Continuous role adaptation for human-robot shared control

Li, Yanan, Tee, Keng Peng, Chan, Wei Liang, Yan, Rui, Chua, Yuanwei and Limbu, Dilip Kumar (2015) Continuous role adaptation for human-robot shared control. IEEE Transactions on Robotics, 31 (3). pp. 672-681. ISSN 1552-3098

[img] PDF - Accepted Version
Download (828kB)

Abstract

In this paper, we propose a role adaptation method for human-robot shared control. Game theory is employed for fundamental analysis of this two-agent system. An adaptation law is developed such that the robot is able to adjust its own role according to the human’s intention to lead or follow, which is inferred through the measured interaction force. In the absence of human interaction forces, the adaptive scheme allows the robot to take the lead and complete the task by itself. On the other hand, when the human persistently exerts strong forces that signal an unambiguous intent to lead, the robot yields and becomes the follower. Additionally, the full spectrum of mixed roles between these extreme scenarios is afforded by continuous online update of the control that is shared between both agents. Theoretical analysis shows that the resulting shared control is optimal with respect to a two-agent coordination game. Experimental results illustrate better overall performance, in terms of both error and effort, compared to fixed-role interactions.

Item Type: Article
Additional Information: (c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Schools and Departments: School of Engineering and Informatics > Engineering and Design
Depositing User: Yanan Li
Date Deposited: 15 Dec 2017 10:51
Last Modified: 15 Dec 2017 10:51
URI: http://sro.sussex.ac.uk/id/eprint/72080

View download statistics for this item

📧 Request an update