Trajectory Optimization for the Handling of Elastically Coupled Objects via Reinforcement Learning and Flatness-Based Control

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Kaczor, Daniel; Bensch, Martin; Schappler, Moritz; Ortmaier, Tobias: Trajectory Optimization for the Handling of Elastically Coupled Objects via Reinforcement Learning and Flatness-Based Control. In: Schüppstuhl, T.; Tracht, K.; Henrich, D. (Eds.): Annals of Scientific Society for Assembly, Handling and Industrial Robotics. Berlin: Springer Vieweg, 2020, S. 319-329. DOI: https://doi.org/10.1007/978-3-662-61755-7_29

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To cite the version in the repository, please use this identifier: https://doi.org/10.15488/10390

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Sum total of downloads: 65




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Abstract: 
Positioning objects in industrial handling applications is often compromised by elasticity-induced oscillations reducing the possible motion time and thereby the performance and profitability of the automation solution. Existing approaches for oscillation reduction mostly focus on the elasticity of the handling system itself, i.e. the robot structure. Depending on the task, elastic parts or elastic grippers like suction cups strongly influence the oscillation and prevent faster positioning. In this paper, the problem is investigated exemplarily with a typical handling robot and an additional end effector setup representing the elastic load. The handling object is modeled as a base-excited spring and mass, making the proposed approach independent from the robot structure. A model-based feed-forward control based on differential flatness and a machine-learning method are used to reduce oscillations solely with a modification of the end effector trajectory of the robot. Both methods achieve a reduction of oscillation amplitudes of 85% for the test setup, promising a significant increase in performance. Further investigations on the uncertainty of the parameterization prove the applicability of the not yet widely-used learning approach in the field of oscillation reduction.
License of this version: CC BY 4.0 Unported
Document Type: conferenceObject
Publishing status: acceptedVersion
Issue Date: 2020-07-21
Appears in Collections:Fakultät für Maschinenbau

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pos. country downloads
total perc.
1 image of flag of Germany Germany 54 83.08%
2 image of flag of United States United States 3 4.62%
3 image of flag of France France 3 4.62%
4 image of flag of Iran, Islamic Republic of Iran, Islamic Republic of 2 3.08%
5 image of flag of China China 2 3.08%
6 image of flag of Russian Federation Russian Federation 1 1.54%

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