A Hierarchical Human-Robot Interaction-Planning Framework for Task Allocation in Collaborative Industrial Assembly Processes
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
-
Online-Ressource
- Language
-
Englisch
- Bibliographic citation
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In: Johannsmeier, L. & Haddadin, S.: A hierarchical human-robot interaction-planning framework for task allocation in collaborative industrial assembly processes. In: IEEE Robotics and Automation Letters 2 (2017), Nr. 1, S. 41-48. DOI: https://doi.org/10.1109/LRA.2016.2535907
- Classification
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Elektrotechnik, Elektronik
- Event
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Veröffentlichung
- (where)
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Hannover, Hannover
- (who)
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Gottfried Wilhelm Leibniz Universität Hannover, Technische Informationsbibliothek (TIB)
- (when)
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2016
- Creator
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Johannsmeier, Lars
- DOI
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10.15488/3178
- URN
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urn:nbn:de:101:1-2020060307001793159936
- Rights
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
25.03.2025, 1:43 PM CET
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Johannsmeier, Lars
- Gottfried Wilhelm Leibniz Universität Hannover, Technische Informationsbibliothek (TIB)
Time of origin
- 2016
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Erratum to: Hazard Analysis of Collaborative Human-Robot Systems: A Simulation- and Agent-based Approach
A hierarchical framework for physical human-robot interaction
Capability-based task allocation in human-robot collaboration
Incorporating Contextual Knowledge Into Human-Robot Collaborative Task Execution
Exploitation of physical coupling for human-robot collaborative object manipulation
Intuitive human-robot cooperation
Planning for Human-Robot Interaction
Hazard Analysis of Collaborative Human-Robot Systems: A Simulation- and Agent-based Approach
Teleoperation interfaces in human-robot teams
Competence Modeling for Human-Robot Cooperation
Trust-Based Human-Robot Industrial Collaboration
Robot Learning from Human Demonstrations for Human-Robot Synergy