期刊
ASSEMBLY AUTOMATION
卷 40, 期 6, 页码 895-904出版社
EMERALD GROUP PUBLISHING LTD
DOI: 10.1108/AA-04-2020-0053
关键词
Peg-in-hole; Robotic assembly; Cartesian DMPs; Learning from demonstration; Compliance; Force control; Control; Automatic assembly; Assembly; Artificial intelligence; Assembly sequence planning
资金
- National Key R&D Program of China [2018YFB1309000]
- National Natural Science Foundation of China [51805025]
Purpose This paper aims to enable the robot to obtain human-like compliant manipulation skills for the peg-in-hole (PiH) assembly task by learning from demonstration. Design/methodology/approach A modified dynamic movement primitives (DMPs) model with a novel hybrid force/position feedback in Cartesian space for the robotic PiH problem is proposed by learning from demonstration. To ensure a compliant interaction during the PiH insertion process, a Cartesian impedance control approach is used to track the trajectory generated by the modified DMPs. Findings The modified DMPs allow the robot to imitate the trajectory of demonstration efficiently and to generate a smoother trajectory. By taking advantage of force feedback, the robot shows compliant behavior and could adjust its pose actively to avoid a jam. This feedback mechanism significantly improves the dynamic performance of the interactive process. Both the simulation and the PiH experimental results show the feasibility and effectiveness of the proposed model. Originality/value The trajectory and the compliant manipulation skill of the human operator can be learned simultaneously by the new model. This method adopted a modified DMPs model in Cartesian space to generate a trajectory with a lower speed at the beginning of the motion, which can reduce the magnitude of the contact force.
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