Wireless sensor systems in indoor situation modeling II (WISM II)

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Contributions à la coordination de tâches et de mouvements pour un système multi-robots

This thesis studies the issue of multi-robot coordination from the standpoint of planning, which contains task planning and motion planning. Two new approaches are proposed for the task planning. The first one is a decentralized approach based on trading rules. This approach is designed to simulate the relationship between buyers and sellers in a business system, to achieve dynamic task allocation by using a mechanism of unsolicited bid. The second approach is an heuristic one which is based on an empirical model. It is designed to assign the transportation task to individual robots by estimating the production rate of goods in a centralized system. Two new approaches are proposed for the motion planning. Both are sampling-based approaches, designed to plan separate kinematic paths for multiple robots to minimize the issue of waiting situation including congestion, collision and deadlock, in an effective way in order to improve the system planning efficiency. All these proposed approaches have been tested and evaluated through simulation experiments.Nous étudions dans ce mémoire le problème de la coordination de tâches et de mouvements pour un système multi-robots du point de vue de la planification. Nos travaux se concrétisent par quatre nouvelles approches : une approche contrarienne centrée sur les échanges, une approche heuristique basée sur un modèle empirique, une approche par échantillonnage dédié à la décision et une approche par échantillonnage adaptatif. Toutes les approches proposées dans ce mémoire ont été testées et évaluées par des expériences de simulation

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp