In-hand object localization and control : enabling dexterous manipulation with robotic hands / Martin Pfanne.
Pfanne, Martin, author.
2022
T55.3.M35
Available Online

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Title In-hand object localization and control : enabling dexterous manipulation with robotic hands / Martin Pfanne.
Author Pfanne, Martin, author.
ISBN 9783031069673 (electronic bk.)
3031069676 (electronic bk.)
9783031069666
3031069668
DOI https://doi.org/10.1007/978-3-031-06967-3
Published Cham : Springer, [2022]
Copyright ©2022
Language English
Description 1 online resource (xxxix, 180 pages) : illustrations (chiefly color).
Item Number 10.1007/978-3-031-06967-3 doi
Call Number T55.3.M35
Dewey Decimal Classification 629.8/933
Summary This book introduces a novel model-based dexterous manipulation framework, which, thanks to its precision and versatility, significantly advances the capabilities of robotic hands compared to the previous state of the art. This is achieved by combining a novel grasp state estimation algorithm, the first to integrate information from tactile sensing, proprioception and vision, with an impedance-based in-hand object controller, which enables leading manipulation capabilities, including finger gaiting. The developed concept is implemented on one of the most advanced robotic manipulators, the DLR humanoid robot David, and evaluated in a range of challenging real-world manipulation scenarios and tasks. This book greatly benefits researchers in the field of robotics that study robotic hands and dexterous manipulation topics, as well as developers and engineers working on industrial automation applications involving grippers and robotic manipulators.
Bibliography, etc. Note Includes bibliographical references.
Access Note Access limited to authorized users.
Source of Description Online resource; title from PDF title page (SpringerLink, viewed September 9, 2022).
Series Springer tracts in advanced robotics ; v. 149. 1610-742X
Available in Other Form Print version: 9783031069666
In-hand object localization and control.
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Introduction
Related Work
Grasp Modeling
Grasp State Estimation
Impedance-based Object Control
Conclusion.

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