Roboteye technology for thermal target tracking using predictive control

Singh, AM; Ha, Q; Wood, DK; Bishop, M; Nguyen, Q; Wong, A

Roboteye technology for thermal target tracking using predictive control

Singh, AM Ha, Q

Wood, DK Bishop, M Nguyen, Q Wong, A

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Publication Type:: Conference Proceeding
Citation:: ISARC 2018 - 35th International Symposium on Automation and Robotics in Construction and International AEC/FM Hackathon: The Future of Building Things, 2018
Issue Date:: 2018-01-01

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Field	Value	Language
dc.contributor.author	Singh, AM	en_US
dc.contributor.author	Ha, Q https://orcid.org/0000-0003-0978-1758	en_US
dc.contributor.author	Wood, DK	en_US
dc.contributor.author	Bishop, M	en_US
dc.contributor.author	Nguyen, Q	en_US
dc.contributor.author	Wong, A	en_US
dc.date.issued	2018-01-01	en_US
dc.identifier.citation	ISARC 2018 - 35th International Symposium on Automation and Robotics in Construction and International AEC/FM Hackathon: The Future of Building Things, 2018	en_US
dc.identifier.uri	http://hdl.handle.net/10453/132299
dc.description.abstract	© ISARC 2018 - 35th International Symposium on Automation and Robotics in Construction and International AEC/FM Hackathon: The Future of Building Things. All rights reserved. Thermal cameras are widely used in the fatigue analysis of mechanical structures using the thermoelastic effect. Nevertheless, such analysis is hampered due to blurry images resulting from the motion of structure-under-test. To address the issue this paper presents a system that utilizes robotic vision and predictive control. The system comprises of a thermal camera, a vision camera, a RobotEye, and a fiducial detection system. A marker is attached to a thermal target in order to estimate its position and orientation using the proposed detection system. To predict the future position of the thermal moving object, a Kalman filter is used. Finally, the Model Predictive Control (MPC) approach is applied to generate commands for the robot to follow the target. Results of the tracking by MPC are included in this paper along with the performance evaluation of the whole system. The evaluation clearly shows the improvement in the tracking performance of the development for thermal structural analysis.	en_US
dc.relation.ispartof	ISARC 2018 - 35th International Symposium on Automation and Robotics in Construction and International AEC/FM Hackathon: The Future of Building Things	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Roboteye technology for thermal target tracking using predictive control	en_US
dc.type	Conference Proceeding
utslib.for	0913 Mechanical Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CBI - Centre for Built Infrastructure
utslib.copyright.status	open_access	*
pubs.publication-status	Published	en_US

Abstract:

© ISARC 2018 - 35th International Symposium on Automation and Robotics in Construction and International AEC/FM Hackathon: The Future of Building Things. All rights reserved. Thermal cameras are widely used in the fatigue analysis of mechanical structures using the thermoelastic effect. Nevertheless, such analysis is hampered due to blurry images resulting from the motion of structure-under-test. To address the issue this paper presents a system that utilizes robotic vision and predictive control. The system comprises of a thermal camera, a vision camera, a RobotEye, and a fiducial detection system. A marker is attached to a thermal target in order to estimate its position and orientation using the proposed detection system. To predict the future position of the thermal moving object, a Kalman filter is used. Finally, the Model Predictive Control (MPC) approach is applied to generate commands for the robot to follow the target. Results of the tracking by MPC are included in this paper along with the performance evaluation of the whole system. The evaluation clearly shows the improvement in the tracking performance of the development for thermal structural analysis.

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http://hdl.handle.net/10453/132299