Data-Driven Tracing and Directional Control Strategy for a Simulated Continuum Robot Within Anguilliform Locomotion

Biorobotics leverages the principles of natural locomotion to enhance the mobility of bioinspired aquatic robots. Among various swimming modes, anguilliform locomotion is particularly recognized as an energy-efficient mode incorporating complex multiphysics. Due to whole-body undulation, the determination of the anguilliform swimmer’s direction is not trivial. Furthermore, the neuromuscular mechanism that controls straight swimming is not fully understood. This study investigates the challenge of predicting and controling the gross motion trajectory of a soft robot that utilizes anguilliform swimming. The robot consists of a six-segment continuous body, where each segment is actuated with pneumatic artificial muscles. A mode extraction technique based on dynamic mode decomposition (DMD) is proposed to identify the robot’s future state. Using the complex-variable delay embedding (CDE) technique, the CDE DMD algorithm is developed to predict the robot trajectory trend. To vary the robot direction, a hypothesis that asymmetric sidewise actuation results in slightly different fluid velocities between the left and right sides of the robot was investigated using COMSOL Multiphysics® 6.2. The simulation results demonstrate the CDE DMD’s ability to predict gross motion across various scenarios. Furthermore, integrating the prediction model with the asymmetric actuation rule provides a control strategy for directional stability of the robot. Simulations of the closed-loop system with non-zero initial pose (step response) indicate the performance in maintaining straight-line swimming with approximately a 60s settling time.