Machine Learning Assisted Locomotion Tracking of Dielectric Elastomer Actuators Based Soft Water Robot

Locomotion tracking of a soft water robot that is propelled and steerable by dielectric elastomer actuators (DEAs), is proposed and demonstrated using a hybrid machine learning model. Measuring at 6 cm x 17 cm x 5 cm and weighing 28 g, this soft water robot can achieve a speed of 3 cm/s, equivalent to 50% of its body length per second (BL/s), while consuming approximately 1.0 W of power. Its buoyancy is enabled by the hexagonal honeycomb base made up of silicone. The DEA actuators are constructed from conductive carbon grease as the electrodes on the elastomer. The steerable soft water robot can complete a full 90° turn within 18 seconds. A comprehensive finite element analysis (FEA) is conducted on the actuators and the soft water robot to provide an in-depth study of the soft robot's performance including actuation force, travel distance, actuation angle, and steering disposition. For precise locomotion tracking, a novel hybrid machine learning model is developed, comprising two multi-layer perceptron branches in a single in-house model. Two additional bypass layers are added for steering behavior classification. The model attains a real- time accuracy of 98% for both the water robot's displacement and steering behavior prediction.