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le (1h48s)

Printemps Agile 2015 : 04 - Introduction au leadership tribal

Introduction au leadership tribal – Florent Lothon Motiver une ou plusieurs équipes a toujours été un challenge pour un manager ou leader. Un challenge d’autant plus grand avec les nouvelles générations qui exigent – à juste titre – davantage de sens. Plus question d’exécuter une tâche – en particulier répétitive – sans savoir pourquoi. Plus question d’avoir le sentiment de n’être que l’engrenage d’une « machine », sans latitude pour exprimer sa créativité. Dans cette session, nous ne traitons pas de management au sens strict (gérer un ...
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Autres

le (24m21s)

La langue arrachée / Chiara Mulas

La langue arrachée / Chiara Mulas, performance artistique, in "Pey sur paroles" en parallèle du colloque "Serge Pey. Un poète-chercheur en action", colloque international organisé par le Laboratoire "France, Amériques, Espagne–Sociétés, pouvoirs, acteurs" (FRAMESPA), le laboratoire Cultures Anglo-Saxonnes (CAS) et les membres du séminaire POP (Poésie Ou Poésie…) de l'Université Toulouse Jean Jaurès-campus Mirail, Toulouse, Cave Poésie, 14 juin 2018. Thématique 3 : Poésieaction.
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le (8m8s)

2.1. Localization process in a probabilistic framework: basic concepts

In this part, we will talk about localization which is a fundamental problem that a robot has to be able to solve in order to accomplish almost any tasks. In particular, we will start by discussing several motion sensor models and, starting from them, we will derive and we will discuss the most appropriate approaches to solve localization which are adopted by the mobile robotics community.
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le (8m5s)

2.2. Characterization of proprioceptive and exteroceptive sensors

Before deriving the equations of the Bayes filter, I want to remind you a few concepts in the theory of probability, and also some mathematical characterization for the statistical error of the robot's sensors. In particular, in this sequence, what I want to do, is to derive the link between the robot configuration, and the physical quantities that are measured by the robot's sensors.
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le (11m53s)

2.7. Grid Localization: an example in 1D

Now that we have the equations of the Bayes filter, we need a method in order to implement in real cases these equations.So, in the following, I want to discuss two methods, which are commonly adopted by the Mobile Robotics Community and, these, if you want, correspond to two extreme solutions because one is a fully numerical and it is based on a grid and, for the case of localization, is known as the grid-localization approach – and the other one is a fully analytical and it is known as a ...
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le (9m14s)

3.1. Examples for the Action in the EKF

In part 2, we have seen the equations of the Bayes filter, which are the general equations which allow us to update the probability distribution, as the data from both proprioceptive sensors and exteroceptive sensors are delivered. We have seen a possible implementation of these equations, based on a numerical solution: the grid localization.We have also started to see the equations of the Kalman filter, or better the extended Kalman filter. In part 3, we want to better explain these equations starting from a very simple example in 1D. Then ...
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