Learning Based Exteroception of Soft Underwater Manipulator With Soft Actuator Network

Abstract

Interactions with environmental objects can induce substantial alterations in both exteroceptive and proprioceptive signals. However, the deployment of exteroceptive sensors within underwater soft manipulators encounters numerous challenges and constraints, thereby imposing limitations on their perception capabilities. In this article, we present a novel learning-based exteroceptive approach that utilizes internal proprioceptive signals and harnesses the principles of soft actuator network (SAN). Deformation and vibration resulting from external collisions tend to propagate through the SANs in underwater soft manipulators and can be detected by proprioceptive sensors. We extract features from the sensor signals and develop a fully-connected neural network (FCNN)-based classifier to determine collision positions. We have constructed a training dataset and an independent validation dataset for the purpose of training and validating the classifier. The experimental results affirm that the proposed method can identify collision locations with an accuracy level of 97.11% using the independent validation dataset, which exhibits potential applications within the domain of underwater soft robotics perception and control.