Human Inspired Dexterity in Robotic Manipulation PDF
Preview Human Inspired Dexterity in Robotic Manipulation
HUMAN INSPIRED DEXTERITY IN ROBOTIC MANIPULATION HUMAN INSPIRED DEXTERITY IN ROBOTIC MANIPULATION Edited by TETSUYOU WATANABE KENSUKE HARADA MITSUNORI TADA AcademicPressisanimprintofElsevier 125LondonWall,LondonEC2Y5AS,UnitedKingdom 525BStreet,Suite1650,SanDiego,CA92101,UnitedStates 50HampshireStreet,5thFloor,Cambridge,MA02139,UnitedStates TheBoulevard,LangfordLane,Kidlington,OxfordOX51GB,UnitedKingdom ©2018ElsevierInc.Allrightsreserved. Nopartofthispublicationmaybereproducedortransmittedinanyformorbyanymeans, electronicormechanical,includingphotocopying,recording,oranyinformationstorageand retrievalsystem,withoutpermissioninwritingfromthepublisher.Detailsonhowtoseek permission,furtherinformationaboutthePublisher’spermissionspoliciesandourarrangements withorganizationssuchastheCopyrightClearanceCenterandtheCopyrightLicensingAgency, canbefoundatourwebsite:www.elsevier.com/permissions. ThisbookandtheindividualcontributionscontainedinitareprotectedundercopyrightbythePublisher (otherthanasmaybenotedherein). Notices Knowledgeandbestpracticeinthisfieldareconstantlychanging.Asnewresearchandexperience broadenourunderstanding,changesinresearchmethods,professionalpractices,ormedicaltreatment maybecomenecessary. Practitionersandresearchersmustalwaysrelyontheirownexperienceandknowledgeinevaluatingand usinganyinformation,methods,compounds,orexperimentsdescribedherein.Inusingsuchinformation ormethodstheyshouldbemindfuloftheirownsafetyandthesafetyofothers,includingpartiesfor whomtheyhaveaprofessionalresponsibility. Tothefullestextentofthelaw,neitherthePublishernortheauthors,contributors,oreditors,assumeany liabilityforanyinjuryand/ordamagetopersonsorpropertyasamatterofproductsliability,negligenceor otherwise,orfromanyuseoroperationofanymethods,products,instructions,orideascontainedinthe materialherein. LibraryofCongressCataloging-in-PublicationData AcatalogrecordforthisbookisavailablefromtheLibraryofCongress BritishLibraryCataloguing-in-PublicationData AcataloguerecordforthisbookisavailablefromtheBritishLibrary ISBN978-0-12-813385-9 ForinformationonallAcademicPresspublicationsvisitour websiteathttps://www.elsevier.com/books-and-journals Publisher:MaraConner AcquisitionEditor:SonniniR.Yura EditorialProjectManager:ThomasVanDerPloeg ProductionProjectManager:VijayBharath CoverDesigner:MatthewLimbert TypesetbySPiGlobal,India CONTRIBUTORS JumpeiArata DepartmentofMechanicalEngineering,FacultyofEngineering,KyushuUniversity, Fukuoka,Japan Ji-HunBae RoboticsR&DGroup,KoreaInstituteofIndustrialTechnology,Cheonan,SouthKorea QiushiFu MechanicalandAerospaceEngineering,UniversityofCentralFlorida,Orlando, FL,UnitedStates IgorGoncharenko CollegeofInformationScienceandEngineering,RitsumeikanUniversity,Kusatsu,Japan KensukeHarada GraduateSchoolofEngineeringScience,OsakaUniversity,Toyonaka,Japan AkihiroKawamura DepartmentofAdvancedInformationTechnology,KyushuUniversity,Fukuoka,Japan MakikoKouchi HumanInformaticsResearchInstitute,NationalInstituteofAdvancedIndustrialScienceand Technology,Tokyo,Japan VictorKryssanov CollegeofInformationScienceandEngineering,RitsumeikanUniversity,Kusatsu,Japan RyutaOzawa DepartmentofMechanicalEngineeringInformatics,MeijiUniversity,Kawasaki,Japan MarcoSantello SchoolofBiologicalandHealthSystemsEngineering,ArizonaStateUniversity,Tempe,AZ, UnitedStates MikhailSvinin CollegeofInformationScienceandEngineering,RitsumeikanUniversity,Kusatsu,Japan MitsunoriTada HumanInformaticsResearchInstitute,NationalInstituteofAdvancedIndustrialScienceand Technology,Tokyo,Japan KenjiTahara DepartmentofMechanicalEngineering,KyushuUniversity,Fukuoka,Japan TetsuyouWatanabe FacultyofMechanicalEngineering,InstituteofScienceandEngineering,Kanazawa University,Kanazawa,Japan ix x Contributors ZheXu DepartmentofMechanicalEngineering,YaleUniversity,NewHaven,CT,UnitedStates MotojiYamamoto DepartmentofMechanicalEngineering,KyushuUniversity,Fukuoka,Japan CHAPTER 1 Background: Dexterity in Robotic Manipulation by Imitating Human Beings Tetsuyou Watanabe FacultyofMechanicalEngineering,InstituteofScienceandEngineering, KanazawaUniversity,Kanazawa,Japan Contents 1.1 Background 1 1.2 ComplementalInformation 3 1.2.1 StatisticallySignificantDifference 3 1.2.2 StateSpaceRepresentation 4 1.2.3 MechanicalImpedance 5 1.2.4 FundamentalGraspingStyle 5 1.2.5 KinematicsandStaticsofRobots 6 1.2.6 DynamicsofRobots 6 References 7 1.1 BACKGROUND Humanbeingshavebeenakindoftextbookforconstructingrobotsbecause robots perform the tasks that humans do in the support of human beings. However, there are a lot of unrevealed things about human beings. Even if knowledge is given, how to utilize that knowledge during the develop- ment of robots is on a case by case basis and there are no general method- ologies to effectively transfer the knowledge during the development process. It is impossible to deal with all aspects of human beings. This text is focused on dexterity and aims to understand how human beings acquire dexterityinobjectmanipulation,discussesthepossibilityofitsapplicationin roboticsystems,anddrawskeystrategiesfordealingwithroboticdexterous manipulation in the next generation. Here,wearefocusedonup-to-dateresearchabouthumanfunctionsand the method for transferring the functions to robotic manipulation. HumanInspiredDexterityinRoboticManipulation ©2018ElsevierInc. 1 https://doi.org/10.1016/B978-0-12-813385-9.00001-7 Allrightsreserved. 2 HumanInspiredDexterityinRoboticManipulation Human functions: The level of dexterous manipulation by robots is cur- rently far from that of human beings. What can improve the ability of robots? One hint might be to understand the approach that human beings takeindexterityacquisition.Forexample,supposethereisataskofgrasping and manipulating an unknown object. The shape, softness, weight, and gravitational center are unknown parameters of the presented object. Howdohumanbeingsidentify thephysicalparametersandusetheidenti- fiedparameterstocompletedexterousmanipulation?Howdohumanbeings learn such procedures? The methodology for the identifying and learning processofhumanbeingscouldprovidevaluableinsightsfortheconstruction ofdexterousmanipulationofrobots.Thestructureofthehumanfingerand handplayanimportantrolefordexterousmanipulation.Itwasacquiredin the process of evolution. The key structures for dexterity are also valid for the key structures of robotic hand design. Methodfor transferring humanfunctions torobotic manipulation:It isbasically difficulttotransferhumanmanipulationtechniquesorfunctionstorobotic manipulation, because of the different structures and system architectures. Oneapproachmightbeimitatingorembeddingkeyfunction/components inrobotsonebyone.Recentattemptstotransferthetechniquesorfunctions could give us deep insights in realizing dexterous manipulation of robots. Such attempts could proceed to creating new strategies to design, control, andplanforroboticmanipulations,althoughthefunctionsdonotperfectly coincide with the ones for human beings. This text will consolidate recent approaches from both viewpoints in acceleratingthenextdevelopmentsinthedexterousmanipulationofrobots. Tofacilitatethisunderstanding,therearetwoseparatesectionscorrespond- ing to the two viewpoints. The first section focuses on human functions while the second section focuses on transferring the functions to robots. Chapter 2 provides recent revelations in hand anatomy, which lead to human functions for dexterous manipulation. Newly discovered functions giveusnewviewpointsforconstructingroboticmanipulation.Theconcept of muscle synergy has been utilized for controlling robotic hands, but the synergydoesnotalwayscorrespondtothatforhumanoranimalevolution. Chapter 3 presents how human beings learn dexterous manipulation in the context of sensorimotor functions. The actual functions of human beings give us different insights to understand dexterous manipulation. Chapter4presentsatrailofexcitationofamultisensoryillusionofasurgical robotic system to enhance the dexterity of the control of surgical robotic systems.Ahintoftheembeddedfeaturesofhumanbehaviorsinthesystem Background:DexterityinRoboticManipulationbyImitatingHumanBeings 3 can be obtained. Chapter 5 examines human reaching behavior when manipulating parallel flexible objects and shows that the optimal hand tra- jectory is composed of a fifth order polynomial (as in the classic minimum jerk model) and trigonometric terms depending on the natural frequencies of the system and time movement. Thesecondsectionprovidesrecentresultsofdesign(Chapters6and7), control (Chapters 8 and 9), and planning (Chapter 10) for dexterous robotic manipulation while considering human functions. Chapter 6 pre- sentsanovelanthropomorphicrobotichanddesign,imitatingthesalientfea- tures of the human hand. Chapter 7 presents a novel fingertip design imitatingthestructureofhumanfingers,andarobotichandequippedwith thefingertip.Bothdesignsgivehintsforconstructingrobotichandsutilizing humanhandfeatures.Basedonthehumanfunctionofthumbopposability, Chapter 8 presents a control schema utilizing the concept of passivity. Chapter9presentsthecontrollerwithconsideringthedifferenceofsensing timing between visual sensors (low sampling rate) and joint sensors (high samplingrate).Chapter10presentsaplanningmethodologytomanipulate objects with two arms like human beings. 1.2 COMPLEMENTAL INFORMATION To make it easier to understand, complemental information is provided here. 1.2.1 Statistically Significant Difference Whenexaminingthedifferencesbetweentwogroups,statisticalhypothesis testingisused.BothgroupsaresupposedtoberepresentedbytheStudent’s t-distribution. The testing outputs the P-value (probability value), which indicates the probability for the null hypothesis that each element of the two groups belongs to the same distribution. If the P-value is less than thegivenlevelofsignificance(forexample,0.05or5%),thenullhypothesis is rejected, and it can be said that there is a statically significant difference between the two groups. This analysis is valid for the two groups. If the numberofthetargetgroupsismorethantwo,theposthoctestisperformed for the analysis. Table 1.1 shows a summary of which test should be per- formed in each case. For details, please see text books on statistics, for example [1]. 4 HumanInspiredDexterityinRoboticManipulation Table1.1 Whichstatisticaltestshouldbeperformedineachcase Numberof Assumptionof Assumption Nameof Target elementsin Distributionin ofvariancein test comparison eachgroup eachgroup eachgroup T-test Twogroups No limitation Nothing Nothing Tukey- Pairwise No limitation Normal Homogeneity Kramer differences Bonferoni/ Pairwise Same Normal Homogeneity Dunn differences Scheffe No limitation No limitation Nothing Nothing 1.2.2 State Space Representation State space representation is conducted when modeling a system as a first- order differential equation of the input (u), output (y), and state (x). If the system is linear, the state and observation equations are respectively represented by x_ ¼Ax+Bu (1.1) y5Cx+Du whereA,B,C,Darethematrixes.ItshouldbenotedthatD50formostof the cases because it is the feedthrough term. If the system is nonlinear, the state and observation equations are respectively represented by x_ ¼fðx,uÞ (1.2) y5gðx,uÞ Here, one simple example is shown. The model of mass, damper, and spring is considered and illustrated in Fig. 1.1. Let x be the state, m be the mass, d be the damping coefficient, k be the spring coefficient, and f be the applied force. The equation of motion is then represented by k m f d x Fig.1.1 Modelofmass,damper,andspring.
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