springer com Springer Handbook of Robotics,Springer Handbook of Robotics. B Siciliano Universit degli Studi di Napoli robotics The handbook is an ideal resource. Federico II Naples Italy O Khatib Stanford for robotics experts but also for people new to. University Stanford CA USA Eds this expanding field such as engineers medical. doctors computer scientists designers edited, Robotics is undergoing a major transforma by two internationally renowned experts. tion in scope and dimension Starting from a, predominantly industrial focus robotics has 7 Research and application oriented hand. been rapidly expanding into the challenges book covering one of the hottest topics in. of unstructured environments The Springer science and technology. Handbook of Robotics incorporates these 7 A timely and up to date reference edited by. new developments and therefore basically two internationally renowned experts. differs from other handbooks of robotics 7 Surveys developments and applications of. focusing on industrial applications It presents robotics in industrial settings and beyond. a widespread and well structured coverage 7 Ideal for experts as well as people new to. from the foundations of robotics through the this growing field computer scientists. consolidated methodologies and technologies medical doctors and engineers. up to the new emerging application areas of,Visit springer com for detailed table of content. 2008 Print 2008 eReference 2008 Print eReference, LX 1611 p 1375 illus ISBN 978 3 540 30301 5 ISBN 978 3 540 38219 5. 422 in color With DVD with 7 199 00 7 249 00,full content Hardcover. ISBN 978 3 540 23957 4,About the Editors, Bruno Siciliano is Professor of Control and President for Technical Activities and Vice. Robotics in the Faculty of Engineering of the President for Publications as an AdCom. University of Naples Director of the PRISMA Lab member as a Distinguished Lecturer and as. in the Department of Computer and Systems of 2008 the Society President Prof Siciliano. Engineering He is a Fellow of both IEEE and has co authored 210 journal and conference. ASME and on the Board of the European papers 7 books on robotics and edits the. Robotics Research Network He has served the Springer Tracts in Advanced Robotics STAR. IEEE Robotics and Automation Society as Vice series. Oussama Khatib is Professor at Stanford Society and a recipient of the JARA Japan. University President of IFRR the International Robot Association Award in Research and. Foundation of Robotics Research Distinguished Development. Lecturer of the IEEE Robotics and Automation,Springer Handbook of Robotics springer com. Part Editors, David Orin Part A Robotics Foundations Raja Chatila Part E Mobile and Distributed. Frank Chongwoo Park Part B Robot Structures Robotics. Henrik I Christensen Part C Sensing and Alexander Zelinsky Part F Field and Service. Perception Robotics, Makoto Kaneko Part D Manipulation and Daniela Rus Part G Human Centered and. Interfaces Life Like Robotics,Table of Contents, Introduction to Robotics Bruno Chap 16 Legged Robots Shuuji Kajita Chap 31 Telerobotics G nter Niemeyer. Siciliano Oussama Khatib Bernard Espiau Carsten Preusche Gerd Hirzinger. Part A Robotics Foundations Chap 17 Wheeled Robots Guy Campion Chap 32 Networked Teleoperation. David Orin Woojin Chung Dezhen Song Kenneth Goldberg. Chap 1 Kinematics Ken Waldron Chap 18 Micro Nano Robots Nak Young Chong. James Schmiedeler Brad Nelson Lixin Dong Fumihito Arai Chap 33 Exoskeletons for Human Perfor. Chap 2 Dynamics Roy Featherstone mance Augmentation Hami Kazerooni. David Orin Part C Sensing and Perception, Chap 3 Mechanisms and Actuation Henrik Christensen Part E Mobile and Distributed Robotics. Victor Scheinman Michael McCarthy Chap 19 Force and Tactile Sensors Raja Chatila. Chap 4 Sensing and Estimation Mark Cutkosky Robert Howe Chap 34 Motion Control of Wheeled. Henrik Christensen Gregory Hager William Provancher Mobile Robots Pascal Morin. Chap 5 Motion Planning Lydia Kavraki Chap 20 Inertial Sensors GPS and Odom Claude Samson. Steve LaValle etry Gregory Dudek Michael Jenkin Chap 35 Motion Planning and Obstacle. Chap 6 Motion Control Wankyun Chung Chap 21 Sonar Sensing Lindsay Avoidance Javier Minguez. Li Chen Fu Su Hau Hsu Kleeman Roman Kuc Florent Lamiraux Jean Paul Laumond. Chap 7 Force Control Luigi Villani Chap 22 Range Sensors Robert Fisher Chap 36 World Modeling. Joris De Schutter Kurt Konolige Wolfram Burgard Martial Hebert. Chap 8 Robotic Systems Architectures Chap 23 3D Vision and Recognition Chap 37 Simultaneous Localization and. and Programming David Kortenkamp Kostas Daniliidis Jan Olof Eklundh Mapping Sebastian Thrun John Leonard. Reid Simmons Chap 24 Visual Servoing and Visual Chap 38 Behavior Based Systems. Chap 9 AI Reasoning Methods for Tracking Fran ois Chaumette Maja Mataric Fran ois Michaud. Robotics Joachim Hertzberg Raja Chatila Seth Hutchinson Chap 39 Distributed and Cellular Robots. Chap 25 Sensor Fusion Zack Butler Alfred Rizzi, Part B Robot Structures Frank Park Hugh Durrant Whyte Tom Henderson Chap 40 Multiple Mobile Robot Systems. Chap 10 Performance Evaluation and Lynne Parker, Design Criteria Jorge Angeles Frank Park Part D Manipulation and Interfaces Chap 41 Networked Robots Vijay Kumar. Chap 11 Redundant Manipulators Makoto Kaneko Daniela Rus Gaurav Sukhatme. Stefano Chiaverini Giuseppe Oriolo Chap 26 Motion for Manipulation Tasks. Ian Walker Oliver Brock James Kuffner Jing Xiao Part F Field and Service Robotics. Chap 12 Parallel Mechanisms and Robots Chap 27 Modelling and Manipulation Alexander Zelinsky. Jean Pierre Merlet Cl ment Gosselin Imin Kao Kevin Lynch Joel Burdick Chap 42 Industrial Robotics Martin. Chap 13 Robots with Flexible Elements Chap 28 Grasping Jeff Trinkle H gele Klas Nilsson Norberto Pires. Alessandro De Luca Wayne Book Domenico Prattichizzo Chap 43 Underwater Robotics Gianluca. Chap 14 Model Identification Chap 29 Cooperative Manipulators Antonelli Thor Inge Fossen Dana Yoerger. John Hollerbach Wisama Khalil Fabrizio Caccavale Masaru Uchiyama Chap 44 Aerial Robotics Eric Feron. Maxime Gautier Chap 30 Haptics Blake Hannaford Eric Johnson. Chap 15 Robot Hands Allison Okamura Chap 45 Space Robots and Systems. Claudio Melchiorri Makoto Kaneko Kazuya Yoshida Brian Wilcox. no printing,Springer Handbook of Robotics springer com. no printing, Chap 46 Robotics in Agriculture and Chap 52 Medical Robots and Systems Chap 58 Social Robots that Interact. Forestry John Billingsley Arto Visala Russell Taylor Arianna Menciassi with People Cynthia Breazeal. Mark Dunn Gabor Fichtinger Paolo Dario Atsuo Takanishi Tetsunori Kobayashi. Chap 47 Robotics in Construction Chap 53 Rehabilitation and Health Care Chap 59 Robot Programming by. no printing, Kamel Saidi Jonathan O Brien Robotics Machiel van der Loos Demonstration Aude Billard. Alan Lytle David J Reinkensmeyer Sylvain Calinon Ruediger Dillmann. Chap 48 Robotics in Hazardous Chap 54 Domestic Robots Stefan Schaal. Applications James Trevelyan Erwin Prassler Kazuhiro Kosuge Chap 60 Biologically Inspired Robots. no printing, Sungchul Kang William Hamel Chap 55 Robots for Education Jean Arcady Meyer Agn s Guillot. Chap 49 Mining Robotics Peter Corke David Miller Illah Nourbakhsh Chap 61 Evolutionary Robotics Dario. Jonathan Roberts Jock Cunningham Roland Siegwart Floreano Phil Husbands Stefano Nolfi. no printing, David Hainsworth Chap 62 Neurorobotics From Vision to. Chap 50 Search and Rescue Robotics Part G Human Centered and Life Like Action Michael Arbib Giorgio Metta. Robin Murphy Satoshi Tadokoro Robotics Daniela Rus Patrick van der Smagt. Daniele Nardi Adam Jacoff Paolo Fiorini Chap 56 Humanoids Charles Kemp Chap 63 Perceptual Robotics. no printing, Howie Choset Aydan Erkmen Paul Fitzpatrick Hirohisa Hirukawa Heinrich B lthoff Christian Wallraven. Chap 51 Intelligent Vehicles Kazuhito Yokoi Kensuke Harada Martin Giese. Alberto Broggi Alexander Zelinsky Yoshio Matsumoto Chap 64 Roboethics Social and Ethical. Michel Parent Charles Thorpe Chap 57 Safety for Physical Human Implications Gianmarco Veruggio. no printing,Robot Interaction Antonio Bicchi Fiorella Operto. Michael Peshkin Edward Colgate,no printing,Kinematics. 1 Kinematics,no printing,Ken Waldron Jim Schmiedeler. 1 1 Overview 1,Kinematics pertains to the motion of bod. no printing, ies in a robotic mechanism without regard 1 2 Position and Orientation Representation 2. 1 2 1 Position and Displacement 2 1,to the forces torques that cause the motion. Since robotic mechanisms are by their very 1 2 2 Orientation and Rotation 2. Multisensor D, essence designed for motion kinematics is 1 2 3 Homogeneous Transformations 5. the most fundamental aspect of robot de 1 2 4 Screw Transformations 6. 25 Multisensor Data Fusion,1 2 5 Matrix Exponential. sign analysis control and simulation The,Parameterization 8. robotics community has focused on efficiently,1 2 6 Pl cker Coordinates 10. applying different representations of position, and orientation and their derivatives with re 1 3 Joint Kinematics 10. spect to time to solve foundational kinematics 1 3 1 Lower Pair Joints 11. 1 3 2 Higher Pair Joints 13,no printing,problems 25 1 Multisensor Data Fusion Methods 1. 1 3 3 Compound Joints 14 Multisensor data fusion is the process of com. This chapter will present the most useful 25 1 1 Bayes Rule 2. 1 3 4 6 DOF Joint 14 bining observations from a number of different. representations of the position and orienta 25 1 2 Probabilistic Grids 5. 1 3 5 Physical Realization 14 sensors to provide a robust and complete de. tion of a body in space the kinematics of 25 1 3 The Kalman Filter 6. 1 3 6 Holonomic scription of an environment or process of 25 1 4 Sequential Monte Carlo Methods 10. the joints most commonly found in robotic and Nonholonomic Constraints 15 interest Data fusion nds wide application 25 1 5 Alternatives to Probability 12. mechanisms and a convenient convention for 1 3 7 Generalized Coordinates 15 in many areas of robotics such as object. representing the geometry of robotic mech 25 2 Multisensor Fusion Architectures 14. 1 4 Geometric Representation 15 recognition environment mapping and locali. anisms These representational tools will be 25 2 1 Architectural Taxonomy 14. 1 5 Workspace 17 sation, applied to compute the workspace the for 25 2 2 Centralized Local Interaction. This chapter has three parts methods ar, ward and inverse kinematics the forward 1 6 Forward Kinematics 18 and Hierarchical 16. chitectures and applications Most current data,no printing. and inverse instantaneous kinematics and 1 7 Inverse Kinematics 19 25 2 3 Decentralized Global Interaction. fusion methods employ probabilistic descriptions, the static wrench transmission of a robotic 1 7 1 Closed Form Solutions 19 and Heterarchical 16. of observations and processes and use Bayes rule, mechanism For brevity the focus will be on 1 7 2 Numerical Methods 20 25 2 4 Decentralized Local Interaction. algorithms applicable to open chain mecha, to combine this information This chapter sur and Hierarchical 17. 1 8 Forward Instantaneous Kinematics 21 veys the main probabilistic modeling and fusion. nisms 25 2 5 Decentralized Local Interaction, 1 8 1 Jacobian 21 techniques including grid based models Kalman. The goal of this chapter is to provide the and Heterarchical 18. reader with general tools in tabulated form 1 9 Inverse Instantaneous Kinematics 22 ltering and sequential Monte Carlo techniques. 1 9 1 Inverse Jacobian 22 This chapter also brie y reviews a number of 25 3 Applications 19. and a broader overview of algorithms that 25 3 1 Dynamic System Control 19. can be applied together to solve kinemat 1 10 Static Wrench Transmission 22 non probabilistic data fusion methods Data fu. sion systems are often complex combinations of 25 3 2 ANSER II Decentralised Data Fusion 20. ics problems pertaining to a particular robotic 1 11 Conclusions and Further Reading 23 no printing. mechanism sensor devices processing and fusion algorithms 25 4 Conclusions and Further Reading 23. References 23 This chapter provides an overview of key principles. References 24,in data fusion architectures from both a hardware. and algorithmic viewpoint The applications of, data fusion are pervasive in robotics and underly application in mapping and environment model. 1 1 Overview the core problem of sensing estimation and per ing. ception We highlight two example applications The essential algorithmic tools of data fusion. Unless explicitly stated otherwise robotic mecha fore robot kinematics describes the pose velocity. that bring out these features The rst describes are reasonably well established However the. nisms are systems of rigid bodies connected by acceleration and all higher order derivatives of the. a navigation or self tracking application for an development and use of these tools in realistic. joints The position and orientation of a rigid body pose of the bodies that comprise a mechanism Since. autonomous vehicle The second describes an robotics applications is still developing. no printing, in space are collectively termed the pose There kinematics does not address the forces torques that. 25 1 Multisensor Data Fusion Methods, The most widely used data fusion methods employed In this section we review the main data fusion meth. in robotics originate in the elds of statistics esti ods employed in robotics These are very often based on. mation and control However the application of these probabilistic methods and indeed probabilistic meth. no printing, methods in robotics has a number of unique features ods are now considered the standard approach to data. and challenges In particular most often autonomy fusion in all robotics applications 25 1 Probabilis. is the goal and so results must be presented and tic data fusion methods are generally based on Bayes. Each chapter comes with a, interpreted in a form from which autonomous deci rule for combining prior and observation information. sions can be made for recognition or navigation for Practically this may be implemented in a number of. example ways through the use of the Kalman and extended. summary and its own index for,cross referencing to sections. no printing,no printing,springer com Springer Handbook of Robotics. no printing, Multisensor Data Fusion 25 4 Conclusions and Further Reading 23. Part C 25 4,no printing,Easy to read and use includes about 1000. diagrams and illustrations,no printing, Multisensor Data Fusion 25 1 Multisensor Data Fusion Methods 3. no printing, the product of the two becomes when normalised the in tracking and navigation The general ltering problem. Part C 25 1, new posterior can be formulated in Bayesian form This is signi cant. because it provides a common representation for a range. Bayesian Filtering of discrete and continuous data fusion problems without. Filtering is concerned with the sequential process of recourse to speci c target or observation models. maintaining a probabilistic model for a state which De ne xt as the value of a state of interest at time t. evolves over time and which is periodically observed This may for example describe a feature to be tracked. no printing, by a sensor Filtering forms the basis for many problems the state of a process being monitored or the location. Fig 25 10a i A synopsis of the ANSER II autonomous network and its operation a c Main system components a air vehicle. b ground vehicle c human operative d e The perception process d top three dimensions of features discovered from. ground based visual sensor data along with the derived mixture model describing these feature properties e sector of the overall. picture obtained from fusing air vehicle UAV ground vehicle GV and human operator HO information Each set of ellipses P xk 1 xk. corresponds to a particular feature and the labels represent the identity state with highest probability f i Sequential fusion. P xk xk 1 dxk 1, process for two close landmarks f a tree and a red car g bearing only visual observations of these landmarks are successively P xk 1. no printing, fused h to determine location and identity i Note the Gaussian mixture model for the bearing measurement likelihood 1 2. 25 4 Conclusions and Further Reading 0 6 P xk xk 1 dxk. P xk 1 xk 1, Multisensor data fusion has progressed much in the and integration conference and journal literature Ro. last few decades further advances in the eld will bust applications are being elded based on the body. be documented in the robotics and multisensor fusion of theory and experimental knowledge produced by 0 2. no printing,2 Part C Sensing and Perception 0, quencies the sonar energy is concentrated in a beam low power consumption and low computational effort 40. xk f xk 1 Uk, providing directional information in addition to range compared to other ranging sensors In some applications. Its popularity is due to its inexpensive cost light weight such as in underwater and low visibility environments 30 15 xk. sonar is often the only viable sensing modality, Sonars in robotics have three different but related 20 10. a purposes,no printing, O 1 Obstacle avoidance The rst detected echo is as. P sumed to measure the range to the closest object. Robots use this information to plan paths around, obstacles and to prevent collisions Fig 25 1 Time update step for the full Bayes lter At a time k 1 knowledge of the state xk 1 is summarised in. 2 Sonar mapping A collection of echoes acquired by a probability distribution P xk 1 A vehicle model in the form of a conditional probability density P xk xk 1 then. b performing a rotational scan or from a sonar array describes the stochastic transition of the vehicle from a state xk 1 at a time k 1 to a state xk at a time k Functionally. Transmission are used to construct a map of the environment Sim this state transition may be related to an underlying kinematic state model in the form xk f xk 1 uk The gure shows. ilar to a radar display a range dot is placed at the two typical conditional probability distributions P xk xk 1 on the state xk given xed values of xk 1 The product. t0 Time detected range along the probing pulse direction of this conditional 16 distribution marginal distribution P xk 1 describing the prior likelihood of values of xk. with theFoundations,Part A Robotics, 3 Object recognition A sequence of echoes or sonar gives the the joint distribution P xk xk 1 shown as the surface in the gure The total marginal density P xk describes. no printing, c maps are processed to classify echo producing struc knowledge of xk after state transition has occurred The marginal density P xk is obtained by integrating projecting the. Sonar Range, location dot tures composed of one or more physical objects joint distribution P xk xk 1 the. oversubscripts,all xk 1 of the joint parameters,Equivalently using the dototalnotprobability. match thattheorem the marginal density can,Part A 1 4. When successful this information is useful for robot of the joint. be obtained by integrating summing axis Waldron,all conditional 1 27 P x. densities andk Paul,xk 1 1 28 modi by the prior probability P xk 1 of. r0 registration or landmark navigation fied thebelabeling. each xk 1 The process can equally of axes a,run in reverse in retroverse. the originalmotion,convention such,model to obtain P xk 1 from P xk given. a model P xk 1 xk that joint i is located between links i 1 and i in order. Figure 21 1 shows a simpli ed sonar from con gu to make it consistent with the base member of a serial z 3. d ration to sonar map A sonar transducer T R acts as both chain being member 0 This places joint i at the inboard. the transmitter T of a probing acoustic pulse P and side of link i and is the convention used in all of the other z 2. the receiver of echoes E An object O lying within the modified versions Furthermore Waldron and Paul ad z 6 z 5. sonar beam indicated as the shaded region re ects the dressed the mismatch between subscripts of the joint. no printing,Part C 21 1, probing pulse A part of the re ected signal impinges parameters and joint axes by placing the z i axis along. on the transducer as is detected as an echo The echo the i 1 joint axis This of course relocates the sub z 1. Fig 21 1a d Sonar ranging principles a Sonar con guration travel time to commonly called the time of ight TOF script mismatch to the correspondence between the joint z 4. b Echo waveform c Range dot placement d Sonar map is measured from the probing pulse transmission time axis and the z axis of the reference frame Craig 1 29. In this case the echo waveform is a replica of the prob eliminated all of the subscript mismatches by placing. ing pulse which usually consists of as many as 16 cycles the z i axis along joint i but at the expense of the homo. a at the resonant frequency of the transducer The object geneous transform i 1Ti being formed with a mixture. range ro is computed from to using of joint parameters with subscript i and link parameters. with subscript i 1 Khalil and Dombre 1 26 intro, T R cto duced another variation similar to Craig s except that it. no printing, E ro 21 1 defines the link parameters ai and i along and about Fig 1 3 Example six degree of freedom serial chain. the x i 1 axis In this case the homogeneous transform manipulator composed of an articulated arm with no joint. i 1T is formed only with parameters with subscript i offsets and a spherical wrist. where c is the sound speed 343 m s at standard tem i. b and the subscript mismatch is such that ai and i indi. Transmission Transmission perature and pressure The factor of 2 converts the. time 1 time 2 round trip P E travel distance to a range measure cate the length and twist of link i 1 rather than link native conventions are that the z axes of the reference. ment The beam spreading loss and acoustic absorption i Thus in summary the advantages of the convention frames share the common subscript of the joint axes. t0 Time used throughout this handbook compared to the alter and the four parameters that define the spatial transform. limit sonar range, In forming a sonar map a range dot is placed along from reference frame i to reference frame i 1 all share. c the common subscript i, Sonar the direction corresponding to the transducer s physical x i In this handbook the convention for serial chain. no printing, location FR orientation A sonar map is usually built by rotating the. manipulators is shown in Fig 1 2 and summarized as. sensor about the vertical axis indicated by the orienta i. r0 Body i follows The numbering of bodies and joints follows the. tion angle through a series of discrete angles separated. convention, Fig 21 2a c False range reading a Sonar con guration b Prob by and placing sonar dots the corresponding ranges. ing pulse 2 transmitted before echo from pulse 1 arrives c False Since the range from the object O to the center of T R is the N moving bodies of the robotic mechanism are. range FR is measured from transmission time 2 almost constant as T R rotates the range dots typically di x i 1 numbered from 1 to N The number of the base is 0. the N joints of the robotic mechanism are numbered. from 1 to N with joint i located between members,no printing. Joint i With this numbering scheme the attachment of refer. Joint i 1 ence frames follows the convention,the z i axis is located along the axis of joint i. i 1 the x i 1 axis is located along the common normal. Body between the z i 1 and z i axes, Using the attached frames the four parameters that. locate one frame relative to another are defined as. Fig 1 2 Schematic of the numbering of bodies and joints. Thumb indices identify the part in a robotic manipulator the convention for attaching ref. erence frames to the bodies and the definitions of the four. ai is the distance from z i 1 to z i along x i 1,i is the angle from z i 1 to z i about x i 1. no printing, parameters ai i di and i that locate one frame relative di is the distance from x i 1 to x i along z i. and chapter section to another i is the angle from x i 1 to x i about z i. Part title for easy navigation,no printing,ABCD PRSRT STD. U S POSTAGE,233 Spring Street,New York NY 10013,Order Now Springer Reference. 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