We present an approach for evaluating the design of Dielectric Elastomer (DE) capacitive pressure sensors on robotic graspers. This approach has used the ANSYS software for Finite Element Method (FEM), along with a MatLab script for calculation of capacitance change. The model has been set up with an axisymmetric indenter and frictionless contact. This study has compared several structured dielectric elastomer (DE) pressure sensors with different sub-surface soft padding thicknesses.
The results suggest that:
-For padding that is too thin the contact area will be small with localized compression and sensor sensitivity will be compromised by this;
-For padding that is very thick compared with the sensor thickness –deformation will be spread over a wider area and the signal sensitivity will be somewhat lower; for a given indenter radius of curvature;
-This suggests that there will be an optimal padding thickness for a given contact geometry.
The approach developed and presented in the paper will be helpful for sensor soft sensor design for different applications, such as robotics and bio-instrumentational systems, in particular, the design of graspers to identify and pick up different objects.
Multi touch sensors are widely used for screen interfaces, but are at an early stage of development for soft wearable technology and humanoid devices. We demonstrated a soft, flexible and stretchable tactile dielectric elastomer (DE) capacitive sensor array which is designed for multi-touch applications. The touch input is measured by the capacitance variation resulting from the deformation of the sensor modelled as a variable parallel plate capacitor. The flexibility and soft nature of capacitive DE sensor makes them comfortable to wear and versatile. This sensor module is composed of a 2-D capacitive sensor array composed of a grid of DE sensors. The sensor arrangement enables the measurement of touch capacitance on and between sensor centerlines. This technology has fewer connections with fewer wires and enables continuous location identification; convenient for emerging wearable technology as well as humanoid devices. It is possibility solution for wearable technology that needs to measure the reaction of forces in the human body; and can also be applicable to measure/control in humanoid devices to determine grasp ability to pick up an object.