Modelling and implementation of soft bio-mimetic turtle using echo state network and soft pneumatic actuators

Abstract

Advances of soft robotics enabled better mimicking of biological creatures and closer realization of animals’ motion in the robotics field. The biological creature’s movement has morphology and flexibility that is problematic deportation to a bio-inspired robot. This paper aims to study the ability to mimic turtle motion using a soft pneumatic actuator (SPA) as a turtle flipper limb. SPA’s behavior is simulated using finite element analysis to design turtle flipper at 22 different geometrical configurations, and the simulations are conducted on a large pressure range (0.11–0.4 Mpa). The simulation results are validated using vision feedback with respect to varying the air pillow orientation angle. Consequently, four SPAs with different inclination angles are selected to build a bio-mimetic turtle, which is tested at two different driving configurations. The nonlinear dynamics of soft actuators, which is challenging to model the motion using traditional modeling techniques affect the turtle’s motion. Conclusively, according to kinematics behavior, the turtle motion path is modeled using the Echo State Network (ESN) method, one of the reservoir computing techniques. The ESN models the turtle path with respect to the actuators’ rotation motion angle with maximum root-mean-square error of $$1.04 \times 10^{-11}$$ 1.04 × 10 - 11 . The turtle is designed to enhance the robot interaction with living creatures by mimicking their limbs’ flexibility and the way of their motion.

Underwater Soft Robotics: A Review of Bioinspiration in Design, Actuation, Modeling, and Control

“…The data can then be used with different supervised learning, unsupervised learning, and reinforcement learning techniques to develop models and controllers for soft robots. Examples of commonly used techniques include feedforward neural network (FNN) [109][110][111][112], recurrent neural network (RNN) [113], convolutional neural network (CNN) [114], and echo state network (ESN) [109], based on the reservoir computing framework. FNNs are widely used for modeling soft systems.…”

“…An RNN used sensory data from cPDMS resistive sensors and a load cell to predict the deformation and force models of a soft pneumatic finger [113]. A reservoir computing approach with the ESN architecture was used to model the 2D motion of a bioinspired turtle actuated through soft pneumatic flippers [109].…”

Modeling of Soft Pneumatic Actuators with Different Orientation Angles Using Echo State Networks for Irregular Time Series Data

“…The yielding and buckling issues of the SPA's bending and their effects on the output force are also discussed in [23]. In [24], 4 SPAs (seen in Figure 2) with different orientation angles of 35 • , 60 • , 120 • , and 145 • were fabricated using Fused Deposition Modeling (FDM) 3D printing with the Felix Tec 4. In addition, CURA software is used for slicing the CAD design.…”

“…The two streams were calibrated using a calibration grid and synchronized to obtain the 3D position at each actuation step. Finally, the Tracker tool was used to annotate the videos and extract the ground truth positions, as described in [24]. The tool auto tracks the pixels containing the marker on the actuator's tip and extracts its coordinates for each frame of the video.…”

“…The FEA data was obtained using Ansys Multi-Physics 2019 R2 TM . The TPU material is identified using the Mooney-Rilivin five parameters material hyperelastic model and applied to the 22 SPAs with different θ angles [24].…”