Robot joints are the main structure for controlling the motion of the machine body, where the motion state of them directly affects the performance of the industrial robot. Due to the difficulty of obtaining the joint torque information of industrial robots, it is very hard to monitor the motion state of them. Based on the velocity and force driven by current of motors, we propose a novel Bayesian optimization framework to predict the joint motion state of industrial robot in this paper. Based on the temporal correlation of joint current and the correlation between the current and motion state of joint, we use the LSTM and BiLSTM to regressing prediction of the current and state of joint motor first. Then, the Bayesian optimization method is used to adjust the hyperparameters of our network, which realize the analysis of the joint motor current under different motion states and improve the accuracy of the prediction of joint motion states. Finally, we design the joint current acquisition platform of industrial robot based on Hall current sensors, which can collect joint currents without contact and generate experimental dataset. Comparing with the popular intelligent methods, the results show that our Bayesian optimization framework realizes a more accurate prediction of motion state for the four-axis industrial robot on the basis of contact-less current acquisition.

机器人关节是控制机体运动的主要结构，其运动状态直接影响工业机器人的性能。由于工业机器人关节扭矩信息获取困难，对其运动状态进行监测非常困难。本文基于电机电流驱动的速度和力，提出了一种新颖的贝叶斯优化框架来预测工业机器人的关节运动状态。基于关节电流的时间相关性以及关节电流与运动状态之间的相关性，我们首先使用LSTM和BiLSTM对关节运动的电流和状态进行回归预测。然后，利用贝叶斯优化方法调整网络的超参数，实现了不同运动状态下关节电机电流的分析，提高了关节运动状态预测的准确性。最后，设计了基于霍尔电流传感器的工业机器人关节电流采集平台，能够非接触式采集关节电流并生成实验数据集。与流行的智能方法相比，结果表明，我们的贝叶斯优化框架在非接触式电流采集的基础上实现了四轴工业机器人运动状态的更准确预测。