An End-to-End Dynamic Posture Perception Method for Soft Actuators Based on Distributed Thin Flexible Porous Piezoresistive Sensors

Abstract

This paper proposes a method for accurate 3D posture sensing of the soft actuators, which could be applied to the closed-loop control of soft robots. To achieve this, the method employs an array of miniaturized sponge resistive materials along the soft actuator, which uses long short-term memory (LSTM) neural networks to solve the end-to-end 3D posture for the soft actuators. The method takes into account the hysteresis of the soft robot and non-linear sensing signals from the flexible bending sensors. The proposed approach uses a flexible bending sensor made from a thin layer of conductive sponge material designed for posture sensing. The LSTM network is used to model the posture of the soft actuator. The effectiveness of the method has been demonstrated on a finger-size 3 degree of freedom (DOF) pneumatic bellow-shaped actuator, with nine flexible sponge resistive sensors placed on the soft actuator’s outer surface. The sensor-characterizing results show that the maximum bending torque of the sensor installed on the actuator is 4.7 Nm, which has an insignificant impact on the actuator motion based on the working space test of the actuator. Moreover, the sensors exhibit a relatively low error rate in predicting the actuator tip position, with error percentages of 0.37%, 2.38%, and 1.58% along the x-, y-, and z-axes, respectively. This work is expected to contribute to the advancement of soft robot dynamic posture perception by using thin sponge sensors and LSTM or other machine learning methods for control.

Drift‐Aware Feature Learning Based on Autoencoder Preprocessing for Soft Sensors

“…To achieve a convincing visualization in the temporal dimension, the time-variant estimation results and R 2 of representative neural networks are illustrated in Figure 8. The estimation of For soft sensors, a saturation region exists where the sensor signals exhibit insensitivity to changes in the measurement output, [5][6][7] as depicted in Figure 9. As indicated by the pinkhighlighted area, the saturation introduces a discrepancy between the estimation results and the actual extended length, resulting in a deflection with a span of 50 mm in the R 2 graph.…”

“…Following established practices in neural network research, [7,38] we trained different sets of hyperparameters to achieve optimal performance. This involved fine-tuning the number of hidden layers, the number of hidden states, and the dropout layer rate.…”

“…For soft sensors, a saturation region exists where the sensor signals exhibit insensitivity to changes in the measurement output, [ 5–7 ] as depicted in Figure . As indicated by the pink‐highlighted area, the saturation introduces a discrepancy between the estimation results and the actual extended length, resulting in a deflection with a span of 50 mm in the graph.…”

“…The development of soft sensors has garnered significant attention in the field of soft robotics due to their compliance with the robot body and potential for miniaturization. Piezoresistive sensors [1][2][3][4][5][6][7] are widely used in soft robots for proprioception and exteroception. However, the accuracy of sensor measurement data is crucial for the soft robot's perception and control.…”