Digital Twin Model of Underwater Construction Robot for Real-Time Grinding Simulation

EasyChair Preprint 13484

Abstract

This paper addresses the enhancement of remote operation for underwater construction robots through the development and integration of a digital twin. The digital twin model, developed using the recursive subsystem synthesis method, accurately predicts the robot's behavior in real-time. To address the challenge of simulating high-frequency contact forces during tasks like grinding and drilling, a deep neural network (DNN)-based meta-model was created to estimate tool forces. Experimental tests were conducted to collect data, including tool penetration, position, orientation, hydraulic actuator pressure, pressure differences in hydraulic motors, and IMU information of the cabin. This data was used to integrate the virtual tool force estimation model with the virtual robot model. Basic hydraulic motor models for the manipulator arm were also developed. The combined model's real-time simulation performance was evaluated, demonstrating the feasibility of accurate and efficient remote control in harsh underwater environments.

Keyphrases: Deep Neural Network, Digital Twin, cyber-physical system, real-time simulation, underwater robot

@booklet{EasyChair:13484,
  author    = {Je Hyeop Han and Jong-Boo Han and Sung-Soo Kim and Myoung-Ho Kim and Yong Hwan Kim and Hyeonbeen Lee and Jin-Gyun Kim and Tae-Kyeong Yeu},
  title     = {Digital Twin Model of Underwater Construction Robot for Real-Time Grinding Simulation},
  howpublished = {EasyChair Preprint 13484},
  year      = {EasyChair, 2024}}