In-hand object localization and control : enabling dexterous manipulation with robotic hands
By: Pfanne, Martin.
Series: Springer tracts in advanced robotics. / edited by Bruno Siciliano and Oussama Khatib ; v.149.Publisher: Switzerland Springer 2022Description: xxxix, 180p.ISBN: 9783031069666.Subject(s): Manipulators (Mechanism) | Robot handsDDC classification: 629.8933 | P472i 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.Item type | Current location | Collection | Call number | Status | Date due | Barcode | Item holds |
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Books | PK Kelkar Library, IIT Kanpur | General Stacks | 629.8933 P472i (Browse shelf) | Available | A186260 |
Browsing PK Kelkar Library, IIT Kanpur Shelves , Collection code: General Stacks Close shelf browser
629.8933 K179r Robot hands and multi fingered haptic interfaces | 629.8933 M713 Mobile service robotics | 629.8933 M919M MULTI-POINT INTERACTION WITH REAL AND VIRTUAL OBJECTS | 629.8933 P472i In-hand object localization and control | 629.8933 R448r Robotic manipulators and vehicles | 629.8933 W831G GRIPPERS IN MOTION | 629.895 Au82 Automated guided vehicle systems |
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.
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