Soft material structures exhibit high deformability and conformability which can be useful for many engineering applications such as robots adapting to unstructured and dynamic environments. However, the fact that they have almost infinite degrees of freedom challenges conventional sensory systems and sensorization approaches due to the difficulties in adapting to soft structure deformations.
In this paper, we address this challenge by proposing a novel method which designs flexible sensor morphologies to sense soft material deformations by using a functional material called conductive thermoplastic elastomer (CTPE). This model-based design method, called Strain Vector Aided Sensorization of Soft Structures (SVAS), provides a simulation platform which analyzes soft body deformations and automatically finds suitable locations for CTPE-based strain gauge sensors to gather strain information which best characterizes the deformation.
Our chosen sensor material CTPE exhibits a set of unique behaviors in terms of strain length electrical conductivity, elasticity, and shape adaptability, allowing us to flexibly design sensor morphology that can best capture strain distributions in a given soft structure. We evaluate the performance of our approach by both simulated and real-world experiments and discuss the potential and limitations.
CONDUCTIVE THERMOPLASTIC ELASTOMER FOR STRAIN SENSING
Thermoplasticity comes into play when custom shaped sensors need to be fabricated for complex surfaces, while elasticity allows the sensors to undergo high deformations. Figure 2a shows that when the hybrid sensor material is extracted from the high shear mixer, it can be fed to warm presses or heated extruders to fabricate variable sizes and centro-symmetric shapes such as fibers, tubes and sheets.
SVAS DESIGN METHOD
Figure 3b shows the example block as fixed to the ground on both short ends (green rectangles), and a block of force is applied in the positive y direction (purple prism). Depending on the material properties of the soft structure and the mechanical stimulus range, VoxCad calculates the final posture of the object as shown in Figure 3c with a color coding where lighter colors represent higher magnitudes of positive strain.
In order to place the sensor fibers accurately on the guidelines with no slack, we used steel pins as anchor points on the silicone and attached these sensors to the silicone block surfaces with a high elasticity transparent silicone glue (Dow Corning 732). Figure 5a shows the integration process for the second experiment set where CTPE-based sensors are stretched over the rectangular silicone block by anchor pins and then attached to the surface with the silicone paste.
For the twisting, a clamp is attached to fix one end of the silicone block. The other end is attached directly to a servo motor shaft to generate torque. Total amount of twist angle is measured with an angle compass and values are mapped into torque. The forces and torques are applied continuously with an increase of 1 N and 1 Nmm every 0.5 s. In all setups, CTPE sensors are connected to a simple voltage divider circuit, whose output is processed with an Arduino Due® microprocessor. Figure 6 shows setups for both experiment sets.
Figure 9a shows snapshots from the final morphology of sensors in experimental setups. The end points of the sensors are then connected to a simple voltage divider circuit, whose output is processed with an Arduino Due® microprocessing unit.
In this paper we have proposed a novel approach named SVAS which designs flexible sensor morphologies by using the strain information generated in soft deformations. In this context, our method involves simulation tools to model soft structures and deformations to extract necessary strain information to construct sensor morphology designs. We have chosen a carbon black/thermoplastic elastomer material (CTPE) to model and generate strain gauge sensors with linear sensitivity response characteristics.
The current state of our method models fibrous strain gauge sensors and uses extracted strain information to design custom pathways for these fibers to follow. In order to show the scalability of our approach to various soft structure materials and applications, we have performed simulations and experiments to discriminate complex behaviors. We have generated two sensor morphologies by using our method to discriminate three postures on various shapes of silicone blocks due to bending, twisting and pushing deformations.
To validate the efficacy of our approach and sensor performance estimations, we have casted different shaped blocks out of silicone, fabricated fiber shaped CTPE sensors and integrated them following the morphology designs generated by the simulations. By comparing the simulation and experimental results, we confirm that the proposed approach is able to discriminate the three motion patterns with tunable performance. We also proposed the application of this method in other research fields by showing an example case on gloves to discriminate American Hand Sign Language based “E”, “T” and “H” letters.
With respect to the current state of our approach, we used sensor locations suggested by our simulation method and experimentally applied the sensor morphologies based on the simulation results. The experiments showed successful discrimination results as well as the potential of the use of our approach for more complex applications. Overall, we showed that our approach can design sensor morphologies by simulating soft deformations and estimate sensor performances which are validated by following experiments.
Such a sensor design approach can have an impact on sensor morphology for detecting complex behaviors and postures for soft continuum bodied structures. The usage of CTPE as a material for the fabrication of strain gauge sensors also supports this idea, as many different morphologies can be created and easily integrated into soft structures.
Authors: Utku Culha | Surya G. Nurzaman | Frank Clemens | Fumiya Iida