Abstract
When performing robotic assembly, a task should be conducted through force-based control such as impedance control. Using impedance control, it is possible to control the contact force by appropriately adjusting the impedance parameters. However, the impedance parameters should be set by the user because it is difficult to accurately recognize the dynamics of the contact environment, which takes a lot of time because it should be performed whenever the assembly task changes. Moreover, the parameters may not be optimal because it depends on the experience and skill level of the user. To this end, a reinforcement learning-based impedance parameter tuning method is proposed in this study. Since this method uses only the physics-based robotic simulation on the virtual environment, there is no risk of damaging the robots or parts and learning time can be significantly reduced. The proposed method was verified by assembling an HDMI connector with a tolerance of 0.03 mm. Impedance parameters were learned in the virtual environment and transferred to the real environment. Finally, it was confirmed that parameter tuning for impedance without the aid of the user is possible by using the proposed method.
Original language | English |
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Title of host publication | 2021 21st International Conference on Control, Automation and Systems, ICCAS 2021 |
Publisher | IEEE Computer Society |
Pages | 833-836 |
Number of pages | 4 |
ISBN (Electronic) | 9788993215212 |
DOIs | |
Publication status | Published - 2021 |
Event | 21st International Conference on Control, Automation and Systems, ICCAS 2021 - Jeju, Korea, Republic of Duration: 2021 Oct 12 → 2021 Oct 15 |
Publication series
Name | International Conference on Control, Automation and Systems |
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Volume | 2021-October |
ISSN (Print) | 1598-7833 |
Conference
Conference | 21st International Conference on Control, Automation and Systems, ICCAS 2021 |
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Country/Territory | Korea, Republic of |
City | Jeju |
Period | 21/10/12 → 21/10/15 |
Bibliographical note
Publisher Copyright:© 2021 ICROS.
Keywords
- Connector assembly
- Reinforcement learning
- Robotic assembly
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Science Applications
- Control and Systems Engineering
- Electrical and Electronic Engineering