Observation missions with UAVs : defining and learning models for active perception and proposition of an architecture enabling repeatable distributed simulations ; Missions d'observations pour des drones : définition et apprentissage de modèles pour la perception active, et proposition d'une architecture permettant des simulations distribuées répétables
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| Název: | Observation missions with UAVs : defining and learning models for active perception and proposition of an architecture enabling repeatable distributed simulations ; Missions d'observations pour des drones : définition et apprentissage de modèles pour la perception active, et proposition d'une architecture permettant des simulations distribuées répétables |
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| Autoři: | Reymann, Christophe |
| Přispěvatelé: | Équipe Robotique et InteractionS (LAAS-RIS), Laboratoire d'analyse et d'architecture des systèmes (LAAS), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT), INSA de Toulouse, Simon Lacroix |
| Zdroj: | https://theses.hal.science/tel-02368597 ; Automatic. INSA de Toulouse, 2019. English. ⟨NNT : 2019ISAT0017⟩. |
| Informace o vydavateli: | CCSD |
| Rok vydání: | 2019 |
| Sbírka: | Université Toulouse III - Paul Sabatier: HAL-UPS |
| Témata: | SIMULATION INFRASTRUCTURE, ROBOTICS, UAVS, SLAM, LEARNING ERROR MODELS, ROBOTIQUE, APPRENTISSAGE DE MODELES D’ERREUR, INFRASTRUCTURE DE SIMULATION, [SPI.AUTO]Engineering Sciences [physics]/Automatic |
| Popis: | This thesis focuses on perception tasks for an unmanned aerial vehicle (UAV). When sensing is the finality, having a good environment model as well as being capable of predicting the impacts of future observations is crucial. Active perception deals with integrating tightly perception models in the reasoning process, enabling the robot to gain knowledge about the status of its mission and to replan its sensing trajectory to react to unforeseen events and results. This manuscript describes two approaches for active perception tasks, in two radically different settings. The first one deals with mapping highly dynamic and small scale meteorological phenomena such as cumulus clouds. The presented approach uses Gaussian Process Regression to build environment models, learning its hyperparameters online. Normalized marginal information metrics are introduced to compute the quality of future observation trajectories. A stochastic planning algorithm is used to optimize an utility measure balancing maximization of theses metrics with energetic minimization goals. The second setting revolves around mapping crop fields for precision agriculture purposes. Using the output of a monocular graph Simultaneous Localization and Mapping (SLAM) algorithm, a novel approach to building a relative error model is proposed. This model is learned both from features extracted from the SLAM algorithm’s data structures, as well as the underlying topology of the covisibility graph of the observations. All developments have been tested using realistic, distributed simulations. An analysis of the simulation issue in robotics is proposed. Focusing on the problem of managing time advancement of multiple interconnected simulators, a novel solution based on a decentralized scheme is presented. ; Cette thèse se focalise sur des tâches de perceptions pour des drones à voilures fixes (UAV). Lorsque la perception est la finalité, un bon modèle d'environnement couplé à la capacité de prédire l'impact de futures observations sur celui-ci est crucial. La ... |
| Druh dokumentu: | doctoral or postdoctoral thesis |
| Jazyk: | English |
| Relation: | NNT: 2019ISAT0017 |
| Dostupnost: | https://theses.hal.science/tel-02368597 https://theses.hal.science/tel-02368597v2/document https://theses.hal.science/tel-02368597v2/file/2019ReymannChristophe.pdf |
| Rights: | info:eu-repo/semantics/OpenAccess |
| Přístupové číslo: | edsbas.78AB9235 |
| Databáze: | BASE |
Nájsť tento článok vo Web of Science | Abstrakt: | This thesis focuses on perception tasks for an unmanned aerial vehicle (UAV). When sensing is the finality, having a good environment model as well as being capable of predicting the impacts of future observations is crucial. Active perception deals with integrating tightly perception models in the reasoning process, enabling the robot to gain knowledge about the status of its mission and to replan its sensing trajectory to react to unforeseen events and results. This manuscript describes two approaches for active perception tasks, in two radically different settings. The first one deals with mapping highly dynamic and small scale meteorological phenomena such as cumulus clouds. The presented approach uses Gaussian Process Regression to build environment models, learning its hyperparameters online. Normalized marginal information metrics are introduced to compute the quality of future observation trajectories. A stochastic planning algorithm is used to optimize an utility measure balancing maximization of theses metrics with energetic minimization goals. The second setting revolves around mapping crop fields for precision agriculture purposes. Using the output of a monocular graph Simultaneous Localization and Mapping (SLAM) algorithm, a novel approach to building a relative error model is proposed. This model is learned both from features extracted from the SLAM algorithm’s data structures, as well as the underlying topology of the covisibility graph of the observations. All developments have been tested using realistic, distributed simulations. An analysis of the simulation issue in robotics is proposed. Focusing on the problem of managing time advancement of multiple interconnected simulators, a novel solution based on a decentralized scheme is presented. ; Cette thèse se focalise sur des tâches de perceptions pour des drones à voilures fixes (UAV). Lorsque la perception est la finalité, un bon modèle d'environnement couplé à la capacité de prédire l'impact de futures observations sur celui-ci est crucial. La ... |
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