Robust and adaptive door operation with a mobile robot
Cita com:
hdl:2117/365367
Tipus de documentArticle
Data publicació2021-05-18
EditorSpringer
Condicions d'accésAccés obert
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Abstract
The ability to deal with articulated objects is very important for robots assisting humans. In this work, a framework to robustly and adaptively operate common doors, using an autonomous mobile manipulator, is proposed. To push forward the state of the art in robustness and speed performance, we devise a novel algorithm that fuses a convolutional neural network with efficient point cloud processing. This advancement enables real-time grasping pose estimation for multiple handles from RGB-D images, providing a speed up improvement for assistive human-centered applications. In addition, we propose a versatile Bayesian framework that endows the robot with the ability to infer the door kinematic model from observations of its motion and learn from previous experiences or human demonstrations. Combining these algorithms with a Task Space Region motion planner, we achieve an efficient door operation regardless of the kinematic model. We validate our framework with real-world experiments using the Toyota human support robot.
Descripció
The version of record is available online at: http://dx.doi.org/10.1007/s11370-021-00366-7
CitacióArduengo, M.; Torras, C.; Sentis, L. Robust and adaptive door operation with a mobile robot. "Intelligent service robotics", 18 Maig 2021, vol. 14, p. 409-425.
ISSN1861-2776
Versió de l'editorhttps://link.springer.com/article/10.1007%2Fs11370-021-00366-7
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