A Robot Learning Method with Physiological Interface for Teleoperation Systems

Jing Luo,Hang Su,Chenguang Yang,Chao Liu

doi:10.3390/app9102099

Abstract

The human operator largely relies on the perception of remote environmental conditions to make timely and correct decisions in a prescribed task when the robot is teleoperated in a remote place. However, due to the unknown and dynamic working environments, the manipulator’s performance and efficiency of the human-robot interaction in the tasks may degrade significantly. In this study, a novel method of human-centric interaction, through a physiological interface was presented to capture the information details of the remote operation environments. Simultaneously, in order to relieve workload of the human operator and to improve efficiency of the teleoperation system, an updated regression method was proposed to build up a nonlinear model of demonstration for the prescribed task. Considering that the demonstration data were of various lengths, dynamic time warping algorithm was employed first to synchronize the data over time before proceeding with other steps. The novelty of this method lies in the fact that both the task-specific information and the muscle parameters from the human operator have been taken into account in a single task; therefore, a more natural and safer interaction between the human and the robot could be achieved. The feasibility of the proposed method was demonstrated by experimental results.

Highlights

With significant progress in computer science, information science, automation, and artificial intelligence techniques, telerobots have been extensively applied in areas as diverse as telemedicine [1,2], telerehabilitation [3], minimally invasive surgery [4], disaster rescue and relief operation [5], maintenance and exploration in deep sea or out space [6,7], surgeon training [8], and telemanufacture [9], etc
The results indicated that robot learning method could significantly improve the efficiency of the teleoperation and reduce the operational pressure for the human operator
This module is mainly used to enable the remote Baxter robot to execute the task according to the learned task trajectories and learned muscle stiffness

Summary

Introduction

With significant progress in computer science, information science, automation, and artificial intelligence techniques, telerobots have been extensively applied in areas as diverse as telemedicine [1,2], telerehabilitation [3], minimally invasive surgery [4], disaster rescue and relief operation [5], maintenance and exploration in deep sea or out space [6,7], surgeon training [8], and telemanufacture [9], etc. Telerobots provide an alternative interactive way between the human operator and the teleoperation in order to enhance perception and motion ability of the human beings [10,11]. It is the integration of human intelligence and the robot’s advantages under the constraint of long distance [12,13]. The performance of teleoperation largely depends on the perception of remote environmental conditions. Many achievements indicated that some related algorithms such as impedance control, virtual fixture, and shared control, could further improve the performance of teleoperation. According to literature [14], a switched-impedance control algorithm was presented

Methods

Results

Discussion

Conclusion