Electronic Thesis and Dissertation Repository


Master of Engineering Science


Electrical and Computer Engineering


Dr. Rajni V. Patel


Reduced access size in minimally invasive surgery and therapy (MIST) poses several restriction on the design of the dexterous robotic instruments. The instruments should be developed that are slender enough to pass through the small sized incisions and able to effectively operate in a compact workspace. Most existing robotic instruments are operated by big actuators, located outside the patient’s body, that transfer forces to the end effector via cables or magnetically controlled actuation mechanism. These instruments are certainly far from optimal in terms of their cost and the space they require in operating room. The lack of adequate sensing technologies make it very challenging to measure bending of the flexible instruments, and to measure tool-tissue contact forces of the both flexible and rigid instruments during MIST. Therefore, it requires the development of the cost effective miniature actuators and strain/force sensors. Having several unique features such as bio-compatibility, low cost, light weight, large actuation forces and electrical resistivity variations, the shape memory alloys (SMAs) show promising applications both as the actuators and strain sensors in MIST. However, highly nonlinear hysteretic behavior of the SMAs hinders their use as actuators. To overcome this problem, an adaptive artificial neural network (ANN) based Preisach model and a model predictive controller have been developed in this thesis to precisely control the output of the SMA actuators. A novel ultra thin strain sensor is also designed using a superelastic SMA wire, which can be used to measure strain and forces for many surgical and intervention instruments. A da Vinci surgical instrument is sensorized with these sensors in order to validate their force sensing capability.

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