File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Conference Paper: Human-robot Collaboration Using Variable Admittance Control And Human Intention Prediction

TitleHuman-robot Collaboration Using Variable Admittance Control And Human Intention Prediction
Authors
KeywordsDamping
Task analysis
Admittance
Collaboration
Trajectory
Issue Date2020
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1001095
Citation
The 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE), Virtual Conference, Hong Kong, 20-21 August 2020, p. 1116-1121 How to Cite?
AbstractDue to the difficulty of modeling human limb, it is very challenging to design the controller for human-robot collaboration. In this paper, we present a novel controller combining the variable admittance control and assistant control. In particular, the reinforcement learning is used to obtain the optimal damping value of the admittance controller by minimizing the reward function. In addition, we use the long short-term memory networks (LSTMs) to predict human intention based on the human limb dynamics and then an assistant controller is proposed to help human complete collaboration tasks. We validate the performance of our prediction algorithm and controller on a 7 d.o.f Franka Emika robot equipped with joint torque sensors. The proposed controller can both achieve minimum-jerk trajectory and low-effort cost.
DescriptionParallel Session - FrCT1: Foundations of Automation II - no. 25
Persistent Identifierhttp://hdl.handle.net/10722/284883
ISSN

 

DC FieldValueLanguage
dc.contributor.authorLu, W-
dc.contributor.authorHu, Z-
dc.contributor.authorPan, J-
dc.date.accessioned2020-08-07T09:03:53Z-
dc.date.available2020-08-07T09:03:53Z-
dc.date.issued2020-
dc.identifier.citationThe 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE), Virtual Conference, Hong Kong, 20-21 August 2020, p. 1116-1121-
dc.identifier.issn2161-8070-
dc.identifier.urihttp://hdl.handle.net/10722/284883-
dc.descriptionParallel Session - FrCT1: Foundations of Automation II - no. 25-
dc.description.abstractDue to the difficulty of modeling human limb, it is very challenging to design the controller for human-robot collaboration. In this paper, we present a novel controller combining the variable admittance control and assistant control. In particular, the reinforcement learning is used to obtain the optimal damping value of the admittance controller by minimizing the reward function. In addition, we use the long short-term memory networks (LSTMs) to predict human intention based on the human limb dynamics and then an assistant controller is proposed to help human complete collaboration tasks. We validate the performance of our prediction algorithm and controller on a 7 d.o.f Franka Emika robot equipped with joint torque sensors. The proposed controller can both achieve minimum-jerk trajectory and low-effort cost.-
dc.languageeng-
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1001095-
dc.relation.ispartofIEEE International Conference on Automation Science and Engineering (CASE) Proceedings-
dc.rightsIEEE International Conference on Automation Science and Engineering (CASE) Proceedings. Copyright © IEEE.-
dc.rights©2020 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.-
dc.subjectDamping-
dc.subjectTask analysis-
dc.subjectAdmittance-
dc.subjectCollaboration-
dc.subjectTrajectory-
dc.titleHuman-robot Collaboration Using Variable Admittance Control And Human Intention Prediction-
dc.typeConference_Paper-
dc.identifier.emailPan, J: jpan@cs.hku.hk-
dc.identifier.authorityPan, J=rp01984-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/CASE48305.2020.9217040-
dc.identifier.scopuseid_2-s2.0-85094153112-
dc.identifier.hkuros312217-
dc.identifier.spage1116-
dc.identifier.epage1121-
dc.publisher.placeUnited States-
dc.identifier.issnl2161-8070-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats