Artificial Intelligence Approach to the Trajectory Generation and Dynamics of a Soft Robotic Swallowing Simulator
Soft robotics is an area where the robots are designed by using soft and compliant modules which provide them with infinite degrees of freedom. The intrinsic movements and deformation of such robots are complex, continuous and highly compliant because of which the current modelling techniques are unable to predict and capture their dynamics. This paper describes a machine learning based actuation and system identification technique to discover the governing dynamics of a soft bodied swallowing robot. A neural based generator designed by using Matsuoka’s oscillator has been implemented to actuate the robot so that it can deliver its maximum potential. The parameters of the oscillator were found by defining and optimising a quadratic objective function. By using optical motion tracking, time-series data was captured and stored. Further, the data were processed and utilised to model the dynamics of the robot by assuming that few significant non-linearities are governing it. It has also been shown that the method can generalise the surface deformation of the time-varying actuation of the robot.
KeywordsSoft robotics Swallowing robot Peristalsis Matsuoka’s oscillator Machine learning Optimisation
The work presented in this paper was funded by Riddet Institute Centre of Research Excellence, New Zealand.
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