UC Santa Cruz

Soft robotics as a new and emerging field has the potential to become an integral part in the area of robotics by providing new methods of interacting with environments not capable through conventional robotics. However, soft robot's nonlinear nature can be difficult to both control and model. This paper will present several examples of modern soft robotic applications and solutions. A novel construction method for embedded, intrinsically soft sensors as a part of a more significant actuator system will be shown. This was accomplished by employing the use of low cost components and tools including a spin coater and a vacuum chamber. This actuator was made using both Ecoflex 00-30 and Dragonskin 10 silicone rubber with the goal being to maximize sensor responsitivity without compromising useful grip strength. The sensor is composed entirely of an ionic liquid whose fluidic nature makes it intrinsically soft and an ideal sensor medium for soft robots. The ionic liquid is captured inside of continuous sensor channels that are molded into the layers of the actuator. These channels are sampled by taking advantage of the change in impedance of the liquid as its cross sectional area and length increases or decreases. To be able to accurately measure this change without breaking down the ionic fluid, the sensors are integrated as a resistor into a relaxation oscillator where the output frequency is driven by the change in impedance of the sensor. The equation Ps = Rs/(3.891*10^8) describes this relationship where Ps is the output period of a square wave and Rs is the impedance of the sensor. The actuator was integrated into a matching mount system that was designed to securely hold the actuator and provide strain relief for the 4 wires and single air supply tube. These wires and tube connect the actuator to its support system, shown in figure 0.1, which is necessary to both sample the integrated sensors on and control the actuator. The actuator system is evaluated from the viewpoint of both sensor responsitivity and sensitivity to changes in input pressure. Results will be shown and discussed that demonstrate the effectiveness of both the bend and force sensors. The complete actuator system was then integrated into a feedback control loop, which allowed the actuator to track a ramp reference signal. This was made possible through the characterization of the actuator which resulted in a 4th order polynomial representing the relationship between input pressure and bend sensor output. Finally, suggestions will be made for further research to increase the overall usefulness of this system in an effort to increase the cumulative research impact of this design.