ABSTRACT. In the field of chemical and biological defence, technical and technological developments have followed those of threats and risks. As a result, the means of detection, identification, protection, decontamination and medical, preventive or curative treatments have been significantly improved. CBRN defence More efficient, faster and especially more reliable, the equipment has undeniably made it possible to improve the CBRN defences in particular in these specific areas in order to better cope with major events. Without being exhaustive, this paper proposes to present the most significant developments in the field of chemical and biological defence, including the use of Robotics Systems, knowing that technical and technological developments have also been significant in the nuclear and radiological fields which will not be discussed here. Robotics : The Mobile Robotics Systems begin to emerge in applications related to the security and the environmental surveillance: prevention of disasters, intervention during disasters, assistance to Fire-Fighting and/or Protection Services, with all possible kinds of mission ensuring the safety of the human beings. In the event of a CBRNE emergency a necessary but time consuming pre-requisite, that could delay the real rescue operation, is to establish whether the ground or area can be entered safely by human emergency workers The robots will be equipped with sensors that detect the presence of CBR agents and, in parallel, image data is collected and forwarded to an advanced base station.. At the base station the data is processed and combined with geographical information originating from a web of sources; thus providing the personnel leading the operation with in-situ processed data that can improve decision making. The information may also be forwarded to other forces involved in the operation (e.g. fire fighters, rescue workers, police, etc.). The robots will be designed to navigate individually or cooperatively and to follow high-level instructions from the base station. The robots are off-the-shelf units, consisting of wheeled robots for the common fire ground and robotic caterpillars for specialised situations. The robots connect wirelessly to the base station and to each other; using a wireless self-organising network of mobile communication nodes. The robots are intended as the first explorers of the area, as well as in-situ supporters to act as safeguards to human personnel . Reference: - Les agents biologiques et leurs risques ’épidémies/endémies (WS Bio threats, ICI, 28 Feb 2020 – www.ici-belgium.be/events) - The book on Weapons of Mass Destruction and its Prohibition, for which I was honored to be the co-author, was published in late 2019. An updated version that takes into account the evolving threat is expected to be released in the last quarter of 2020 and will be translated into English.

ABSTRACT. Today disaster medicine education uses theoretical lessons and a day of practical workshops. At the end of the training, the students take part in three full-scale disaster exercise days. Although very educational, they require a lot of staff and are quite expensive to set up. We therefore explored different opportunities in order to diversify the practical side of this training: 1. Exercise on scale models 2. Virtual reality exercises 3. Individual exercise on PC under the form of serious game with adaptive tactical choices 4. Modeling by the use of drones1, 2 for areas representing risks which prevent human access (example: chemical risk) or the identification of large-scale areas (example: floods)

These different means offer multiple advantages: 1. Lower operating cost in comparison with full-scale exercises. 2. Ability to continue training in current circumstances (when real courses and exercises are impossible due to confinement). 3. Possibility of extending the certificate of disaster medicine to foreign participants. 4. Possible development of a European certificate in disaster medicine combining these different means with an E-learning and podcasts authorizing a fully exportable education to candidates located in different countries.

ABSTRACT. This paper will present a multi-robot system [1] for the safety of people in outdoor areas. The robot is composed of an aerial vehicle connected through a tethering system to a ground mobile platform and allows the identification of crowds in public areas. The work described is motivated by the ongoing interest in the field of biological security. In view of the current COVID-19 pandemic, our solution may provide a valuable support to the prevention of contagion and reduction of virus spread. The use of a thermal imaging camera on board of a mobile platform allows the remote detection of body temperature in total safety. The multi-robot system consists of a DJI multicopter, a commercial platform tailored to house a camera for crowds detection. The UAV rises up, finds the presence of people aggregations and subsequently guides the ground platform towards the area detected as being at risk. The tethered system increases the flight endurance by avoiding the need to change batteries. In fact, this system ensures, through the use of an appropriate cabling system, to provide the electrical charge necessary to guarantee flight and data connection with the field platform. This platform is a Pioneer 3-AT, a research mobile robot with a four-wheel drive. On top of the robot a departure/landing platform is located, as in [2]–[6]. Before measuring the body temperature, the drone lands on the robot and together they reach the aggregation, thus avoiding to fly above the people, as required by the legislation in force. Given the promising results obtained, further tests on the robustness of the solution against different heights and number of people are being performed. Looking at future developments, the improvements are related to the evaluation of several methods to look for and approach crowds. In conclusion, the project led to the creation of a multi-robot system which can become part of a wider and across-the-board solution, possibly involving the healthcare with the final aim of increasing the people safety.

ABSTRACT. In order for unmanned maritime systems to provide added value for maritime law enforcement agencies, they have to be able to work together as a coordinated team for tasks such as area surveillance and patrolling. Therefore, this paper proposes a methodology that optimizes the coverage of a fleet of unmanned maritime systems, and thereby maximizes the chances of noticing threats. Unlike traditional approaches for maritime coverage optimization, which are also used for example in search and rescue operations when searching for victims at sea, this approaches takes into consideration the limited seaworthiness of small unmanned systems, as compared to traditional large ships, by incorporating the danger level in the design of the optimizer.

ABSTRACT. Localization is a widely known task in a world of robotics. Mobile robots nowadays are becoming fully autonomous. For robot to be fully autonomous, ability to localize itself is very fundamental. Sufficient accuracy is often achieved by using multiple sources of position information. In the scope of this article, two such sources were used: Intel RealSense T265 tracking camera for continuous visual odometry calculation and RGB sensor in a combinations with ArUco fiducial markers to minimize error accumulated in visual odometry. These markers - just like all sources of information - have it’s flaws. To minimize error caused by the flaws of ArUco markers, variance filter was successfully implemented and evaluated. Whole system was tested several times on a drone, which was able to fly autonomously in indoor environment. Furthermore, all processes needed for such localization ran smoothly on Nvidia Jetson Nano computer mounted directly on the drone while flying. All these achievements were proven to be a really strong foundation for autonomous drone, which can later perform useful tasks in indoor environments including: warehouse inventories, industrial halls inspections, or even some security operations. All this without operator, or pilot intervention.

ABSTRACT. Simultaneous localization and mapping (SLAM) is about consistent maps in the long run. Loop closing is the most popular way for ensure long-term consistency in presence of multiple measurements by the same or multiple robots. Loop closure can be executed using raw odometrical data, but a more sophisticated, yet still light-weight method is presented in this paper: a landmark descriptor-based relative displacement calculation method for diminishing unwanted orientation errors that otherwise often lead to map inconsistency. Landmark descriptors are created using light detection and ranging (LiDAR) scans and the relation is calculated using scan-matching. The novelty of this research is a method providing long-term orientation and position correction without additional overhead between landmark detections, thus enabling simple agents to do the SLAM in a cooperative way.

ABSTRACT. The motion planning for mobile robots is a challenging task even if the agent has to reach the target position in a dense, dynamic environment. In this paper, our goal is to develop a motion planning algorithm using the changing uncertainties of the sensor-based data of the obstacles. The collision-free motion must be ensured by the algorithm using a cost function optimization method. As an assumption, some of the data of the obstacles (e.g. positions of the static obstacles) are already known at the beginning of the planning, and the other information (e.g. velocity vectors of moving obstacles) must be measured using the sensors. The algorithm is tested in simulations.

ABSTRACT. Many researches deal with the control of airplanes, especially with the control of fixed wing UAVs. There are already classical books dealing with aircraft dynamic modeling and control or others approaching the problem from the direction of robotics. Most aircraft are underactuated and the actuator models including control deflections and thrust play an important role. From the large domain of the field the paper deals with dynamic modeling, nonlinear backstepping control and dynamic inversion control, and maneuver (reference signal) design. Dynamic models with aerodynamic and thrust parameters for UAVs are necessary for testing the control methods. For this purpose the Sekwa UAV will be used.

ABSTRACT. The information about a system's dynamics represented by measurement data sets are often confined to regions of restricted operations where the system is not sufficiently excited for model identification purposes. Experiments performed in closed-loop with safety constraints allow only for reduced order modeling. In the paper, a set of low order models are identified from real experimental data of the lateral dynamics of an electric passenger car. Low order models are advantageous for on-line computation in model based control, though uncertainty due to neglected dynamics may deteriorate control performance and constraint satisfaction. The effect of uncertainty is analyzed by controller cross-validation where a controller designed based on one model is evaluated on other models playing the role of the true system. This method allows us to qualify not only model-controller pairs, but to determine the properties of input data and model uncertainty, which lead to more useful data sets, more robust and better performing controllers than the others.