In this study, an intelligent robotic precise assembly system for rapid teaching and admittance control was developed. The system comprised three parts, namely the teaching, trajectory learning, and controlling systems. The teaching system controlled the robotic arm through a haptic device and generated tactile feedback for the operator. The trajectory learning system optimized the reference trajectory by mixture Gaussian regression and verified the safety of the reference trajectory through a force field simulation. The controlling system employed admittance control in response to the potential environmental disturbance. A robotic Ethernet-connector assembly system was validated. The results revealed that the arm learned the reference trajectory from ten sets of teaching data. Additionally, the contact force on the aluminium shell in the plugin process was significantly lower than that of the teaching data. Even when the shell was displaced by 1 mm and vibrated at 2 Hz, the robotic arm was able to insert the plastic component in the shell.

在这项研究中，开发了一种用于快速教学和导纳控制的智能机器人精确装配系统。该系统包括三个部分，即教学，轨迹学习和控制系统。教学系统通过触觉设备控制机械臂，并为操作员生成触觉反馈。轨迹学习系统通过混合高斯回归对参考轨迹进行了优化，并通过力场仿真验证了参考轨迹的安全性。该控制系统采用导纳控制来应对潜在的环境干扰。机器人以太网连接器组装系统得到了验证。结果表明，手臂从十组教学数据中学习了参考轨迹。另外，插件过程中铝壳上的接触力明显低于教学数据。即使将外壳移位1 mm并以2 Hz的频率振动，机械手也能够将塑料组件插入外壳。