Localizing an intermittent and moving sound source using a mobile robot

International audienceThis paper addresses the problem of localizing and tracking one intermittent, moving sound source using a microphone array on a mobile robot. Robot motion provides a solution for estimating the distance to the source and avoiding front-back ambiguity. We propose a mixture Kalman filter (MKF) framework in order to fuse the robot motion information and the measurements taken at different poses of the robot. Experiments and statistical results demonstrate the ability of the proposed method to track one intermittent sound source in a reverberant environment where false measurements of the source angle of arrival (AoA) and the source activity often occur compared to a method that does not consider tracking source activity into account

Long-term robot motion planning for active sound source localization with Monte Carlo tree search

International audienceWe consider the problem of controlling a mobile robot in order to localize a sound source. A microphone array can provide the robot with information on source localization. By combining this information with the movements of the robot, the localization accuracy can be improved. However, random robot motion or short-term planning may not result in optimal localization. In this paper, we propose an optimal long-term robot motion planning algorithm for active source lo-calization. We introduce a Monte Carlo tree search (MCTS) method to find a sequence of robot actions that minimize the entropy of the belief on the source location. A tree of possible robot movements which balances between exploration and exploitation is first constructed. Then, the movement that leads to minimum uncertainty is selected and executed. Experiments and statistical results show the effectiveness of our proposed method on improving sound source localization in the long term compared to other motion planning methods

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