Imperial College London
Alternate

ProfessorEtienneBurdet

Faculty of Engineering, Department of Bioengineering

Professor of Human Robotics
 
 
 
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Contact

 

e.burdet Website

 
 
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Location

 

419BSir Michael Uren HubWhite City Campus

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Summary

 

Publications

Citation

BibTex format

@article{Li:2020:10.1109/LRA.2020.2998715,
author = {Li, Y and Eden, J and Carboni, G and Burdet, E},
doi = {10.1109/LRA.2020.2998715},
journal = {IEEE Robotics and Automation Letters},
pages = {4399--4406},
title = {Improving tracking through human-robot sensory augmentation},
url = {http://dx.doi.org/10.1109/LRA.2020.2998715},
volume = {5},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - This letter introduces a sensory augmentation technique enabling a contact robot to understand its human user's control in real-time and integrate their reference trajectory information into its own sensory feedback to improve the tracking performance. The human's control is formulated as a feedback controller with unknown control gains and desired trajectory. An unscented Kalman filter is used to estimate first the control gains and then the desired trajectory. The estimated human's desired trajectory is used as augmented sensory information about the system and combined with the robot's measurement to estimate a reference trajectory. Simulations and an implementation on a robotic interface demonstrate that the reactive control can robustly identify the human user's control, and that the sensory augmentation improves the robot's tracking performance.
AU  - Li,Y
AU  - Eden,J
AU  - Carboni,G
AU  - Burdet,E
DO  - 10.1109/LRA.2020.2998715
EP  - 4406
PY  - 2020///
SN  - 2377-3766
SP  - 4399
TI  - Improving tracking through human-robot sensory augmentation
T2  - IEEE Robotics and Automation Letters
UR  - http://dx.doi.org/10.1109/LRA.2020.2998715
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000543054800003&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://ieeexplore.ieee.org/document/9103973
UR  - http://hdl.handle.net/10044/1/92330
VL  - 5
ER  -
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