GuLiM: A Hybrid Motion Mapping Technique for Teleoperation of Medical Assistive Robot in Combating the COVID-19 Pandemic

Honghao Lv; Depeng Kong; Gaoyang Pang; Baicun Wang; Zhangwei Yu; Zhibo Pang; Geng Yang

doi:10.1109/TMRB.2022.3146621

GuLiM: A Hybrid Motion Mapping Technique for Teleoperation of Medical Assistive Robot in Combating the COVID-19 Pandemic

IEEE Trans Med Robot Bionics. 2022 Jan 26;4(1):106-117. doi: 10.1109/TMRB.2022.3146621. eCollection 2022 Feb.

Authors

Honghao Lv^{1

2}, Depeng Kong¹, Gaoyang Pang³, Baicun Wang¹, Zhangwei Yu⁴, Zhibo Pang^{5

6}, Geng Yang¹

Affiliations

¹ State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical EngineeringZhejiang University Hangzhou 310027 China.
² School of Electrical Engineering and Computer ScienceKTH Royal Institute of Technology 11758 Stockholm Sweden.
³ School of Electrical and Information EngineeringThe University of Sydney Sydney NSW 2006 Australia.
⁴ Hangzhou Institute of Advanced Studies, Zhejiang Normal University Hangzhou 321017 China.
⁵ Department of Automation TechnologyABB Corporate Research Sweden 72178 Vasteras Sweden.
⁶ Department of Intelligent SystemsKTH Royal Institute of Technology 11758 Stockholm Sweden.

Abstract

Driven by the demand to largely mitigate nosocomial infection problems in combating the coronavirus disease 2019 (COVID-19) pandemic, the trend of developing technologies for teleoperation of medical assistive robots is emerging. However, traditional teleoperation of robots requires professional training and sophisticated manipulation, imposing a burden on healthcare workers, taking a long time to deploy, and conflicting the urgent demand for a timely and effective response to the pandemic. This paper presents a novel motion synchronization method enabled by the hybrid mapping technique of hand gesture and upper-limb motion (GuLiM). It tackles a limitation that the existing motion mapping scheme has to be customized according to the kinematic configuration of operators. The operator awakes the robot from any initial pose state without extra calibration procedure, thereby reducing operational complexity and relieving unnecessary pre-training, making it user-friendly for healthcare workers to master teleoperation skills. Experimenting with robotic grasping tasks verifies the outperformance of the proposed GuLiM method compared with the traditional direct mapping method. Moreover, a field investigation of GuLiM illustrates its potential for the teleoperation of medical assistive robots in the isolation ward as the Second Body of healthcare workers for telehealthcare, avoiding exposure of healthcare workers to the COVID-19.

Keywords: COVID-19; HCPS; Hybrid motion mapping; healthcare 40; medical assistive robot.

Grants and funding

This work was supported in part by the National Natural Science Foundation of China under Grant 51975513 and Grant 51890884; in part by the Natural Science Foundation of Zhejiang Province under Grant LR20E050003; in part by the Major Research Plan of Ningbo Innovation 2025 under Grant 2020Z022; and in part by the Zhejiang University Special Scientific Research Fund for COVID-19 Prevention and Control under Grant 2020XGZX017. The work of Honghao Lv was supported by the China Scholarship Council. The work of Zhibo Pang was supported in part by the Swedish Foundation for Strategic Research (SSF) under Project APR20-0023.