Carbon particle‐filled polymers are frequently used as stretchable conductors and strain sensors. Many of the proposed resistance‐based stretchable strain sensors show non‐monotonic strain response, especially under dynamic conditions. This is commonly attributed to the competing destruction and reformation of the conductive network, but the underlying mechanism is still unknown. Therefore, systematic cyclic tensile tests are performed with carbon black‐filled silicone yarns, which show that the non‐monotonic behavior found depends on strain rate, strain history, and maximum applied strain. Based on the experimental results, a novel model is developed that incorporates the visco‐elastic nature of the polymer and representations of the percolative networks in strain direction as well as the transversal direction. It is shown that the non‐monotonic behavior is a result of the combination of visco‐elasticity and transversal contraction of the percolative network. The model is an equivalent circuit model and its simulation results are in good agreement with the experimental results. The model can be used to further understand the strain sensing behavior of conductive polymers and optimize sensor systems.

The concept of merging pre-processed textile materials with tailored mechanical properties into soft matrices is so far rarely used in the field of soft robotics. The herein presented work takes the advantages of textile materials in elastomer matrices to another level by integrating a material with highly anisotropic bending properties. A pre-fabricated textile material consisting of oriented carbon fibers is used as a stiff component to precisely control the mechanical behavior of the robotic setup. The presented robotic concept uses a multi-layer stack for the robot’s body and dielectric elastomer actuators (DEAs) on both outer sides of it. The bending motion of the whole structure results from the combination of its mechanically adjusted properties and the force generation of the DEAs. We present an antagonistic switching setup for the DEAs that leads to deflections to both sides of the robot, following a biomimetic principle. To investigate the bending behavior of the robot, we show a simulation model utilizing electromechanical coupling to estimate the quasi-static deflection of the structure. Based on this model, a statement about the bending behavior of the structure in general is made, leading to an expected maximum deflection of 10 mm at the end of the fin for a static activation. Furthermore, we present an electromechanical network model to evaluate the frequency dependent behavior of the robot’s movement, predicting a resonance frequency of 6.385 Hz for the dynamic switching case. Both models in combination lead to a prediction about the acting behavior of the robot. These theoretical predictions are underpinned by dynamic performance measurements in air for different switching frequencies of the DEAs, leading to a maximum deflection of 9.3 mm located at the end of the actuators. The herein presented work places special focus on the mechanical resonance frequency of the robotic setup with regard to maximum deflections.

For soft robotics, shape memory alloy (SMA)‐based elastomeric actuators are a promising material combination but their maximum stroke is limited by the small inherent contraction of SMAs. In this work, a textile‐reinforced soft actuator is presented, which has additional SMA wire length included in the textile structure as well as a sensoric textile to track the actuator's pose. This strategy eliminates the need for external SMA wires with extra mechanical components. Various experiments with different excitation voltages are performed to show the actuator's performance. In a horizontal setup, the soft actuator reaches a bending angle of 270° at a power input of 18 W. The integrated sensor reflects the actuator's position but is also influenced by the temperature increase during activation. Moreover, an equivalent circuit model is proposed that includes the actuator, sensor, and mechanical support structure in one model. The model incorporates not only the mechanical but also the thermal and electrical domains. The simulation results are in good agreement with the experimental results.

Recently, there has been remarkable progress in the development of smart textiles, especially knitted strain sensors, to achieve reliable sensor signals. Stable and reliable electro-mechanical properties of sensors are essential for using textile-based sensors in medical applications. However, the challenges associated with significant hysteresis and low gauge factor (GF) values remain for using strain sensors for motion capture. To evaluate these issues, a comprehensive investigation of the cyclic electro-mechanical properties of weft-knitted strain sensors was conducted in the present study to develop a drift-free elastic strain sensor with a robust sensor signal for motion capture for medical devices. Several variables are considered in the study, including the variation of the basic knit pattern, the incorporation of the electrically conductive yarn, and the size of the strain sensor. The effectiveness and feasibility of the developed knitted strain sensors are demonstrated through an experimental evaluation, by determining the gauge factor, its nonlinearity, hysteresis, and drift. The developed knitted piezoresistive strain sensors have a GF of 2.4, a calculated drift of 50%, 12.5% hysteresis, and 0.3% nonlinearity in parts.

This paper presents the manufacturing process and electromechanical characterization of highly stretchable textile strain sensors. These sensors can be used in self-sensing soft robots or motion tracking. To improve linearity and compliance of silver-plated yarns, the braiding technology is used to fabricate both resistive and capacitive strain sensors. Braiding is a highly productive and automated process and the resulting sensor properties can be improved. Moreover, the braids can be further processed to complex textiles. In the fabrication process different base materials are used and the braiding density as well as the composition of the conductive threads in the braid are varied. With the produced samples electro-mechanical characterization experiments are carried out to analyze the sensors’ properties, e.g. gauge factor, linearity, hysteresis and compliance. The experimental results are correlated with the material and process parameters. Both, braiding density and base material, have an enormous influence on the sensor characteristics. The most promising sensor type was integrated into a soft actuator. This demonstrates the feasibility of such textile sensors to provide self-sensing capabilities and proprioception to soft robots.

Soft actuators are a promising option for the advancing fields of human-machine interaction and dexterous robots in complex environments. Shape memory alloy wire actuators can be integrated into fiber rubber composites for highly deformable structures. For autonomous, closed-loop control of such systems, additional integrated sensors are necessary. In this work, a soft actuator is presented that incorporates fiber-based actuators and sensors to monitor both deformation and temperature. The soft actuator showed considerable deformation around two solid body joints, which was then compared to the sensor signals, and their correlation was analyzed. Both, the actuator as well as the sensor materials were processed by braiding and tailored fiber placement before molding with silicone rubber. Finally, the novel fiber-rubber composite material was used to implement closed-loop control of the actuator with a maximum error of 0.5°.