Audio source localization by optimal control of a mobile robot

International audienceWe consider the task of audio source localization using a mi-crophone array on a mobile robot. Active localization algo-rithms have been proposed in the literature that can estimate the 3D position of a source by fusing the measurements taken for different poses of the robot. The robot movements are typ-ically fixed, however, or they obey heuristic strategies, such as turning the head and moving towards the source, which may be suboptimal. In this paper, we propose to control the robot movements so as to locate the source as quickly as possible. We represent the belief about the source position by a discrete grid and we introduce a dynamic programming algorithm to find the optimal robot motion minimizing the entropy of the grid. We report initial results in a real environment

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

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