Modélisation bayésienne et robotique

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.Ce document décrit mes travaux de recherche autour de la modélisation bayésienne et de la robotique. Mon travail a commencé par la modélisation de processus biologiques avant, dans un deuxième temps, d'évoluer vers la robotique. Dans les deux cas, je me suis intéressé à la fois à la perception et à l'action. J'ai donc proposé un modèle de la perception humaine de plans par le flux optique qui réunit deux hypothèses de la littérature dans un cadre unique. J'ai aussi proposé et comparé différents modèle de la sélection de mouvement oculaire dans une tâche de suivi multi-cibles, et montré que le modèle prenant en compte explicitement l'incertitude proposait des mouvements plus proches de ceux des sujets.Du côté robotique, j'ai travaillé sur l'estimation d'état de plusieurs robots avec des techniques classiques de filtrage mais en incluant la fusion de plusieurs sources d'informations de nature et caractéristiques différentes. Je discute aussi de l'algorithme d'Iterative Closest Point pour proposer une méthode plus rigoureuse d'évaluation des différentes variantes. Le dernier travail que je présente concerne la planification en ligne et l'exécution de chemin pour un robot à chenille avec des capacités de franchissement importantes.Je conclus ce document par des perspectives de travail sur ce que j'appelle la robotique située, c'est-à-dire des robots non plus isolés mais plongés dans un environnement équipé de capteurs et partagé avec des humains

The mean encapsulation rate (artificial unit) of the parental generation reared in copper contaminated and uncontaminated environments (uncontaminated: N=189, mean =64.53, SD =14.54; contaminated: N=184, mean =68.82, SD =14.24).

<p>The encapsulation rate was measured as average gray value of reflected light, which is considered as relative darkness (for more details see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0038832#s4" target="_blank">Materials and methods</a>).</p

The mean encapsulation response of the offspring whose parents were reared in copper contaminated and uncontaminated environments (uncontaminated: N=90, mean =53.52, SD =11.97; contaminated: N=92, mean =61.78, SD =19.29).

<p>The mean encapsulation response of the offspring whose parents were reared in copper contaminated and uncontaminated environments (uncontaminated: N=90, mean =53.52, SD =11.97; contaminated: N=92, mean =61.78, SD =19.29).</p

Kaplan-Meier survival analysis was used for the comparisons of development times.

<p>A reduced probability value of P = 0.05/6 = 0.008 was used to control for multiple comparisons. All comparisons were statistically significant except for that between the progeny of S-P and P-S parents.</p><p>Log Rank (Mantel-Cox) statistics are reported.</p

Binary logistic regression analysis was used to identify factors associated with pathogen resistance.

<p>Survival among the disease treated flies was worse than among the control flies.</p><p>Overall percentage of cases correctly classified by the model: 86.1%.</p><p>Omnibus Tests of Model coefficients: P<0.001.</p><p>Hosmer-Lemeshow Goodness of Fit Test: P = 0.039.</p><p>Nagelkerke R Square: 0.418.</p

Binary logistic regression analysis was used to identify factors associated with pathogen resistance (control-treatment).

<p>Overall percentage of cases correctly classified by the model: 97.4%.</p><p>Omnibus Tests of Model coefficients: P<0.001.</p><p>Hosmer-Lemeshow Goodness of Fit Test: P = 0.781.</p><p>Nagelkerke R Square: 0.686.</p

Summary of analysis of variance on offspring body size (thorax length) with data pooled over sexes. Significant effects are shown in bold.

<p>*Error term used for the test of significance: SS = 1.007, df = 246.321.</p><p>**Error term used for the test of significance: SS = 1.007, df = 246.470.</p>†<p>Error term used for the test of significance: SS = 0.423, df = 262.208.</p>††<p>Error term used for the test of significance: SS = 1.007, df = 246.559.</p>‡<p>Error term used for the test of significance: SS = 0.425, df = 263.097.</p>‡‡<p>Error term used for the test of significance: SS = 0.427, df = 264.103.</p>+<p>Error term used for the test of significance: SS = 0.428, df = 264.718.</p>++<p>Error term used for the test of significance: SS = 0.337, df = 221.</p

Binary logistic regression analysis was used to identify factors associated with pathogen resistance (disease treatment).

<p>Overall percentage of cases correctly classified by the model: 77.5%.</p><p>Omnibus Tests of Model coefficients: P<0.001.</p><p>Hosmer-Lemeshow Goodness of Fit Test: P = 0.675.</p><p>Nagelkerke R Square: 0.322.</p

Summary of analysis of variance on offspring body size (thorax length) separately for males and females. Significant effects are shown in bold.

<p>Summary of analysis of variance on offspring body size (thorax length) separately for males and females. Significant effects are shown in bold.</p

Development time was analyzed using Cox regression survival analysis.

<p>A significant interaction between the maternal and the paternal diets indicates that a parent's dietary effect on offspring development time was dependent upon the dietary effect of the other parent.</p