A Parameter Identification Method for Static Cosserat Rod Models: Application to Soft Material Actuators with Exteroceptive Sensors

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Bartholdt, M.; Wiese, M;.Schappler, M.; Spindeldreier, S.; Raatz, A.: A Parameter Identification Method for Static Cosserat Rod Models: Application to Soft Material Actuators with Exteroceptive Sensors. In: 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Piscataway, NJ : IEEE, 2021, S. 624-631. DOI: https://doi.org/10.1109/IROS51168.2021.9636447

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Sum total of downloads: 119




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Abstract: 
Soft material robotics is a rather young research field in the robotics and material science communities. A popular design is the soft pneumatic actuator (SPA) which, if connected serially, becomes a highly compliant manipulator. This high compliance makes it possible to adapt to the environment and in the future might be very useful for manipulation tasks in narrow and wound environments. A central topic is the modelling of the manipulators. While comparatively rigid continuum robots are build of metal or other materials, that conduct a linear behaviour, the material used in soft material robotics often exhibits a nonlinear stress-strain relationship. In this paper we contribute an identification method for material parameters and data-based approach within the constitutive equations of a Cosserat rod model. We target bending and extension stiffness, consider shear and neglect torsional strains. The proposed method is applicable to any continuum robot which can be modelled by the classic theory of special Cosserat rods, including constraint models, and shows great improvement in experimental results with mean position errors of 0.59% reference length.
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: BookPart
Publishing status: acceptedVersion
Issue Date: 2021-12-16
Appears in Collections:Fakultät für Maschinenbau

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pos. country downloads
total perc.
1 image of flag of Germany Germany 47 39.50%
2 image of flag of United States United States 22 18.49%
3 image of flag of China China 8 6.72%
4 image of flag of France France 5 4.20%
5 image of flag of Korea, Republic of Korea, Republic of 4 3.36%
6 image of flag of Russian Federation Russian Federation 3 2.52%
7 image of flag of Japan Japan 3 2.52%
8 image of flag of Switzerland Switzerland 3 2.52%
9 image of flag of Indonesia Indonesia 2 1.68%
10 image of flag of Spain Spain 2 1.68%
    other countries 20 16.81%

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