Purpose: Soft robots are highly flexible and adaptable instruments that have proven extremely useful, especially in the surgical environment where compliance allows for improved maneuverability throughout the body. Endoscopic devices are a primary example of an instrument that physicians use to navigate to difficult-to-reach areas inside the body. This paper presents a modular soft robotic pneumatic actuator as a proof of concept for a compliant endoscopic device.
Methods: The actuator is 3D printed using an FDM printer. Maximum bending angle is measured using image processing in MATLAB at a gauge pressure level of 35 psi. End-effector displacement is measured using electromagnetic tracking as gauge pressure ranges from 10 to 35 psi, and uniaxial tensile loading ranges from 0 to 120 g.
Results: The actuator achieves a maximum bending angle of 145°. Fourth-order polynomial regression is used to model the actuator displacement upon inflation and tensile loading with an average coefficient of correlation value of 0.998. We also develop a feedforward neural network as a robust computer-assisted method for controlling the actuator that achieves a coefficient of correlation value of 0.996.
Conclusion: We propose a novel modular soft robotic pneumatic actuator that is developed via rapid prototyping and evaluated using image processing and machine learning models. The curled resting shape allows for simple manufacturing and achieves a greater range of bending than other actuators of its kind. A feedforward neural network provides accurate prediction of end-effector displacement upon inflation and loading to deliver precise manipulation and control.
Keywords: Electromagnetic tracking; Image processing; Neural network; Soft robot.