Descripción
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Despite the large progress of autonomous systems, their application is often limited e.g. due to safety margins which can be caused by uncertainties in the environment reconstruction. Then, via teleoperation as a fallback solution expert knowledge can be introduced into the control loop via a human operator. Especially for the case of high delay in the communication channel as in space or disaster scenarios, model-mediated teleoperation has been proposed in literature to provide instantaneous and even predictive feedback on the user input. Compared to standard teleoperation with delayed force feedback, predictive model-mediated teleoperation promises direct force feedback on the user input. Though the interaction of robot and environment is easier to model for mobile robots compared to robotic manipulators, state mismatches, model errors and the modeling challenges of complex wheel-ground contacts limit the performance of model-mediated teleoperation setups. In contrast, sensors on the remote slave side can provide additional information which cannot be modelled easily. Therefore, in this paper, we introduce the concept of extended modelmediated teleoperation for mobile robots fusing local fictitious force feedback and remote force feedback. In such a system, the remote force feedback can be measured, computed or fictitious forces. We provide a method to guarantee stability of the extended model-mediated teleoperation (involving time delay, multilateral coupling, fictitious force feedback and permanent updates of the local model) based on the passivity theorem. The benefits of the approach are highlighted in human-in-the-loop experiments with a wheeled mobile robot. | |
Internacional
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Nombre congreso
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IEEE International Vehicles Symposium |
Tipo de participación
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970 |
Lugar del congreso
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Revisores
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ISBN o ISSN
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978-1-5090-4804-5 |
DOI
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Fecha inicio congreso
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26/06/2018 |
Fecha fin congreso
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29/06/2018 |
Desde la página
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Hasta la página
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Título de las actas
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Extended Predictive Model-Mediated Teleoperation of Mobile Robots fusing Local and Remote Force Feedback through Multilateral Control |