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
Zusammenfassung: | |
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. | |
Lizenzbestimmungen: | CC BY 4.0 Unported |
Publikationstyp: | ConferenceObject |
Publikationsstatus: | acceptedVersion |
Erstveröffentlichung: | 2020-07-21 |
Die Publikation erscheint in Sammlung(en): | Fakultät für Maschinenbau |
Pos. | Land | Downloads | ||
---|---|---|---|---|
Anzahl | Proz. | |||
1 | Germany | 102 | 67,55% | |
2 | United States | 20 | 13,25% | |
3 | China | 7 | 4,64% | |
4 | Russian Federation | 5 | 3,31% | |
5 | Turkey | 4 | 2,65% | |
6 | Iran, Islamic Republic of | 4 | 2,65% | |
7 | France | 4 | 2,65% | |
8 | Spain | 2 | 1,32% | |
9 | Taiwan | 1 | 0,66% | |
10 | Canada | 1 | 0,66% | |
andere | 1 | 0,66% |
Hinweis
Zur Erhebung der Downloadstatistiken kommen entsprechend dem „COUNTER Code of Practice for e-Resources“ international anerkannte Regeln und Normen zur Anwendung. COUNTER ist eine internationale Non-Profit-Organisation, in der Bibliotheksverbände, Datenbankanbieter und Verlage gemeinsam an Standards zur Erhebung, Speicherung und Verarbeitung von Nutzungsdaten elektronischer Ressourcen arbeiten, welche so Objektivität und Vergleichbarkeit gewährleisten sollen. Es werden hierbei ausschließlich Zugriffe auf die entsprechenden Volltexte ausgewertet, keine Aufrufe der Website an sich.