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            <title xml:lang="en">Probabilistic motion planning algorithms for a mobile robot</title>
            <title xml:lang="fr">Algorithmique de la planification de mouvement probabiliste pour un robot mobile</title>
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            <idno type="halRefHtml">Intelligence artificielle [cs.AI]. Université Paris 8 Vincennes Saint-Denis, 2004. Français. &lt;a target="_blank" href="https://www.theses.fr/"&gt;&amp;#x27E8;NNT : &amp;#x27E9;&lt;/a&gt;</idno>
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                <title xml:lang="en">Probabilistic motion planning algorithms for a mobile robot</title>
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                    <forename type="first">Nicolas</forename>
                    <surname>Jouandeau</surname>
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                <authority type="supervisor">Arab Ali Chérif</authority>
                <authority type="jury">Patrick Greussay</authority>
                <authority type="jury">Raja Chatila</authority>
                <authority type="jury">Yacine Amirat</authority>
                <authority type="jury">René Zapata</authority>
                <authority type="jury">Jacqueline Signorini</authority>
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                <term xml:lang="en">Motion Planning</term>
                <term xml:lang="en">Monte Carlo MC method</term>
                <term xml:lang="en">Artificial intelligence algorithm</term>
                <term xml:lang="fr">Méthode Monte-Carlo</term>
                <term xml:lang="fr">Planification de Mouvements</term>
                <term xml:lang="fr">Intelligence artificielle</term>
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              <p>In this thesis, we are studying incremental probabilistic motion planning. Our studies present a new fast algorithm to expand Rapidly exploring Random Tree (RRT) and a new irregular cell partition based on visibility. Our algorithm improves the existing successful probabilistic path planner called RRT by restricting each expansion step to the first collision free configuration. The analysis of the principal sampling's properties used in probabilistic motion planning leads us to propose a new irregular cell partition based on visibility. This new decomposition is tested in narrow environments and in cluthered ones. Results show that this new algorithm and this new decomposition are two significant componants of RRT methods. The motion planner we developped is implemented for mobile robot, evolving in a static well-known environment.</p>
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              <p>Nous étudions dans ce mémoire la planification de mouvement probabiliste incrémentale. Nos travaux se concrétisent par un nouvel algorithme de construction des arbres aléatoires d'exploration rapide et une nouvelle décomposition de l'espace des configurations en cellules irrégulières. Partant d'une synthèse des méthodes de planification incrémentale probabiliste, nos travaux présentent un algorithme de construction accélérant l'exploration de l'espace de recherche. Partant des principales approches d'échantillonnage de l'espace de recherche utilisées dans les méthodes probabilistes, l'analyse des propriétés associées à ces échantillonnages nous conduit à proposer une décomposition de l'espace en cellules irrégulières adaptée à la notion d'accessibilité. Après définition de cette décomposition, ces algorithmes de construction sont évalués comparativement à un échantillonnage uniforme de l'espace de recherche.</p>
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