A Method for Selecting the Next Best Angle-of-Approach for Touch-Based Identification of Beam Members in Truss Structures

Bykerk, L; Quin, P; Liu, D

A Method for Selecting the Next Best Angle-of-Approach for Touch-Based Identification of Beam Members in Truss Structures

Bykerk, L

Quin, P

Liu, D

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Publication Type:: Journal Article
Citation:: IEEE Sensors Journal, 2019, 19 (10), pp. 3939 - 3949
Issue Date:: 2019-05-15

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Field	Value	Language
dc.contributor.author	Bykerk, L https://orcid.org/0000-0002-2973-6164	en_US
dc.contributor.author	Quin, P https://orcid.org/0000-0001-6542-3459	en_US
dc.contributor.author	Liu, D https://orcid.org/0000-0002-1581-5582	en_US
dc.date.available	2021-05-18T19:02:30Z
dc.date.issued	2019-05-15	en_US
dc.identifier.citation	IEEE Sensors Journal, 2019, 19 (10), pp. 3939 - 3949	en_US
dc.identifier.issn	1530-437X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/133781
dc.description.abstract	© 2001-2012 IEEE. A robot designed to climb truss structures such as power transmission towers is expected to have an adequate tactile sensing in the grippers to identify a structural beam member and its properties. Depending on how a gripper grasps a structural member, defined as the Angle-of-Approach (AoA), the extracted tactile data can result in erroneous identifications due to the similarities in beam cross-sectional shapes and sizes. In these cases, further grasps at favorable Angles-of-Approach (AoAs) are required to correctly identify the beam member and its properties. This paper presents an information-based method which uses tactile data to determine the next best AoA for the identification of beam members in truss structures. The method is used in conjunction with a state estimate of beam shape, dimension, and AoA calculated by a Random Forest classifier. The method is verified through simulation by using the data collected using a soft gripper retrofitted with simple tactile sensors. The results show that this method can correctly identify a structural beam member and its properties with a small number of grasps (typically fewer than 4). This method can be applied to other adaptive robotic gripper designs fitted with suitable tactile sensors, regardless of the number of sensors used and their layout.	en_US
dc.relation.ispartof	IEEE Sensors Journal	en_US
dc.relation.isbasedon	10.1109/JSEN.2019.2896596	en_US
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject.classification	Analytical Chemistry	en_US
dc.title	A Method for Selecting the Next Best Angle-of-Approach for Touch-Based Identification of Beam Members in Truss Structures	en_US
dc.type	Journal Article
utslib.citation.volume	10	en_US
utslib.citation.volume	19	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	0913 Mechanical Engineering	en_US
utslib.for	0205 Optical Physics	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 Mechanical and Mechatronic Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
utslib.copyright.status	open_access	*
pubs.issue	10	en_US
pubs.publication-status	Published	en_US
pubs.volume	19	en_US

Abstract:

© 2001-2012 IEEE. A robot designed to climb truss structures such as power transmission towers is expected to have an adequate tactile sensing in the grippers to identify a structural beam member and its properties. Depending on how a gripper grasps a structural member, defined as the Angle-of-Approach (AoA), the extracted tactile data can result in erroneous identifications due to the similarities in beam cross-sectional shapes and sizes. In these cases, further grasps at favorable Angles-of-Approach (AoAs) are required to correctly identify the beam member and its properties. This paper presents an information-based method which uses tactile data to determine the next best AoA for the identification of beam members in truss structures. The method is used in conjunction with a state estimate of beam shape, dimension, and AoA calculated by a Random Forest classifier. The method is verified through simulation by using the data collected using a soft gripper retrofitted with simple tactile sensors. The results show that this method can correctly identify a structural beam member and its properties with a small number of grasps (typically fewer than 4). This method can be applied to other adaptive robotic gripper designs fitted with suitable tactile sensors, regardless of the number of sensors used and their layout.

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http://hdl.handle.net/10453/133781