基于CNN-GRU的遥操作机器人操作者识别与自适应速度控制方法

传统空间遥操作系统中从端机械臂的运动速度完全取决于操作者的操作速度.为了提高空间遥操作系统的安全性,提出了一种基于操作者操作速度识别的自适应速度控制方法.结合深度学习的理论,提出了一种基于卷积神经网络(CNN)和门控循环单元(GRU)神经网络的融合模型来对操作者的速度进行识别分类.选取了九位受试者构建操作者速度样本库,...

Operator recognition and adaptive speed control method of teleoperation robot based on CNN-GRU

The movement speed of the slave manipulator arm in traditional space teleoperation system completely depends on the operatingspeed of the operator. In order to improve the safety of the space teleoperation system, an adaptive speed control method based on therecognition of the operating speed of the operator is proposed. Combining with the theory of deep learning, a fusion model based onconvolutional neural network (CNN) and gate recurrent unit (GRU) neural network is proposed to identify and classify the speed ofoperator. Nine subjects were selected to construct an operator speed sample library. The operating speed of the operators is divided intothree categories, and the final recognition accuracy rate reaches 92. 71% . And, on this basis, the cascade PID is used to realize theadaptive speed control of the slave manipulator arm. Experiments confirm that the model can also accurately identify new operators. Atthe same time, the accuracy of the model is better than that of the fusion model of convolutional neural network and recurrent neuralnetwork (RNN), and the real-time performance of the model is better than that of the fusion model of convolutional neural network andlong short-term memory (LSTM) neural network. Besides, the adaptive speed control based on this model can reduce the end linearspeed of the manipulator arm while ensuring that the movement trajectory of the slave manipulator arm remains unchanged, which helpsto improve the safety of the space teleoperation system.