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Deep Learning Based Real-Time Computation of Thrust for a Robotic Fish

Abstract : In the motion planning of a fish-inspired robot, the computation of thrust due to the fin movement is an essential step. As thrust determination is a two-way coupling problem, computational fluid dynamics (CFD) -based approach is often used for obtaining accurate estimates. CFD simulations are computationally intensive operation and cannot be performed online during motion planning. This paper reports a deep learning model for determining thrust acting on a robotic fish wherein the training data is generated by offline CFD simulations. The model takes a list of input variables such as robot geometry, motion parameters, orientation and surrounding conditions and determines the thrust acting on the robot for the given conditions. We also report validation of the thrust determined via the developed model with respect to CFD simulation results and physical experimental data reported in the literature. The developed approach reduces the computation memory utilized in the process and even lowers the computation time by a factor of about 50, with appreciable accuracy in results. We envisage that the developed approach can be used for the computation of motion primitives in model predictive planning framework and thereby can allow online dynamically feasible trajectory planning of fish-inspired robots.