因现有软体机械臂材料刚度小、模量不稳定, 导致建模与控制难度大. 提出一种基于循环神经网络(recurrent neural network, RNN) 的方法, 用于二自由度(two-degree-of-freedom, 2-DOF) 软体机械臂的运动建模与控制. 使用动作捕捉仪采集不同气压、负载下的位置坐标,并将其导入门控循环单元 (gated recurrent unit, GRU) 神经网络模型进行训练. 当调节超参数至网络结构最优时, 测试集准确度可达 98.87%. 在此基础上, 构建气压与负载到末端位置的映射函数. 实验结果表明, 本方法可将机械臂的控制精度提升至 6∼8 mm, 显著降低了软体机器人的控制与建模难度.

To address the difficulty of modeling and control of existing soft manipulators due to their small material stiffness and unstable modulus, this study proposes a method based on a recurrent neural network (RNN) for the motion modeling of a two-degree-of-freedom (2-DOF) soft manipulator with control. A motion-capture instrument was used to collect the position coordinates under different pressures and loads, and the coordinates were imported into a gated recurrent unit (GRU) neural network model for training. The accuracy of the test set reached 98.87% when the hyperparameters were adjusted to the optimal network structure. Accordingly, a mapping function for the pressure and load at the end position was constructed. Experimental results showed that the proposed method could improve the control accuracy of the manipulator by approximately 6∼8 mm and significantly reduced the difficulty of control and modeling of a soft robot.