Soft Robots’ Dynamic Posture Perception Using Kirigami-Inspired Flexible Sensors with Porous Structures and Long Short-Term Memory (LSTM) Neural Networks

Abstract

Soft robots can create complicated structures and functions for rehabilitation. The posture perception of soft actuators is critical for performing closed-loop control for a precise location. It is essential to have a sensor with both soft and flexible characteristics that does not affect the movement of a soft actuator. This paper presents a novel end-to-end posture perception method that employs flexible sensors with kirigami-inspired structures and long short-term memory (LSTM) neural networks. The sensors were developed with conductive sponge materials. With one-step calibration from the sensor output, the posture of the soft actuator could be calculated by the LSTM network. The method was validated by attaching the developed sensors to a soft fiber-reinforced bending actuator. The results showed the accuracy of posture prediction of sponge sensors with three kirigami-inspired structures ranged from 0.91 to 0.97 in terms of R2. The sponge sensors only generated a resistive torque value of 0.96 mNm at the maximum bending position when attached to a soft actuator, which would minimize the effect on actuator movement. The kirigami-inspired flexible sponge sensor could in future enhance soft robotic development.

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.…”

“…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.…”

“…Soft sensors may exhibit highly nonlinearity, hysteresis, and longterm drift of sensing signals, which pose significant challenges to calibration algorithms. [5,6,8] The main causes of sensor output signal drifting bias are polymer degradation and viscoelasticity of the material. [9,10] If not calibrated properly, the drift in the sensor signal can adversely affect the controller's performance, leading to a failure to achieve the desired optimal performance.…”

“…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.…”

Effects of Slit Edge Notches on Mechanical Properties of 3D-Printed PA12 Nylon Kirigami Specimens

“…In recent years, kirigami has emerged as a promising design paradigm in engineering, with its ability to enhance the stretching performance of materials by transferring stretching to bending (i.e., buffering-by-buckling) [ 3 ]. The resulting kirigami-inspired structures have found applications in various fields, including intelligent robotics [ 4 , 5 , 6 , 7 , 8 ], smart sensors [ 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ], energy absorption structures [ 17 , 18 , 19 ], biomedical implants [ 20 ], deployable solar panels [ 21 ], and stretchable nanogenerators [ 22 ]. The use of kirigami in engineering designs reflects its potential to enable the creation of complex, multi-functional structures that can adapt to different environments and stimuli, with potential applications in a wide range of fields, including robotics, biomedicine, energy harvesting, and sensing.…”