Enabling dexterous manipulation and safe human-robot interaction, soft robots are widely used in numerous surgical applications. One of the complications associated with using soft robots in surgical applications is reconstructing their shape and the external force exerted on them. Several sensor-based and model-based approaches have been proposed to address the issue. In this paper, a shape sensing technique based on Electrical Impedance Tomography (EIT) is proposed. The performance of this sensing technique in predicting the tip position and contact force of a soft bending actuator is highlighted by conducting a series of empirical tests. The predictions were performed based on a data-driven approach using a Long Short-Term Memory (LSTM) recurrent neural network. The tip position predictions indicate the importance of using EIT data along with pressure inputs. Changing the number of EIT channels, we evaluated the effect of the number of EIT inputs on the accuracy of the predictions. The least RMSE values for the tip position are 3.6 and 4.6 mm in Y and Z coordinates, respectively, which are 7.36% and 6.07% of the actuator's total range of motion. Contact force predictions were conducted in three different bending angles and by varying the number of EIT channels. The results of the predictions illustrated that increasing the number of channels contributes to higher accuracy of the force estimation. The mean errors of using 8 channels are 7.69%, 2.13%, and 2.96% of the total force range in three different bending angles.

翻译：为实现灵巧操作和安全人机交互，软体机器人已广泛应用于多项外科手术中。然而，软体机器人在外科应用中的主要挑战之一在于重构其形变状态及所受外部作用力。针对该问题，现有研究提出了多种基于传感器和基于模型的方法。本文提出一种基于电阻抗断层成像(EIT)的形状感知技术。通过系列实验验证了该感知技术在预测软体弯曲执行器末端位置与接触力方面的性能。预测基于数据驱动方法，采用长短期记忆(LSTM)循环神经网络实现。末端位置预测结果表明，将EIT数据与压力输入联合使用具有重要价值。通过改变EIT通道数量，我们评估了输入通道数对预测精度的影响。末端位置在Y轴与Z轴坐标的最小均方根误差分别为3.6毫米和4.6毫米，占执行器总运动范围的7.36%和6.07%。接触力预测实验在三种不同弯曲角度下进行，并动态调整EIT通道数。预测结果揭示：增加通道数可显著提升力估计精度。在三种弯曲角度下，使用8通道时的平均误差分别占力程范围的7.69%、2.13%和2.96%。