individual companies and public organizations.


Capability gap(s) targeted:
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