Modélisation bayésienne et robotique

Francis Colas 1, 2
Abstract : This document describes my research around Bayesian modeling and robotics. My work started with the modeling of biological processes before evolving towards robotics. In both cases, I was interested in both perception and action. I first proposed a model of human perception of planar surfaces with optic flow which fuses in a single framework two concurrent hypotheses of the literature. I also proposed and compared several models of eye movement selection in a Multiple Object Tracking task. I was able to show that the model with explicit uncertainty was the closest to the subjects eye movements. In robotics, I worked on the state estimation of several robots with classical filtering techniques but also including fusion of multiple sources of information of various nature and characteristics. I also discuss the Iterative Closest Point algorithm for which we proposed a more rigorous method for evaluating the different variants. The last piece of work I present deals with online three-dimensional path planning and execution of a tracked robot with significant climbing capabilities. I conclude this document with perspectives on what I call situated robotics, that is robots not taken in isolation but embedded in a sensorized environment shared with humans.
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Habilitation à diriger des recherches
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https://hal.inria.fr/tel-01647934
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Submitted on : Friday, November 24, 2017 - 4:42:00 PM
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Francis Colas. Modélisation bayésienne et robotique. Robotique [cs.RO]. Université de Lorraine, 2017. ⟨tel-01647934⟩

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