The recent assessment carried out by BARPI shows an increasing evolution of accidentology in classified installations in France with a predominance of fires. In order to characterize their possible impact on the environment and the population, it is essential to collect as quickly and reliably as possible the data relating to the event. Measurement and sampling campaigns are then necessary in order to know the substances emitted, the areas of fallout and to build an adequate sampling plan. The DESIHR project (Swarm Drones for the air Monitoring of High Risk Industrial Sites) aims to study the contribution of new technologies to characterize in real situation and more quickly the substances present in a fire plume and their emission conditions in order to carry out predictive mapping of their propagation. It is based on the use of a fleet of autonomous UAVs capable of adapting its flight plan according to the information acquired by each UAV, in order to fulfill two missions which will consist of : - positioning itself in the plume dispersion axis at increasing distances from the source in order to take samples (coupling micro-sensors, automated opening canisters) that can be rapidly analyzed on the ground thanks to a portable GC/MS for gases and soot (granulometry, chemistry, electron microscopy) in the laboratory. The chemical characterizations will allow to evaluate the dilution rate and flows on vertical sections of the plume. - acquire video images of the plume from outside the plume simultaneously from different viewing angles. This information will be transmitted live (crisis cell) so that image processing can be used to determine parameters useful for plume modeling (plume height, volume, section and shape of the plume, etc.). It is broken down into five tasks : - to select the most relevant UAV payloads for the missions and to define the flight strategy with a swarm of UAVs with regard to various constraints (regulatory, aggressive environments, etc.); - to implement algorithms defining the collective actions of the UAVs, the flight controls and the flight plans. This will provide a fleet of autonomous UAVs whose behavior will be based on the data acquired by the payloads integrated into them; - mechanically integrate the capture systems with the UAVs and perform the electronic interfacing of the capture systems; - set up experiments where the generation of artificial model plumes is controlled. This will then provide a controlled and simple framework for evaluating the performance of the payloads used, the performance of the collective actions undertaken by the UAVs, and finally, the relevance of the approach to the final challenges of chemical analysis and modeling; - to carry out a demonstration in a realistic situation undertaken on the SDIS76 exercise platform in Le Havre, the results of which will make it possible to study the possibility of an industrial valorization of the technological solutions proposed by the project. This project is multidisciplinary, bringing together skills in crisis management, robotics, automation, servo-control, mechanics, metrology of atmospheric pollutants, image analysis, atmospheric modelling and air quality. To cover these fields of expertise, six partners are directly involved in the project. A balance has been found between research and development approaches and representation of end-users and distributors. The project will also benefit from the support of the members of the Normandy UAV Innovation Center (CIDN, 7 founding players including: ULHN- NAE - Le Havre Seine Développement) via access to certain shared equipment (UAVs, simulators) and CIDN resources (indoor/outdoor flight zones).

The APPRENTIS project concerns the safety of industrial or port areas presenting risks (e.g., fire, explosion or toxic leakage). The operational objective is to provide a decision support software tool to plan monitoring and rescue patrols. This tool will minimize the cost of the patrols carried out by mobile agents (as drones or automated vehicles) by optimizing physical and financial resources based on the analysis of data flows. The questions we would like to answer are as follows: • During monitoring, how many mobile agents are required to perform a given set of measurements at given positions? What sensors should each of these agents equip? How to define the patrols of each of the agents in order to meet the overall monitoring requirements? • In the event of an incident, how to use these same monitoring agents to quickly obtain relevant information on the incident, the damage and any victims? How to transport and distribute rescue supplies with the help of intervention agents? Finally, how can we jointly and effectively use monitoring and intervention agents? The originality of the method proposed to solve these problems is based on modeling aspects and a resolution methodology that are derived from discrete event systems (DESs) and artificial intelligence (AI). This dual approach is motivated by the exponential complexity of the problem which appears when the problems of configuration and planning of the patrols of each of the agents are combined, the latter depending on the evaluated configuration. The expected result of the APPRENTIS project is a demonstrator that can serve as a basis for the development of a software devoted to the configuration of the monitoring and intervention patrols from a catalog of equipment, the description of the infrastructure, and the patrol specifications. We target, in particular, 3 types of audiences: 1) Companies with SEVESO classified sites (156 sites in Hauts de France region, 86 sites in Normandy region, 99 sites in the PACA region and 94 sites in the Ile de France region) which are called upon to strengthen the monitoring of their installations; 2) Organizations and local authorities in charge of crisis intervention (SDIS, urban communities, associations as ORMES); 3) Economic interest groups which are concerned with the production, transport or storage of products at risk (Ports of Le Havre, Rouen and Paris - HAROPA, Grand Port Maritime de Marseille - GPMM). The consortium of partners (ULHN - GREAH - EA 3220, AMU - LIS - UMR 7020, IMTLD, USPN - LURPA - EA 1385) was formed on the basis of the partners’ experience in the risk management and in the implementation and use of DES and AI tools. ULHN in Normandy region and IMTLD in Hauts de France region are located in the two regions targeted by the call RA-SIOMRI. AMU and USPN are located in two large and densely populated cities for which the potential impacts of industrial incidents are particularly serious. Finally, the city of Marseille offers similarities with the city of Le Havre through its port activity, an additional vector of risks due to the storage of hazardous materials, and through its concentration of SEVESO industrial sites near residential areas (Fos-sur-Mer near Marseille and Tancarville near Le Havre). The longer-term challenge we initiate here is to coordinate the means of monitoring and intervention in an automated way by combining predictive and decision-making models, and using model-based methods as well as database-based methods.