Installing a six-dimensional force/torque sensor on an industrial arm for force feedback is a common robotic force control strategy. However, because of the high price of force/torque sensors and the closedness of an industrial robot control system, this method is not convenient for industrial mass production applications. Various types of data generated by industrial robots during the polishing process can be saved, transmitted, and applied, benefiting from the growth of the industrial internet of things (IIoT). Therefore, we propose a constant force control system that combines an industrial robot control system and industrial robot offline programming software for a polishing robot based on IIoT time series data. The system mainly consists of four parts, which can achieve constant force polishing of industrial robots in mass production. (1) Data collection module. Install a six-dimensional force/torque sensor at a manipulator and collect the robot data (current series data, etc.) and sensor data (force/torque series data). (2) Data analysis module. Establish a relationship model based on variant long short-term memory which we propose between current time series data of the polishing manipulator and data of the force sensor. (3) Data prediction module. A large number of sensorless polishing robots of the same type can utilize that model to predict force time series. (4) Trajectory optimization module. The polishing trajectories can be adjusted according to the prediction sequences. The experiments verified that the relational model we proposed has an accurate prediction, small error, and a manipulator taking advantage of this method has a better polishing effect.

中文翻译：

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工业物联网中基于时间序列数据的抛光机器人力控制系统

在工业臂上安装六维力/扭矩传感器用于力反馈是一种常见的机器人力控制策略。然而，由于力/扭矩传感器价格高昂以及工业机器人控制系统的封闭性，这种方法不便于工业大规模生产应用。得益于工业物联网（IIoT）的发展，工业机器人在抛光过程中产生的各类数据可以被保存、传输和应用。因此，我们提出了一种结合工业机器人控制系统和工业机器人离线编程软件的恒力控制系统，用于基于 IIoT 时间序列数据的抛光机器人。该系统主要由四部分组成，可实现工业机器人量产的恒力抛光。(1) 数据采集模块。在机械手上安装六维力/扭矩传感器，收集机器人数据（电流系列数据等）和传感器数据（力/扭矩系列数据）。(2)数据分析模块。建立基于变体长短期记忆的抛光机械手当前时间序列数据与力传感器数据之间的关系模型。(3) 数据预测模块。大量同类型的无传感器抛光机器人可以利用该模型来预测力时间序列。(4) 轨迹优化模块。可以根据预测序列调整抛光轨迹。实验验证了我们提出的关系模型预测准确、误差小，利用该方法的机械手具有较好的抛光效果。