로봇학회논문지 (The Journal of Korea Robotics Society)

  • 제17권4호
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  • Pages.455-462
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  • 2022
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  • 1975-6291(pISSN)
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  • 2287-3961(eISSN)
한국로봇학회 (Korea Robotics Society)
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커넥터 조립을 위한 강화학습 기반의 탐색 궤적 생성 및 로봇의 임피던스 강성 조절 방법

Reinforcement Learning-based Search Trajectory Generation and Stiffness Tuning for Connector Assembly

  • 투고 : 2022.06.06
  • 심사 : 2022.09.21
  • 발행 : 2022.11.30
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초록

Since electric connectors such as power connectors have a small assembly tolerance and have a complex shape, the assembly process is performed manually by workers. Especially, it is difficult to overcome the assembly error, and the assembly takes a long time due to the error correction process, which makes it difficult to automate the assembly task. To deal with this problem, a reinforcement learning-based assembly strategy using contact states was proposed to quickly perform the assembly process in an unstructured environment. This method learns to generate a search trajectory to quickly find a hole based on the contact state obtained from the force/torque data. It can also learn the stiffness needed to avoid excessive contact forces during assembly. To verify this proposed method, power connector assembly process was performed 200 times, and it was shown to have an assembly success rate of 100% in a translation error within ±4 mm and a rotation error within ±3.5°. Furthermore, it was verified that the assembly time was about 2.3 sec, including the search time of about 1 sec, which is faster than the previous methods.

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참고문헌

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