A teleoperation framework for mobile robots based on shared control

Luo, Jing; Lin, Zhidong; Li, Yanan; Yang, Chenguang

doi:10.1109/lra.2019.2959442

A teleoperation framework for mobile robots based on shared control

Luo, Jing; Lin, Zhidong; Li, Yanan; Yang, Chenguang

Authors

Jing Luo

Zhidong Lin

Yanan Li

Charlie Yang Charlie.Yang@uwe.ac.uk
Professor in Robotics

Abstract

Mobile robots can complete a task in cooperation with a human partner. In this letter, a hybrid shared control method for a mobile robot with omnidirectional wheels is proposed. A human partner utilizes a six degrees of freedom haptic device and electromyography (EMG) signals sensor to control the mobile robot. A hybrid shared control approach based on EMG and artificial potential field is exploited to avoid obstacles according to the repulsive force and attractive force and to enhance the human perception of the remote environment based on force feedback of the mobile platform. This shared control method enables the human partner to tele-control the mobile robot's motion and achieve obstacles avoidance synchronously. Compared with conventional shared control methods, this proposed one provides a force feedback based on muscle activation and drives the human partners to update their control intention with predictability. Experimental results demonstrate the enhanced performance of the mobile robots in comparison with the methods in the literature.

Citation

Luo, J., Lin, Z., Li, Y., & Yang, C. (2020). A teleoperation framework for mobile robots based on shared control. IEEE Robotics and Automation Letters, 5(2), 377-384. https://doi.org/10.1109/lra.2019.2959442

Journal Article Type	Article
Acceptance Date	Dec 6, 2019
Online Publication Date	Dec 12, 2019
Publication Date	Apr 1, 2020
Deposit Date	Feb 12, 2020
Publicly Available Date	Feb 13, 2020
Journal	IEEE Robotics and Automation Letters
Print ISSN	2377-3766
Electronic ISSN	2377-3774
Publisher	Institute of Electrical and Electronics Engineers
Peer Reviewed	Peer Reviewed
Volume	5
Issue	2
Pages	377-384
DOI	https://doi.org/10.1109/lra.2019.2959442
Keywords	Artificial Intelligence; Computer Vision and Pattern Recognition; Computer Science Applications
Public URL	https://uwe-repository.worktribe.com/output/5376809

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