Nous nous intéressons à trois problèmes issus du monde des arbres aléatoires discrets et continus. Dans un premier lieu, nous faisons une étude générale des arbres de fragmentation auto-similaires, étendant certains résultats de Haas et Miermont en 2006, notamment en calculant leur dimension de Hausdorff sous des hypothèses malthusiennes. Nous nous intéressons ensuite à une suite particulière d’arbres discrets k-aires, construite de manière récursive avec un algorithme similaire à celui de Rémy de 1985. La taille de l’arbre obtenu à la n-ième étape est de l’ordre de n^(1/k), et après renormalisation, on trouve que la suite converge en probabilité vers un arbre de fragmentation. Nous étudions également des manières de plonger ces arbres les uns dans les autres quand k varie. Dans une dernière partie, nous démontrons la convergence locale en loi d’arbres de Galton-Watson multi-types critiques quand on les conditionne à avoir un grand nombre de sommets d’un certain type fixé. Nous appliquons ensuite ce résultat aux cartes planaires aléatoire pour obtenir la convergence locale en loi de grandes cartes de loi de Boltzmann critique vers une carte planaire infinie. / We study three problems related to discrete and continuous random trees. First, we do a general study of self-similar fragmentation trees, extending some results established by Haas and Miermont in 2006, in particular by computing the Hausdorff dimension of these trees under some Malthusian hypotheses. We then work on a particular sequence of k-ary growing trees, defined recursively with a similar method to Rémy’s algorithm from 1985. We show that the size of the tree obtained at the n-th step if of order n^(1/k), and, after renormalization, we prove that the sequence convergences to a fragmentation tree. We also study embeddings of the limiting trees as k varies. In the last chapter, we show the local convergence in distribution of critical multi-type Galton-Watson trees conditioned to have a large number of vertices of a fixed type. We then apply this result to the world of random planar maps, obtaining that large critical Boltzmann-distributed maps converge locally in distribution to an infinite planar map.

This project investigated the implementation and evaluation of various collision-free path planning algorithms for the Saab Seaeye eM1-7 6-DOF Electric Manipulator (eManip). The primary goal was to enhance the autonomous performance of the eManip by integrating efficient path planning methodologies, ultimately ensuring the avoidance of collisions and manipulator singularities during underwater operations. Key algorithms examined included the Rapidly-exploring Random Trees (RRT) algorithm and its enhanced variants. Through simulation tests in MATLAB and Gazebo, metrics such as planning time, path length, and the number of explored nodes were evaluated. The results highlighted the robustness of Goal-biased and Bidirectional RRT* (Gb-Bd-RRT*), which consistently performed well across various environments. The research also highlighted the correlation between algorithm effectiveness and specific task attributes, emphasizing their adaptability to complex environments. This research contributes valuable insights into the effectiveness of path planning algorithms, informing the selection and integration of viable strategies for 6-DOF robotic manipulators.