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I Robot Vision Robot Vision Edited by Aleš Ude In-Tech intechweb.org Published by In-Teh In-Teh Olajnica 19/2, 32000 Vukovar, Croatia Abstracting and non-prot use of the material is permitted with credit to the source. Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published articles. Publisher assumes no responsibility liability for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained inside. After this work has been published by the In-Teh, authors have the right to republish it, in whole or part, in any publication of which they are an author or editor, and the make other personal use of the work. © 2010 In-teh www.intechweb.org Additional copies can be obtained from: publication@intechweb.org First published March 2010 Printed in India Technical Editor: Martina Peric Cover designed by Dino Smrekar Robot Vision, Edited by Aleš Ude p. cm. ISBN 978-953-307-077-3 V Preface The purpose of robot vision is to enable robots to perceive the external world in order to perform a large range of tasks such as navigation, visual servoing for object tracking and manipulation, object recognition and categorization, surveillance, and higher-level decision- making. Among different perceptual modalities, vision is arguably the most important one. It is therefore an essential building block of a cognitive robot. Most of the initial research in robot vision has been industrially oriented and while this research is still ongoing, current works are more focused on enabling the robots to autonomously operate in natural environments that cannot be fully modeled and controlled. A long-term goal is to open new applications to robotics such as robotic home assistants, which can only come into existence if the robots are equipped with signicant cognitive capabilities. In pursuit of this goal, current research in robot vision benets from studies in human vision, which is still by far the most powerful existing vision system. It also emphasizes the role of active vision, which in case of humanoid robots does not limit itself to active eyes only any more, but rather employs the whole body of the humanoid robot to support visual perception. By combining these paradigms with modern advances in computer vision, especially with many of the recently developed statistical approaches, powerful new robot vision systems can be built. This book presents a snapshot of the wide variety of work in robot vision that is currently going on in different parts of the world. March 2010 Aleš Ude VII Contents Preface V 1. Designandfabricationofsoftzoomlensappliedinrobotvision 001 Wei-ChengLin,Chao-ChangA.Chen,Kuo-ChengHuangandYi-ShinWang 2. MethodsforReliableRobotVisionwithaDioptricSystem 013 E.MartínezandA.P.delPobil 3. AnApproachforOptimalDesignofRobotVisionSystems 021 KanglinXu 4. VisualMotionAnalysisfor3DRobotNavigationinDynamicEnvironments 037 ChunrongYuanandHanspeterA.Mallot 5. AVisualNavigationStrategyBasedonInversePerspectiveTransformation 061 FranciscoBonin-Font,AlbertoOrtizandGabrielOliver 6. Vision-basedNavigationUsinganAssociativeMemory 085 MateusMendes 7. VisionBasedRoboticNavigation:ApplicationtoOrthopedicSurgery 111 P.Gamage,S.Q.Xie,P.DelmasandW.L.Xu 8. NavigationandControlofMobileRobotUsingSensorFusion 129 YongLiu 9. VisualNavigationforMobileRobots 143 NilsAxelAndersen,JensChristianAndersen,EnisBayramoğluandOleRavn 10. Interactiveobjectlearningandrecognitionwithmulticlass supportvectormachines 169 AlešUde 11. RecognizingHumanGaitTypes 183 PrebenFihlandThomasB.Moeslund 12. EnvironmentRecognitionSystemforBipedRobotWalkingUsingVision BasedSensorFusion 209 Tae-KooKang,Hee-JunSongandGwi-TaePark VIII 13. NonContact2Dand3DShapeRecognitionbyVisionSystem forRoboticPrehension 231 BikashBepari,RanjitRayandSubhasisBhaumik 14. ImageStabilizationinActiveRobotVision 261 AngelosAmanatiadis,AntoniosGasteratos,SteliosPapadakisandVassilisKaburlasos 15. Real-timeStereoVisionApplications 275 ChristosGeorgoulas,GeorgiosCh.SirakoulisandIoannisAndreadis 16. Robotvisionusing3DTOFsystems 293 StephanHussmannandTorstenEdeler 17. CalibrationofNon-SVPHyperbolicCatadioptricRoboticVisionSystems 307 BernardoCunha,JoséAzevedoandNunoLau 18. ComputationalModeling,Visualization,andControlof2-Dand3-DGrasping underRollingContacts 325 SuguruArimoto,MorioYoshidaandMasahiroSekimoto1 19. TowardsRealTimeDataReductionandFeatureAbstractionforRoboticsVision 345 RafaelB.Gomes,RenatoQ.Gardiman,LuizE.C.Leite, BrunoM.CarvalhoandLuizM.G.Gonçalves 20. LSCICPrecoderforImageandVideoCompression 363 MuhammadKamran,ShiFengandWangYiZhuo 21. Theroboticvisualinformationprocessingsystembasedonwavelet transformationandphotoelectrichybrid 373 DAIShi-jieandHUANG-He 22. Directvisualservoingofplanarmanipulatorsusingmomentsofplanartargets 403 EusebioBugarinandRafaelKelly 23. Industrialrobotmanipulatorguardingusingarticialvision 429 FeveryBrecht,WynsBart,BoullartLucLlataGarcíaJoséRamón andTorreFerreroCarlos 24. RemoteRobotVisionControlofaFlexibleManufacturingCell 455 SilviaAnton,FlorinDanielAntonandTheodorBorangiu 25. Network-basedVisionGuidanceofRobotforRemoteQualityControl 479 Yongjin(James)Kwon,RichardChiou,BillTsengandTeresaWu 26. RobotVisioninIndustrialAssemblyandQualityControlProcesses 501 NikoHerakovic 27. TestingStereoscopicVisioninRobotTeleguide 535 SalvatoreLivatino,GiovanniMuscatoandChristinaKoeffel 28. EmbeddedSystemforBiometricIdentication 557 AhmadNasirCheRosli IX 29. Multi-TaskActive-VisioninRobotics 583 J.Cabrera,D.Hernandez,A.DominguezandE.Fernandez 30. AnApproachtoPerceptionEnhancementinRobotizedSurgery usingComputerVision 597 AgustínA.Navarro,AlbertHernansanz,JoanArandaandAlíciaCasals [...]... arbitrary reference coordinate frame A By inverting this transformation, we can obtain c1 T a , where c1 r 11 c1 r12 c1 r13 c1 t x c1 r 21 c1 r22 c1 r23 c1 ty c1 T a = c1 (23) r 31 c1 r32 c1 r33 c1 tz 0 0 0 1 For convenience, let c1 c1 c1 R1 R2 R1 = = = (c1 r 11 (c1 r 21 (c1 r 31 c1 c1 c1 r12 c1 r22 c1 r32 c1 r13 ) (24) r23 ) (25) r33 ) (26) ... i i o u · k1 + ( p u × o u ) · k2 i i i +1 i +1 i +1 u u u a i +1 · k1 + ( p i +1 × a i +1 ) · k2 i i An Approach for Optimal Design of Robot Vision Systems i M2 i M3 i M4 = = = 25 n u +1 · k2 n i u · k2 o i i +1 u a i +1 · k2 i n u +1 · k3 n i u · k3 o i +1 i u a i +1 · k3 i u u ( p i +1 × n i +1 ) · k3 i ( p u × o u ) · k3 i +1 i +1 i u u ( p i +1 × a i +1 ) · k3 i u u... where ni +1 , oi +1 , ai +1 and pi +1 are four 3 × 1 vectors of matrix Ui +1 which is defined as Ui = Ai Ai +1 · · · A N with U N +1 = I Using the above equations, the manipulator’s differential changes with respect to the base can be represented as dn do da dp dT N = (19 ) 0 0 0 1 where dn = do = da = dp = u N u N o1 δz − a1 δy u N u N − n1 δz + a1 δ x u N u N n1 δy − o1 δ x u u u n1 dx N + o1 dy N + a1 dz N... on optical properties 10 :1 has larger n and ν than 15 :1 In this research, the n 1. 395 and ν 50 is used Table .1 shows the comparison with PDMS and the other most used optical materials Materials BK7 PMMA COC PC PS Refractive index (n) 1. 517 1. 492 1. 533 1. 585 1. 590 Abbe number (vd) 64 .17 57.442 56.23 29. 91 30.87 Table 1 Optical property of PDMS and common optical material PDMS 1. 395 50 3 Methodology... image has been verified for its feasibility of zoom effect 7.0 1. 5 6.0 EFL variation[mm EFL variation[mm 1. 75 1. 25 1 0.75 0.5 0.25 0 0.000 0.005 0. 010 0. 015 Pressure[MPa] 10 0℃ 60min (a) 10 0°C PDMS flat lens 0.020 5.0 4.0 3.0 2.0 1. 0 0.0 0.000 0.005 0. 010 0. 015 0.020 Pressure[MPa] 10 0℃ 60min 10 0℃ 70min (b) 10 0°C PDMS spherical lens Fig 15 The relationship of applied pneumatic pressure and variation... focal length with PDMS lens cured at 10 0°C Design and fabrication of soft zoom lens applied in robot vision 20.000 Zoom ratio[%] 10 .000 Zoom ratio[%] 9 7.500 5.000 2.500 15 .000 10 .000 5.000 0.000 0.000 0.000 0.005 0. 010 0. 015 0.000 0.020 0.005 0. 010 0. 015 0.020 Pressure[MPa] Pressure[MPa] 10 0℃ 60min 10 0℃ 60min (a) PDMS flat lens 10 0℃ 70min (b) PDMS spherical lens Fig 11 The relationship of applied pneumatic... (Wu, 19 84), we obtain dN δN = M1 M2 dθ + M2 0 dr + M3 0 dd + M4 M3 dα (18 ) where dθ dr dd dα = = = = (d 1 dθ2 dθ N )t (dr1 dr2 dr N )t (dd1 dd2 dd N )t (d 1 dα2 dα N )t and M1 , M2 , M3 and M4 are all 3 × N matrices whose components are the function of N joint variables The ith column of M1 , M2 , M3 and M4 can be expressed as follows: i M1 = u u n u +1 · k1 + ( p i +1 × n i +1 ) ·... D 412 98a , the process parameter separate as 10 :1 and 15 :1, cured at 10 0 °C for 30, 45, 60 minutes and 15 0 °C for 15 , 20, 25 minutes As the result, in the same mixed ratio, higher curing temperature and long Design and fabrication of soft zoom lens applied in robot vision 3 curing time cause larger Young’s Modulus; in the same curing parameter, in mixed ratio 10 :1 has larger Young’s Modulus than 15 :1. .. 978 -15 63474552 Zhu, Z.; Karuppiah, D.R.; Riseman, E.M & Hanson, A.R (2004) Keeping Smart, Omnidirectional Eyes on You [Adaptive Panoramic StereoVision] IEEE Robotics and Automation Magazine – Special Issue on Panoramic Robotics, Vol 11 , No 4, (December 2004), page numbers (69–78), ISSN: 10 70-9932 An Approach for Optimal Design of Robot Vision Systems 21 3 0 An Approach for Optimal Design of Robot Vision. .. 0 −cθi sαi −cθi cαi 0 ∂Ai (10 ) = 0 cαi −sαi 0 ∂αi 0 0 0 0 0 0 0 cθi 0 0 0 sθi ∂Ai (11 ) = 0 0 0 0 ∂ri 0 0 0 0 0 0 0 0 0 0 0 0 ∂Ai (12 ) = 0 0 0 1 ∂di 0 0 0 0 Substituting equations (8), (9), (10 ), (11 ) and (12 ) into (7), we obtain 0 −cαi dθi sαi dθi dri cαi dθi 0 −dαi u δAi = −sαi dθi dαi 0 v 0 0 0 0 (13 ) 24 Robot Vision where u = ri cαi dθi + sαi . 0 0.25 0.5 0.75 1 1.25 1. 5 1. 75 0.000 0.005 0. 010 0. 015 0.020 Pressure[MPa] EFL variation[mm 10 0 ℃ 60min 0.0 1. 0 2.0 3.0 4.0 5.0 6.0 7.0 0.000 0.005 0. 010 0. 015 0.020 Pressure[MPa] EFL variation[mm 10 0 ℃ . BK7 PMMA COC PC PS PDMS Refractive index (n) 1. 517 1. 492 1. 533 1. 585 1. 590 1. 395 Abbe number (v d ) 64 .17 57.442 56.23 29. 91 30.87 50 Table. 1. Optical property of PDMS and common optical. Designandfabricationofsoftzoomlensappliedin robot vision 3 curing time cause larger Young’s Modulus; in the same curing parameter, in mixed ratio 10 :1 has larger Young’s Modulus than 15 :1. The mixed ratio 15 :1 is soft then 10 :1 but
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