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Article: Image measurement errors in visual servoing: Estimating the induced positioning error
Title | Image measurement errors in visual servoing: Estimating the induced positioning error |
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Authors | |
Issue Date | 2010 |
Publisher | Springer |
Citation | Lecture Notes In Control And Information Sciences, 2010, v. 401, p. 151-167 How to Cite? |
Abstract | The goal of a visual servo system is to position a robot end-effector by progressively adjusting its location so that some object features in the current image match the same features in a desired image previously recorded. However, this matching in the image domain cannot be ensured due to unavoidable presence of image measurement errors, and even when it is realized, there is no guarantee that the robot end-effector has reached the desired location since the available image measurements are corrupted. The aim of this chapter is to present a strategy for bounding the worst-case robot positioning error introduced by image measurement errors. In particular, two methods are described, which allow one to compute upper and lower bounds of this positioning error. Some examples illustrate the proposed methods with synthetic and real data. © 2010 Springer-Verlag London. |
Persistent Identifier | http://hdl.handle.net/10722/155563 |
ISBN | |
ISSN | 2020 SCImago Journal Rankings: 0.173 |
References |
DC Field | Value | Language |
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dc.contributor.author | Chesi, G | en_US |
dc.contributor.author | Hung, YS | en_US |
dc.contributor.author | Yung, HL | en_US |
dc.date.accessioned | 2012-08-08T08:34:07Z | - |
dc.date.available | 2012-08-08T08:34:07Z | - |
dc.date.issued | 2010 | en_US |
dc.identifier.citation | Lecture Notes In Control And Information Sciences, 2010, v. 401, p. 151-167 | en_US |
dc.identifier.isbn | 9781849960885 | - |
dc.identifier.issn | 0170-8643 | en_US |
dc.identifier.uri | http://hdl.handle.net/10722/155563 | - |
dc.description.abstract | The goal of a visual servo system is to position a robot end-effector by progressively adjusting its location so that some object features in the current image match the same features in a desired image previously recorded. However, this matching in the image domain cannot be ensured due to unavoidable presence of image measurement errors, and even when it is realized, there is no guarantee that the robot end-effector has reached the desired location since the available image measurements are corrupted. The aim of this chapter is to present a strategy for bounding the worst-case robot positioning error introduced by image measurement errors. In particular, two methods are described, which allow one to compute upper and lower bounds of this positioning error. Some examples illustrate the proposed methods with synthetic and real data. © 2010 Springer-Verlag London. | en_US |
dc.language | eng | en_US |
dc.publisher | Springer | - |
dc.relation.ispartof | Lecture Notes in Control and Information Sciences | en_US |
dc.title | Image measurement errors in visual servoing: Estimating the induced positioning error | en_US |
dc.type | Article | en_US |
dc.identifier.email | Chesi, G:chesi@eee.hku.hk | en_US |
dc.identifier.email | Hung, YS:yshung@eee.hku.hk | en_US |
dc.identifier.authority | Chesi, G=rp00100 | en_US |
dc.identifier.authority | Hung, YS=rp00220 | en_US |
dc.description.nature | link_to_subscribed_fulltext | en_US |
dc.identifier.doi | 10.1007/978-1-84996-089-2_9 | en_US |
dc.identifier.scopus | eid_2-s2.0-77950211893 | en_US |
dc.identifier.hkuros | 176727 | - |
dc.relation.references | http://www.scopus.com/mlt/select.url?eid=2-s2.0-77950211893&selection=ref&src=s&origin=recordpage | en_US |
dc.identifier.volume | 401 | en_US |
dc.identifier.spage | 151 | en_US |
dc.identifier.epage | 167 | en_US |
dc.publisher.place | Berlin, Germany | en_US |
dc.identifier.scopusauthorid | Chesi, G=7006328614 | en_US |
dc.identifier.scopusauthorid | Hung, YS=8091656200 | en_US |
dc.identifier.scopusauthorid | Yung, HL=35172434900 | en_US |
dc.identifier.issnl | 0170-8643 | - |