Characterization and Circuit Design of Soft Bend Sensors for Use in Robotic Hands and Orthotics




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Smart materials such as shape memory alloy (SMA) and twisted and coiled polymer fishing line (TCPFL) are essential elements for the realization of novel smart robotic hands, orthotic hands, and prosthetic hands. These artificial muscle-actuated robotic hands need to be assessed extensively to understand the properties and efficiency of the designs. Cyclic movement of the fingers must be monitored to characterize the actuation frequency of the artificial muscles and the response due to the amplitude of stimuli. Flex sensors and strain gauges are commonly used to observe the bending action of robotic fingers by attaching them along the finger’s length. Such sensors are piezoresistive in nature and change their resistance due to stresses exerted on them which change the sensor’s dimensions. This piezoresistive property of some standard sensors available in the market is studied in this research to determine the angular position of the robotic finger during flexion or extension when the artificial muscles are triggered. A similar type of strain gauge sensor was designed in-house, which can be 3D printed and directly embedded into an orthotic finger. This sensor was fabricated using conductive and soft filament materials which consist of a composite of thermoplastic polyurethane (TPU) and carbon nanotubes (CNT). The purpose of this thesis is to characterize the sensor and design the optimized signal conditioning circuit of this sensor. A voltage divider circuit was utilized to characterize and understand the properties of the sensors. Various techniques including Wheatstone bridge, differential amplifiers, and active low pass filters have been implemented for designing the signal conditioning circuit of the strain gauge sensor, and several simulations results were obtained. This optimization converts the variable resistance of this strain gauge into a linearized voltage signal that is easier to monitor through common interfaces. In general, soft smart materials, actuators, and sensors can transform the existing robotic hands to achieve more elegant, lightweight, and modular designs.



Engineering, Robotics, Engineering, Biomedical