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Tools for early and Effective Reconnaissance in cbRne Incidents providing First responders Faster Information and enabling better management of the Control zone

Periodic Reporting for period 1 - TERRIFFIC (Tools for early and Effective Reconnaissance in cbRne Incidents providing First responders Faster Information and enabling better management of the Control zone)

Reporting period: 2018-05-01 to 2019-10-31

TERRIFFIC aims to deliver a step change in the effectiveness of first responders during the 30 minutes of a Radiological, Nuclear, and/or explosive (RNe) incident. TERRIFFIC will enrich the European response to RNe events by integrating a set of complementary, interconnected and modular software and hardware components into an integrated system. These components include new detectors, algorithms, drones, robots, dispersion models, information management software and decision support systems. In addition, advanced augmented reality technology will be leveraged to provide first responders with continuously updated information during operations, available on ad hoc basis. Leading edge technologies will be provided by the R&D partners, whereas key innovative components will be developed by SMEs already involved in military or first responder markets taking on the commercialisation of the TERRIFFIC System and its components.

The project will focus on explosion containing radioactive or nuclear elements but will also provide detailed information on the applicability of some developments within a chemical and biological (C/B) context. Special attention will be given to standardisation with a view to optimise the integration with future and already applied solutions.

The project aims to deliver a System with TRL 6. Post-project Improvements will be necessary for the System to reach the market.
TERRIFFIC partners have established a close and regular dialogue with practitioners through bi-lateral interviews, interactive workshops and during on field trials. This enabled solution providers to better understand the needs of practitioners during the first 30 minutes of an RNe event. As a result of the many discussions, we have been able to start defining the specifications of the TERRIFFIC system, as well as those of each of its components in terms of robustness, endurance, performance, but also maintenance of operational capability.
Two trials have been held so far in the project. These have provided excellent opportunities to assess the core components in the TERRIFFIC system and to establish a strong baseline, from which future development and integration work can progress. The first trial, a two-day event in Chambéry (April 2019). Several radiation (real source) scenarios were utilised to challenge the components in both indoor and outdoor environments. Some TERRIFFIC components were also used during the eNOTICE Trial in Gurcy (May 2019) to demonstrate the augmented value of drone and robot use for first responders. These many interactions allowed solution providers to specify and prioritize the technological developments.

At the end of period 1, the first prototype versions of the innovative radiation detectors are already available, and, some of them have been validated in-lab (first set of data and images acquired) or tested during the first TERRIFFIC trial:
- A functional prototype of the miniaturised gamma camera was validated in-lab. This camera is compatible with, and easily integrated into, both a light weight robot or drone, due to its reduced size and weight.
- A first working prototype of the beta handheld detector, able to detect beta contamination in a high gamma background, was available and functional during the first trial in April 2019. The handheld detector successfully discriminated between beta and gamma signatures. Practitioners provided very positive feedback on this innovative radiation detector and emphasized its high potential for operational use.
- The design of the SiPR developed by Arktis has been finalised (shape, material, electrical parameters)

A first operational version of the direct dispersion models was developed and tested. Similarly, a first operational version of the inverse dispersion model has been developed and was tested during the first in field trial in Chambery, with promising initial results. The source could be globally located, but improvements needs to be done to better characterise the source.

This first period has also been the opportunity to start working on the integration of the different components into an integrated System. At the end of period 1, standard communication and interfaces have been agreed between the mobile platforms and all embedded sensors. In May 2019, AER and NEX participated in the eNotice trial . Indoor and outdoor inspections using drones and robots were performed and provided a good opportunity to test the communication relay capacity between the UAV and the UGV. A proof of concept of the integration of the CEA’s miniaturised gamma camera (called Nanopix) into the robot developed by Nexter was also successfully tested.
The CBRNE-Frontline software will be used as Command & Control System and will display data collected from the radiological sensors, information on the position of the unmanned vehicles (UAV, UGV) and downwind hazards. This NATO-approved interface, developed by Bruhn Newtech, is already used in conflict zones around the world and therefore meets the requirements for an Open and modular TERRIFFIC System. During period 1, the partners approved the design of an Open Sensor API, based on the integrated SCIM® technology. A first version of the Open Sensor API was released (Deliverable D5.1 submitted).Similarly, a first version of the TERRIFFIC Augmented Reality System was developed.
TERRIFFIC will develop a set of innovative technologies to cope with the below limitations and allow to go from sensor data to tactical advices, with minimum human intervention during a RNe incident:
- Current detectors are too heavy to be mounted on drones, however drones are key to improve response times and reduce risk for first responders. Also, current industrial solutions for measuring beta contamination cannot be deployed in environments where the gamma background is non-negligible, leading to costly mistake at the time of triage of victims and mistaken situational awareness.
- Current plume calculation algorithms calculate plumes based on general parameters, but are not able to integrate effectively data coming from mobile detectors, come up with a more precise estimation of the control zone and quick dynamic updates. This forces first responders to take a wide safety margin when defining the extent and severity of the risks, resulting in reduced effectiveness of resource allocation and response time.
- Current plume detection models are not able to identify where the source(s) may be located but calculate the plume based on a known source location. However, the location of the source can be unknown.
- In general, there is a need for a high level of mobility for various detectors to be applied during a RNe incident in order to (a) decrease the time from first alert to actual deployment of the technology and be (b) to adapt to the changing situation and to collect information and data where it needs to be collected.
- Develop Augmented Reality (AR) solutions to allow the visualization of the invisible (i.e. radiation) and reduce the amount of information that first responders must memorise from verbal information.
- Current information management systems (IMS) are not well adapted to plan the missions of robots and drones and integrate the data from multiple detectors and sources reliably with data from plume models into a regularly updated near real-time Common Operational Picture (COP) for an RNe incident.