Safety Map: A Unified Representation for Biomechanics Impact Data and Robot Instantaneous Dynamic Properties

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Mansfeld, N. et al.: Safety Map: A Unified Representation for Biomechanics Impact Data and Robot Instantaneous Dynamic Properties. In: IEEE Robotics and Automation Letters 3 (3), S. 1880-1887. DOI: https://doi.org/10.1109/LRA.2018.2801477

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To cite the version in the repository, please use this identifier: https://doi.org/10.15488/3573

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Sum total of downloads: 567




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Abstract: 
Close physical human-robot interaction makes it essential to ensure human safety. In particular, the intrinsic safety characteristics of a robot in terms of potential human injury have to be understood well. Then, minimal potential harm can be made a key requirement already at an early stage of the robot design. In this letter, we propose the safety map concept, a map that captures human injury occurrence and robot inherent global or task-dependent safety properties in a unified manner, making it a novel, powerful, and convenient tool to quantitatively analyze the safety performance of a certain robot design. In this letter, we derive the concept and elaborate the map representations of the PUMA 560, KUKA Lightweight Robot IV+, and injury data of the human head and chest. For the latter, we classify and summarize the most relevant impact studies and extend existing literature overviews. Finally, we validate our approach by deriving the safety map for a pick and place task, which allows us to assess human safety and guide the task/robot designer how to take measures in order to account for both safety and task performance requirements, respectively.
License of this version: Es gilt deutsches Urheberrecht. Das Dokument darf zum eigenen Gebrauch kostenfrei genutzt, aber nicht im Internet bereitgestellt oder an Außenstehende weitergegeben werden.
Document Type: Article
Publishing status: acceptedVersion
Issue Date: 2018-02-02
Appears in Collections:Fakultät für Elektrotechnik und Informatik

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pos. country downloads
total perc.
1 image of flag of Germany Germany 290 51.15%
2 image of flag of United States United States 65 11.46%
3 image of flag of Canada Canada 28 4.94%
4 image of flag of China China 24 4.23%
5 image of flag of France France 16 2.82%
6 image of flag of United Kingdom United Kingdom 14 2.47%
7 image of flag of India India 11 1.94%
8 image of flag of Japan Japan 10 1.76%
9 image of flag of Serbia Serbia 7 1.23%
10 image of flag of Australia Australia 7 1.23%
    other countries 95 16.75%

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