Macro Fiber Composite-Actuated Soft Robotic Fish: A Gray Box Model-Predictive Motion Planning Strategy Under Limited Actuation

This work experimentally investigates a model-predictive motion planning strategy to impose oscillatory and undulation movements in a macro fiber composite (MFC)-actuated robotic fish. Most of the results in this field exploit sinusoidal input signals at the resonance frequency, which reduces the device's maneuverability. Differently, this work uses body/caudal fin locomotion patterns as references in a motion planning strategy formulated as a model-based predictive control (MPC) scheme. This open-loop scheme requires the modeling of the device, which is accomplished by deriving a gray box state-space model using experimental modal data. This state-space model considers the electromechanical coupling of the actuators. Based on the references and the model, the MPC scheme derives the input signals for the MFC actuators. An experimental campaign is carried out to verify two references for mimicking the locomotion patterns of a fish under limited actuation. The experimental results confirm the motion planning scheme's capability to impose oscillatory and undulation movements.

Automated summary

This study investigates a model-predictive motion planning strategy for a robotic fish actuated by a macro fiber composite (MFC) to perform oscillatory and undulation movements. Unlike previous works, this study uses body/caudal fin locomotion patterns as references in a model-based predictive control (MPC) scheme, which is formulated based on the state-space model derived from experimental modal data considering the electromechanical coupling of the actuators. Experimental results confirm the capability of the motion planning scheme to impose oscillatory and undulation movements.