Motion planning for robot audition

International audienceRobot audition refers to a range of hearing capabilities which help robots explore and understand their environment. Among them, sound source localization is the problem of estimating the location of a sound source given measurements of its angle of arrival with respect to a microphone array mounted on the robot. In addition, robot motion can help quickly solve the front-back ambiguity existing in a linear microphone array. In this article, we focus on the problem of exploiting robot motion to improve the estimation of the location of an intermittent and possibly moving source in a noisy and reverberant environment. We first propose a robust extended mixture Kalman filtering framework for jointly estimating the source location and its activity over time. Building on this framework, we then propose a long-term robot motion planning algorithm based on Monte Carlo tree search to find an optimal robot trajectory according to two alternative criteria: the Shannon entropy or the standard deviation of the estimated belief on the source location. These criteria are integrated over time using a discount factor. Experimental results show the robustness of the proposed estimation framework to false angle of arrival measurements within ±20◦ and 10% false source activity detection rate. The proposed robot motion planning technique achieves an average localization error 48.7% smaller than a one-step-ahead method. In addition, we compare the correlation between the estimation error and the two criteria, and investigate the effect of the discount factor on the performance of the proposed motion planning algorithm

Towards Informative Path Planning for Acoustic SLAM

Acoustic scene mapping is a challenging task as microphone arrays can often localize sound sources only in terms of their directions. Spatial diversity can be exploited constructively to infer source-sensor range when using microphone arrays installed on moving platforms, such as robots. As the absolute location of a moving robot is often unknown in practice, Acoustic Simultaneous Localization And Mapping (a-SLAM) is required in order to localize the moving robot’s positions and jointly map the sound sources. Using a novel a-SLAM approach, this paper investigates the impact of the choice of robot paths on source mapping accuracy. Simulation results demonstrate that a-SLAM performance can be improved by informatively planning robot paths

SARSCEST (human factors)

People interact with the processes and products of contemporary technology. Individuals are affected by these in various ways and individuals shape them. Such interactions come under the label 'human factors'. To expand the understanding of those to whom the term is relatively unfamiliar, its domain includes both an applied science and applications of knowledge. It means both research and development, with implications of research both for basic science and for development. It encompasses not only design and testing but also training and personnel requirements, even though some unwisely try to split these apart both by name and institutionally. The territory includes more than performance at work, though concentration on that aspect, epitomized in the derivation of the term ergonomics, has overshadowed human factors interest in interactions between technology and the home, health, safety, consumers, children and later life, the handicapped, sports and recreation education, and travel. Two aspects of technology considered most significant for work performance, systems and automation, and several approaches to these, are discussed

Architecture de contrôle d'un robot de téléprésence et d'assistance aux soins à domicile

La population vieillissante provoque une croissance des coûts pour les soins hospitaliers. Pour éviter que ces coûts deviennent trop importants, des robots de téléprésence et d’assistance aux soins et aux activités quotidiennes sont envisageables afin de maintenir l’autonomie des personnes âgées à leur domicile. Cependant, les robots actuels possèdent individuellement des fonctionnalités intéressantes, mais il serait bénéfique de pouvoir réunir leurs capacités. Une telle intégration est possible par l’utilisation d’une architecture décisionnelle permettant de jumeler des capacités de navigation, de suivi de la voix et d’acquisition d’informations afin d’assister l’opérateur à distance, voir même s’y substituer. Pour ce projet, l’architecture de contrôle HBBA (Hybrid Behavior-Based Architecture) sert de pilier pour unifier les bibliothèques requises, RTAB-Map (Real-Time Appearance-Based Mapping) et ODAS (Open embeddeD Audition System), pour réaliser cette intégration. RTAB-Map est une bibliothèque permettant la localisation et la cartographie simultanée selon différentes configurations de capteurs tout en respectant les contraintes de traitement en ligne. ODAS est une bibliothèque permettant la localisation, le suivi et la séparation de sources sonores en milieux réels. Les objectifs sont d’évaluer ces capacités en environnement réel en déployant la plateforme robotique dans différents domiciles, et d’évaluer le potentiel d’une telle intégration en réalisant un scénario autonome d’assistance à la prise de mesure de signes vitaux. La plateforme robotique Beam+ est utilisée pour réaliser cette intégration. La plateforme est bonifiée par l’ajout d’une caméra RBG-D, d’une matrice de huit microphones, d’un ordinateur et de batteries supplémentaires. L’implémentation résultante, nommée SAM, a été évaluée dans 10 domiciles pour caractériser la navigation et le suivi de conversation. Les résultats de la navigation suggèrent que les capacités de navigation fonctionnent selon certaines contraintes propres au positionement des capteurs et des conditions environnementales, impliquant la nécessité d’intervention de l’opérateur pour compenser. La modalité de suivi de la voix fonctionne bien dans des environnements calmes, mais des améliorations sont requises en milieu bruyant. Incidemment, la réalisation d’un scénario d’assistance complètement autonome est fonction des performances de la combinaison de ces fonctionnalités, ce qui rend difficile d’envisager le retrait complet d’un opérateur dans la boucle de décision. L’intégration des modalités avec HBBA s’avère possible et concluante, et ouvre la porte à la réutilisabilité de l’implémentation sur d’autres plateformes robotiques qui pourraient venir compenser face aux lacunes observées sur la mise en œuvre avec la plateforme Beam+

Review of Anthropomorphic Head Stabilisation and Verticality Estimation in Robots

International audienceIn many walking, running, flying, and swimming animals, including mammals, reptiles, and birds, the vestibular system plays a central role for verticality estimation and is often associated with a head sta-bilisation (in rotation) behaviour. Head stabilisation, in turn, subserves gaze stabilisation, postural control, visual-vestibular information fusion and spatial awareness via the active establishment of a quasi-inertial frame of reference. Head stabilisation helps animals to cope with the computational consequences of angular movements that complicate the reliable estimation of the vertical direction. We suggest that this strategy could also benefit free-moving robotic systems, such as locomoting humanoid robots, which are typically equipped with inertial measurements units. Free-moving robotic systems could gain the full benefits of inertial measurements if the measurement units are placed on independently orientable platforms, such as a human-like heads. We illustrate these benefits by analysing recent humanoid robots design and control approaches

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