A novel approach to compensate delay in communication by predicting teleoperator behaviour using deep learning and reinforcement learning to control telepresence robot
Fawad Naseer1,✉, Muhammad Nasir Khan1, Akhtar Rasool2, and Nafees Ayub3
1 Electrical Engineering Department, The University of Lahore, Lahore, Pakistan
2 Mechanical Engineering Department, Beijing Institute of Technology, Beijing, China
3 Computer Science Department, Government College University Faisalabad, Faisalabad, Pakistan
Email: fawadn.84@gmail.com
Robots with telepresence capabilities are typically employed for tasks where human presence is not feasible due to geography, safety risks like fire or radiation exposure, or other factors like any epidemic disease. Time delay is a significant consideration in controlling a telepresence robot. This study proposes a deep learning-based approach to compensate for the delay by predicting the behaviour of the teleoperator. We integrate a recurrent neural network (RNN) based on the Long Short-Term Memory (LSTM) architecture with the reinforcement learning-based Deep Deterministic Policy Gradient (DDPG) algorithm. The proposed method predicts the teleoperator’s angular and linear controlling commands by using data gathered by embedded sensors on the specially designed and built telepresence robot. Simulations and experiments assess the operation of the proposed technique in Gazebo simulation and MATLAB with ROS integration, which shows 2.3% better response in the presence of static and dynamic obstacles.
Introduction: Healthcare paramedical staff spends significant time and effort in the sensitive and critically vital field of patient interaction. Robotics has been employed extensively in the healthcare environment for at last few years. The Puma 560 robot performed neurosurgical biopsies in 1985, making it the first robotic surgery [1-3]. The market for surgical robots has grown remarkably in recent years and is expected to reach $15 billion by 2030, with a compound annual growth rate of 17.1%.
ICT and robotic solutions have been developed over the past decade and are legitimate support that could enable persons to survive freely. Robots like Double robot from Double robotics and Nao from Softbank are a few examples. Increasing attention towards telepresence robots has been seen recently in creating for treating patients. Before testing the robot in a real-life environment, several researchers conduct brief robot assessments in a lab setting. However, other works use focus groups or laboratory experiments to analyze the system [4-5]. The telepresence robot generalized system is shown in Figure 1.