Hydrodynamic Modeling and Design of Robotic Fish using Slender Body Theory

Abstract

Design of bio-inspired robotic fish has been an important area of research for applications such as water quality monitoring, aquatic animal behavioral study and underwater exploration. The modeling of robotic fish requires high dimensional analysis, which increases the design complexity. To address this complexity, approximate models are preferred and frequently used in the design of robotic fishes. The accuracy of these models mainly relies on various hydrodynamic parameter identification of which drag coefficient estimation is highly challenging. This paper utilizes a simple deceleration motion model to estimate the drag coefficient effectively so that the linearization error is reduced. The non-linearity in the deceleration model is simplified and mapped as a linear model to predict the drag coefficient. The estimated drag coefficient is used for dynamic modeling of robotic fish using slender body theory. The dynamic model is validated through experimental setup using accelerometer and visual sensor data. The maximum swimming speed of 0.32 ms-1 is achieved at 1.2 Hz caudal fin oscillations.

Real-time implementation of deep reinforcement learning controller for speed tracking of robotic fish through data-assisted modeling

“…The mathematical model used in this work is similar to the work in. 28,43 The robotic fish has a frontal rigid and a rear link, as shown in Figure 4. The rear link oscillates symmetrically with respect to body axis b .…”