9 research outputs found

    Intégration contextuelle de données hétérogènes dans un environnement ambiant ouvert et opportuniste : application aux robots humanoïdes

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    Personal robots associated with ambient intelligence are an upcoming solution for domestic care. In fact, helped with devices dispatched in the environment, robots could provide a better care to users. However, such robots are encountering challenges of perception, cognition and action.In fact, such an association brings issues of variety, data quality and conflicts, leading to the heterogeneity and uncertainty of data. These are challenges for both perception, i.e. context acquisition, and cognition, i.e. reasoning and decision making. With the knowledge of the context, the robot can intervene through actions. However, it may encounter task failures due to a lack of knowledge or context changes. This causes the robot to cancel or delay its agenda. While the literature addresses those topics, it fails to provide complete solutions. In this thesis, we proposed contributions, exploring both reasoning and learning approaches, to cover the whole spectrum of problems. First, we designed novel context acquisition tool that supports and models uncertainty of data. Secondly, we proposed a cognition technique that detects anomalous situation over uncertain data and takes a decision in accordance. Then, we proposed a dynamic planner that takes into consideration the last context changes. Finally, we designed an experience-based reinforcement learning approach to proactively avoid failures.All our contributions were implemented and validated through simulations and/or with a small robot in a smart home platformL'association de robots personnels et d’intelligences ambiantes est une nouvelle voie pour l’aide à domicile. Grâce aux appareils intelligents de l'environnement, les robots pourraient fournir un service de haute qualité. Cependant, des verrous existent pour la perception, la cognition et l’action.En effet, une telle association cause des problèmes de variétés, qualités et conflits, engendrant des données hétérogènes et incertaines. Cela complique la perception du contexte et la cognition, i.e. le raisonnement et la prise de décision. La connaissance du contexte est utilisée par le robot pour effectuer des actions. Cependant, il se peut qu’il échoue, à cause de changements de contexte ou par manque de connaissance. Ce qui annule ou retarde son plan. La littérature aborde ces sujets, mais n’offre aucune solution viable et complète. Face à ces verrous, nous avons proposé des contributions, autour à la fois du raisonnement et de l’apprentissage. Nous avons d’abord conçu un outil d'acquisition de contexte qui gère et modélise l’incertitude. Puis, nous avons proposé une technique de détection de situations anormales à partir de données incertaines. Ensuite, un planificateur dynamique, qui considère les changements de contexte, a été proposé. Enfin, nous avons développé une méthode d'apprentissage par renforcement et expérience pour éviter proactivement les échecs.Toutes nos contributions ont été implémentées et validées via simulation ou à l’aide d’un robot dans une plateforme d’espaces intelligent

    A framework for service robots in smart home: an efficient solution for domestic healthcare

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    International audienceDomestic healthcare is becoming more and more important as the population is growing older. The last technological progresses enable numerous possibilities for monitoring and helping users in their everyday lives. Robotics and smart home are two distinct examples. They both provide great features and possibilities, but also limits. This work addresses the combination of robots and smart home. In this paper, we present a robotic framework that relies on the smart environments strengths. We tackled numerous challenges encountered by the robot for perceiving, reasoning and acting at home and that are critical for healthcare applications. Consequently, multiple solutions are presented and evaluated through both simulation and physical tests

    Context-aware planning by refinement for personal robots in smart homes

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    International audienceThe idea of integrating robots and smart environments is becoming more popular. An important challenge for robotics and large advanced applications, such as Ambient Assisted Living, is to enable robots to seamlessly operate as part of smart spaces.They evolve in an unpredictable and highly dynamic environment where context information change quickly and where smart devices can join or leave it at anytime. In this situation, context-aware task planning is a key enabler. Actually, as the environment evolves, plans can become outdated, putting robots in blocking situations. Currently, few planners are able to consider smart spaces constraints during execution phase. In this paper, we propose a novel planning approach called DHTN (Dynamic HTN) based on HTN (Hierarchical Task Networks) planner. DHTN is able to generate and adapt the plan at execution and has the capability to smartly probe the environment by exclusively querying devices that provide useful data. Our approach was implemented and evaluated through simulation and a real life scenario using Nao robot in a smart offic

    Refining visual activity recognition with semantic reasoning

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    International audienceAs elderly care is getting more and more important, monitoring of activity of daily living (ADL) has become an active research topic. Both robotic and pervasive computing domains, through smart homes, are creating opportunities to move forward in ADL field. Multiple techniques were proposed to identify activities, each with their features, advantages and limits. However, it is a very challenging issue and none of the existing methods provides robust results, in particular in real daily living scenarios. This is particularly true for vision-based approaches used by robots. In this paper, we propose to refine a robot's visual activity recognition process by relying on smart home sensors. We assert that the consideration of further sensors and the knowledge about the target user together with the semantic by means of an ontology and a reasoning layer in the recognition process, has improved the existing works results. We experimented through multiple activity recognition scenarios with and without refinement to assess the relevance of such a combination. Although our tests reveal positive results, they also point out limits and challenges that we discuss in this pape

    FSCEP: a new model for context perception in smart homes

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    International audienceWith the emergence of the Internet of Things and smart devices, smart homes are becoming more and more popular. The main goal of this study is to implement an event driven system in a smart home and to extract meaningful information from the raw data collected by the deployed sensors using Complex Event Processing (CEP). These high-level events can then be used by multiple smart home applications in particular situation identification. However, in real life scenarios, low-level events are generally uncertain. In fact, an event may be outdated, inaccurate, imprecise or in contradiction with another one. This can lead to misinterpretation from CEP and the associated applications. To overcome these weaknesses, in this paper, we propose a Fuzzy Semantic Complex Event Processing (FSCEP) model which can represent and reason with events by including domain knowledge and integrating fuzzy logic. It handles multiple dimensions of uncertainty, namely freshness, accuracy, precision and contradiction. FSCEP has been implemented and compared with a well known CEP. The results show how some ambiguities are solve

    A fuzzy semantic CEP model for situation identification in smart homes

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    International audienceUncertainty is an essential issue for smart home applications. Events generated from sensors can be outdated, inaccurate, imprecise or in contradiction with other ones. These unreliable data can lead to dysfunction in smart home applications. To tackle these challenges, we propose a new model named FSCEP (Fuzzy Semantic Complex Event Processing) that integrates fuzzy logic paradigm, semantic features through an ontology and traditional CEP. We confronted FSCEP with other works tackling uncertainty for CEP and experimented it through simulation with early but promising resul
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