Using wireless sensors and networks program for chemical particle propagation mapping and chemical source localization

Chemical source localization is a challenge for most of researchers. It has extensive applications, such as anti-terrorist military, Gas and oil industry, and environment engineering. This dissertation used wireless sensor and sensor networks to get chemical particle propagation mapping and chemical source localization. First, the chemical particle propagation mapping is built using interpolation and extrapolation methods. The interpolation method get the chemical particle path through the sensors, and the extrapolation method get the chemical particle beyond the sensors. Both of them compose of the mapping in the whole considered area. Second, the algorithm of sensor fusion is proposed. It smooths the chemical particle paths through integration of more sensors\u27 value and updating the parameters. The updated parameters are associated with including sensor fusion among chemical sensors and wind sensors at same positions and sensor fusion among sensors at different positions. This algorithm improves the accuracy and efficiency of chemical particle mapping. Last, the reasoning system is implemented aiming to detect the chemical source in the considered region where the chemical particle propagation mapping has been finished. This control scheme dynamically analyzes the data from the sensors and guide us to find the goal. In this dissertation, the novel algorithm of modelling chemical propagation is programmed using Matlab. Comparing the results from computational fluid dynamics (CFD) software COMSOL, this algorithm have the same level of accuracy. However, it saves more computational times and memories. The simulation and experiment of detecting chemical source in an indoor environment and outdoor environment are finished in this dissertation --Abstract, page iii

Approches environnement-centrées pour la simulation de systèmes multi-agents: Pour un déplacement de la complexité des agents vers l'environnement

This habilitation thesis synthesizes research works which are mainly related to the field of Multi-Agent Based Simulation (MABS). MABS is a general framework for modeling and experimenting with systems in which the dynamics emerges from local interactions among individuals (autonomous agents). Examples of use range from the study of natural systems (e.g. ant colonies, crowds or traffic jams) to the engineering of artificial ones (e.g., collective robotics, distributed artificial intelligence-based softwares). To this end, MABS modeling represents the behavior of individuals, their environment and interactions, so that global dynamics can be computed and studied from the bottom up. In this context, we have been investigating research on the theory and practice of MABS from two different perspectives : (1) the design of generic abstractions dedicated to the modeling of multi-agent dynamics (e.g., the IRM4S model) and (2) the engineering of MABS (MaDKit and TurtleKit platforms). Besides, we have been experimenting with MABS in different application domains such as image processing, video games, and collective robotics. Contrary to approaches that put the emphasis on the agent behaviors, all these works have been done by considering the environment of the agents as a first order abstraction. In this thesis, we first reflect upon the research we have conducted according to this perspective. Next, we show how we actually use this perspective to propose an original approach for using General-Purpose processing on Graphics Processing Units (GPGPU) within MABS, and then present the research perspectives related to our positioning.Les travaux de recherche synthétisés dans ce mémoire s’inscrivent principalement dans le domaine de la modélisation et de la simulation de systèmes multi-agents (SMA). La simulation multi-agents met en œuvre des modèles où les individus, leur environnement et leurs interactions sont directement représentés. Dans ces modèles, chaque individu –agent autonome– possède son propre comportement et produit ses actions en fonction d’une perception locale de son environnement. Ainsi, la simulation multi-agents est utilisée pour étudier des systèmes naturels comme les colonies de fourmis, les dynamiques de foules ou le trafic urbain, mais aussi pour concevoir des systèmes artificiels, par exemple dans le cadre de la robotique collective ou le développement de logiciels basés sur de l’intelligence artificielle distribuée. Dans ce cadre, nos recherches ont porté sur des problématiques liées à la modélisation de simulations multi-agents, avec la proposition de modèles formels et conceptuels (e.g. le modèle IRM4S) et d’outils logiciels génériques (plates-formes MaDKit et TurtleKit), et sur leur utilisation dans divers domaines tels que le jeu vidéo, le traitement numérique de l’image ou la robotique collective. Contrairement aux approches centrées sur la conception des comportements individuels, dans ces travaux l’environnement des agents est considéré comme une abstraction de premier ordre. Dans ce mémoire, nous dressons tout d’abord un bilan de nos recherches en argumentant l’intérêt d’une telle démarche pour les modèles multi-agents. Nous montrons ensuite comment celle-ci nous a récemment permis de proposer une approche originale dans le cadre de l’utilisation du calcul haute performance sur carte graphique (GPGPU) pour la simulation de SMA, avant de présenter les perspectives de recherche associées à notre positionnement