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le (38m17s)

La soie de capture de l’araignée : gluante, liquide et solide à la fois

Conférence expérimentaleLundi 5 décembre 2016 à 18h30 Arnaud Antkowiak et Paul Grandgeorge, Institut d’Alembert, Université Pierre et Marie Curie et CNRS La soie de capture de l’araignée est bien plus qu’un simple fil collant : elle reste tendue même lorsqu’on la comprime, au lieu de se tordre ou de se plier comme n’importe quel autre fil.D’où provient ce comportement étonnant ? Quelle mécanique cache la soie d’araignée ?À l’aide d’expériences, nous éluciderons les rôles joués par l’élasticité et la capillarité dans ce phénomène et nous fabriquerons un fil synthétique inspiré par la soie de capture et présentant les mêmes propriétés fascinantes ...
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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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