Application of Kalman filter in track prediction of shuttlecock
This paper deals with the application of Kalman filter for optimizing and filtering the position signal of shuttlecock obtained by the vision servo system of 'shuttlecock robot'. Non-uniform mass distribution and air resistance effect can make much noise not only in vision recognition but...
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| creator | Man Yongkui Zhao Liang Hu Jingxin |
| description | This paper deals with the application of Kalman filter for optimizing and filtering the position signal of shuttlecock obtained by the vision servo system of 'shuttlecock robot'. Non-uniform mass distribution and air resistance effect can make much noise not only in vision recognition but also in kinematic model analysis of shuttlecock. The Kalman filter algorithm is used to filter the shuttlecock position signal by taking the error of measurement and the error of shuttlecock motion model into account. Besides, by considering the requirement of fast moving control, we reduce dimensions of state vector by decomposition of shuttlecock motion to shorten the executive cycle. The simulation results show its affectivity on improving the accuracy of track prediction. It can also accomplish track prediction fast and accurately when applied on `Shuttlecock Robot'. |
| doi_str_mv | 10.1109/ROBIO.2009.5420475 |
| format | Conference Proceeding |
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Non-uniform mass distribution and air resistance effect can make much noise not only in vision recognition but also in kinematic model analysis of shuttlecock. The Kalman filter algorithm is used to filter the shuttlecock position signal by taking the error of measurement and the error of shuttlecock motion model into account. Besides, by considering the requirement of fast moving control, we reduce dimensions of state vector by decomposition of shuttlecock motion to shorten the executive cycle. The simulation results show its affectivity on improving the accuracy of track prediction. It can also accomplish track prediction fast and accurately when applied on `Shuttlecock Robot'.</description><identifier>ISBN: 9781424447749</identifier><identifier>ISBN: 1424447747</identifier><identifier>EISBN: 9781424447756</identifier><identifier>EISBN: 1424447755</identifier><identifier>DOI: 10.1109/ROBIO.2009.5420475</identifier><identifier>LCCN: 2009905293</identifier><language>eng</language><publisher>IEEE</publisher><subject>air resistance ; Electrical resistance measurement ; Filtering ; Filters ; Kalman filter ; Kinematics ; least squares ; Machine vision ; Motion control ; Motion measurement ; Position measurement ; Robot vision systems ; Servomechanisms ; Shuttlecock Robot</subject><ispartof>2009 IEEE International Conference on Robotics and Biomimetics (ROBIO), 2009, p.2205-2210</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5420475$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,27925,54920</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5420475$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Man Yongkui</creatorcontrib><creatorcontrib>Zhao Liang</creatorcontrib><creatorcontrib>Hu Jingxin</creatorcontrib><title>Application of Kalman filter in track prediction of shuttlecock</title><title>2009 IEEE International Conference on Robotics and Biomimetics (ROBIO)</title><addtitle>ROBIO</addtitle><description>This paper deals with the application of Kalman filter for optimizing and filtering the position signal of shuttlecock obtained by the vision servo system of 'shuttlecock robot'. Non-uniform mass distribution and air resistance effect can make much noise not only in vision recognition but also in kinematic model analysis of shuttlecock. The Kalman filter algorithm is used to filter the shuttlecock position signal by taking the error of measurement and the error of shuttlecock motion model into account. Besides, by considering the requirement of fast moving control, we reduce dimensions of state vector by decomposition of shuttlecock motion to shorten the executive cycle. The simulation results show its affectivity on improving the accuracy of track prediction. It can also accomplish track prediction fast and accurately when applied on `Shuttlecock Robot'.</description><subject>air resistance</subject><subject>Electrical resistance measurement</subject><subject>Filtering</subject><subject>Filters</subject><subject>Kalman filter</subject><subject>Kinematics</subject><subject>least squares</subject><subject>Machine vision</subject><subject>Motion control</subject><subject>Motion measurement</subject><subject>Position measurement</subject><subject>Robot vision systems</subject><subject>Servomechanisms</subject><subject>Shuttlecock Robot</subject><isbn>9781424447749</isbn><isbn>1424447747</isbn><isbn>9781424447756</isbn><isbn>1424447755</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2009</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpVkM1Kw0AUhUekoNa8gG7mBVLvzdzJZFZSiz_FQkB0XSbzg2PTJCTjwre3Yl14NocDH4fDYewKYYEI-ualvlvXiwJALyQVQEqesEyrCqkgIqVkefovk56xix9cgyy0OGPZNH3AQSQFIp6z2-UwtNGaFPuO94E_m3ZvOh5im_zIY8fTaOyOD6N30f5B0_tnSq23vd1dslkw7eSzo8_Z28P96-op39SP69Vyk0dUMuWmtKLRjXXCBU_oDCIJoUqtoEAt0ZUNeB3gMDpoB8qGSjXCWZLGQtUEMWfXv73Re78dxrg349f2-IH4BudsTW8</recordid><startdate>200912</startdate><enddate>200912</enddate><creator>Man Yongkui</creator><creator>Zhao Liang</creator><creator>Hu Jingxin</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200912</creationdate><title>Application of Kalman filter in track prediction of shuttlecock</title><author>Man Yongkui ; Zhao Liang ; Hu Jingxin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-a6c3b9bcd3dfe41da114337697021951d6b0e9f0444f9d07cf87b3dc45ac08bf3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2009</creationdate><topic>air resistance</topic><topic>Electrical resistance measurement</topic><topic>Filtering</topic><topic>Filters</topic><topic>Kalman filter</topic><topic>Kinematics</topic><topic>least squares</topic><topic>Machine vision</topic><topic>Motion control</topic><topic>Motion measurement</topic><topic>Position measurement</topic><topic>Robot vision systems</topic><topic>Servomechanisms</topic><topic>Shuttlecock Robot</topic><toplevel>online_resources</toplevel><creatorcontrib>Man Yongkui</creatorcontrib><creatorcontrib>Zhao Liang</creatorcontrib><creatorcontrib>Hu Jingxin</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Man Yongkui</au><au>Zhao Liang</au><au>Hu Jingxin</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Application of Kalman filter in track prediction of shuttlecock</atitle><btitle>2009 IEEE International Conference on Robotics and Biomimetics (ROBIO)</btitle><stitle>ROBIO</stitle><date>2009-12</date><risdate>2009</risdate><spage>2205</spage><epage>2210</epage><pages>2205-2210</pages><isbn>9781424447749</isbn><isbn>1424447747</isbn><eisbn>9781424447756</eisbn><eisbn>1424447755</eisbn><abstract>This paper deals with the application of Kalman filter for optimizing and filtering the position signal of shuttlecock obtained by the vision servo system of 'shuttlecock robot'. Non-uniform mass distribution and air resistance effect can make much noise not only in vision recognition but also in kinematic model analysis of shuttlecock. The Kalman filter algorithm is used to filter the shuttlecock position signal by taking the error of measurement and the error of shuttlecock motion model into account. Besides, by considering the requirement of fast moving control, we reduce dimensions of state vector by decomposition of shuttlecock motion to shorten the executive cycle. The simulation results show its affectivity on improving the accuracy of track prediction. It can also accomplish track prediction fast and accurately when applied on `Shuttlecock Robot'.</abstract><pub>IEEE</pub><doi>10.1109/ROBIO.2009.5420475</doi><tpages>6</tpages></addata></record> |
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| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | air resistance Electrical resistance measurement Filtering Filters Kalman filter Kinematics least squares Machine vision Motion control Motion measurement Position measurement Robot vision systems Servomechanisms Shuttlecock Robot |
| title | Application of Kalman filter in track prediction of shuttlecock |
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