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Robotics, Vision and Control ——Fundamental Algorithms in Matlab ....pdf
Cover
Contents
Nomenclature
Introduction
About the Book
The MATLAB Software
Audience and Prerequisites
Notation and Conventions
How to Use the Book
Teaching with the Book
Outline
Part I Foundations
Representing Position and Orientation
Representing Pose in 2-Dimensions
Representing Pose in 3-Dimensions
Representing Orientation in 3-Dimensions
Combining Translation and Orientation
Wrapping Up
Time and Motion
Trajectories
Smooth One-Dimensional Trajectories
Multi-Dimensional Case
Interpolation of Orientation in 3D
Cartesian Motion
Time Varying Coordinate Frames
Rotating Coordinate Frame
Incremental Motion
Inertial Navigation Systems
Wrapping Up
Part II Mobile Robots
Mobile Robot Vehicles
Mobility
Car-like Mobile Robots
Moving to a Point
Following a Line
Following a Path
Moving to a Pose
Flying Robots
Wrapping Up
Navigation
Reactive Navigation
Braitenberg Vehicles
Simple Automata
Map-Based Planning
Distance Transform
D*
Voronoi Roadmap Method
Probabilistic Roadmap Method
RRT
Wrapping Up
Localization
Dead Reckoning
Modeling the Vehicle
Estimating Pose
Using a Map
Creating a Map
Localization and Mapping
Monte-Carlo Localization
Wrapping Up
Part III Arm-Type Robots
Robot Arm Kinematics
Describing a Robot Arm
Forward Kinematics
A 2-Link Robot
A 6-Axis Robot
Inverse Kinematics
Closed-Form Solution
Numerical Solution
Under-Actuated Manipulator
Redundant Manipulator
Trajectories
Joint-Space Motion
Cartesian Motion
Motion through a Singularity
Configuration Change
Advanced Topics
Joint Angle Offsets
Determining Denavit-Hartenberg Parameters
Modified Denavit-Hartenberg Notation
Application: Drawing
Application: a Simple Walking Robot
Kinematics
Motion of One Leg
Motion of Four Legs
Wrapping Up
Velocity Relationships
Manipulator Jacobian
Transforming Velocities between Coordinate Frames
Jacobian in the End-Effector Coordinate Frame
Analytical Jacobian
Jacobian Condition and Manipulability
Resolved-Rate Motion Control
Jacobian Singularity
Jacobian for Under-Actuated Robot
Jacobian for Over-Actuated Robot
Force Relationships
Transforming Wrenches between Frames
Transforming Wrenches to Joint Space
Inverse Kinematics: a General Numerical Approach
Wrapping Up
Dynamics and Control
Equations of Motion
Gravity Term
Inertia Matrix
Coriolis Matrix
Effect of Payload
Base Force
Dynamic Manipulability
Drive Train
Forward Dynamics
Manipulator Joint Control
Actuators
Independent Joint Control
Rigid-Body Dynamics Compensation
Flexible Transmission
Wrapping Up
Part IV Computer Vision
Light and Color
Spectral Representation of Light
Absorption
Reflection
Color
Reproducing Colors
Chromaticity Space
Color Names
Other Color Spaces
Transforming between Different Primaries
What Is White?
Advanced Topics
Color Constancy
White Balancing
Color Change Due to Absorption
Gamma
Application: Color Image
Wrapping Up
Image Formation
Perspective Transform
Lens Distortion
Camera Calibration
Homogeneous Transformation Approach
Decomposing the Camera Calibration Matrix
Pose Estimation
Camera Calibration Toolbox
Non-Perspective Imaging Models
Fisheye Lens Camera
Catadioptric Camera
Spherical Camera
Unified Imaging
Mapping Wide-Angle Images to the Sphere
Synthetic Perspective Images
Wrapping Up
Image Processing
Obtaining an Image
Images from Files
Images from an Attached Camera
Images from a Movie File
Images from the Web
Images from Code
Monadic Operations
Diadic Operations
Spatial Operations
Convolution
Template Matching
Non-Linear Operations
Shape Changing
Cropping
Image Resizing
Image Pyramids
Image Warping
Wrapping Up
Image Feature Extraction
Region Features
Classification
Representation
Description
Recap
Line Features
Point Features
Scale-Space Corner Detectors
Wrapping Up
Using Multiple Images
Feature Correspondence
Geometry of Multiple Views
The Fundamental Matrix
The Essential Matrix
Estimating the Fundamental Matrix
Planar Homography
Stereo Vision
Sparse Stereo
Dense Stereo Matching
Peak Refinement
Cleaning up and Reconstruction
3D Texture Mapped Display
Anaglyphs
Image Rectification
Plane Fitting
Matching Sets of 3D Points
Structure and Motion
Application: Perspective Correction
Application: Mosaicing
Application: Image Matching and Retrieval
Application: Image Sequence Processing
Wrapping Up
Part V Robotics, Vision and Control
Vision-Based Control
Position-Based Visual Servoing
Image-Based Visual Servoing
Camera and Image Motion
Controlling Feature Motion
Depth
Performance Issues
Using Other Image Features
Line Features
Circle Features
Wrapping Up
Advanced Visual Servoing
XY/Z-Partitioned IBVS
IBVS Using Polar Coordinates
IBVS for a Spherical Camera
Application: Arm-Type Robot
Application: Mobile Robot
Holonomic Mobile Robot
Non-Holonomic Mobile Robot
Application: Aerial Robot
Wrapping Up
Appendices
Installing the Toolboxes
Simulink
MATLAB Objects
Linear Algebra Refresher
Ellipses
Gaussian Random Variables
Jacobians
Kalman Filter
Homogeneous Coordinates
Graphs
Peak Finding
Bibliography
Index
00730381.pdf
Using Multiple Images
Feature Correspondence
Geometry of Multiple Views
The Fundamental Matrix
The Essential Matrix
Estimating the Fundamental Matrix
Planar Homography
Stereo Vision
Sparse Stereo
Dense Stereo Matching
Peak Refinement
Cleaning up and Reconstruction
3D Texture Mapped Display
Anaglyphs
Image Rectification
Plane Fitting
Matching Sets of 3D Points
Structure and Motion
Application: Perspective Correction
Application: Mosaicing
Application: Image Matching and Retrieval
Application: Image Sequence Processing
Wrapping Up
Peter Corke
Robotics,
Vision
and
Control
FUNDAMENTAL
ALGORITHMS
IN MATLAB®
123
Springer Tracts in Advanced Robotics
Volume 73
Editors: Bruno Siciliano · Oussama Khatib
Peter Corke
Robotics,
Vision and Control
Fundamental Algorithms in MATLAB®
With 393 Images
Additional material is provided at www.petercorke.com/RVC
Professor Bruno Siciliano
Dipartimento di Informatica e Sistemistica, Università di Napoli Federico II,
Via Claudio 21, 80125 Napoli, Italy, E-mail: siciliano@unina.it
Professor Oussama Khatib
Artificial Intelligence Laboratory, Department of Computer Science,
Stanford University, Stanford, CA 94305-9010, USA, E-mail: khatib@cs.stanford.edu
Author
Peter Corke
School of Electrical Engineering and Computer Science
Queensland University of Technology (QUT)
Brisbane QLD 4000
Australia
e-mail: rvc@petercorke.com
ISBN 978-3-642-20143-1
e-ISBN 978-3-642-20144-8
DOI 10.1007/978-3-642-20144-8
Springer Tracts in Advanced Robotics
ISSN 1610-7438
Library of Congress Control Number: 2011934624
© Springer-Verlag Berlin Heidelberg, first edition 2011, corrected second printing 2013
This work is subject to copyright. All rights are reserved, whether the whole or part of the material
is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitations, broad-
casting, reproduction on microfilm or in any other way, and storage in data banks. Duplication of
this publication or parts thereof is permitted only under the provisions of the German Copyright
Law of September 9, 1965, in its current version, and permission for use must always be obtained
from Springer. Violations are liable to prosecution under the German Copyright Law.
The use of general descriptive names, registered names, trademarks, etc. in this publication does
not imply, even in the absence of a specific statement, that such names are exempt from the rel-
evant protective laws and regulations and therefore free for general use.
Production: Armin Stasch and Scientific Publishing Services Pvt. Ltd. Chennai, India
Typesetting and layout: Büro Stasch · Bayreuth (stasch@stasch.com)
Printed on acid-free paper
9 8 7 6 5 4 3 2
springer.com
Editorial Advisory Board
Oliver Brock, TU Berlin, Germany
Herman Bruyninckx, KU Leuven, Belgium
Raja Chatila, LAAS, France
Henrik Christensen, Georgia Tech, USA
Peter Corke, Queensland Univ. Technology, Australia
Paolo Dario, Scuola S. Anna Pisa, Italy
Rüdiger Dillmann, Univ. Karlsruhe, Germany
Ken Goldberg, UC Berkeley, USA
John Hollerbach, Univ. Utah, USA
Makoto Kaneko, Osaka Univ., Japan
Lydia Kavraki, Rice Univ., USA
Vijay Kumar, Univ. Pennsylvania, USA
Sukhan Lee, Sungkyunkwan Univ., Korea
Frank Park, Seoul National Univ., Korea
Tim Salcudean, Univ. British Columbia, Canada
Roland Siegwart, ETH Zurich, Switzerland
Gaurav Sukhatme, Univ. Southern California, USA
Sebastian Thrun, Stanford Univ., USA
Yangsheng Xu, Chinese Univ. Hong Kong, PRC
Shin’ichi Yuta, Tsukuba Univ., Japan
STAR (Springer Tracts in Advanced Robotics) has been promoted un-
der the auspices of EURON (European Robotics Research Network)
To my family Phillipa, Lucy and Madeline for their indulgence and support;
my parents Margaret and David for kindling my curiosity;
and to Lou Paul who planted the seed that became this book.
Foreword
Once upon a time, a very thick document of a dissertation from a faraway land came
to me for evaluation. Visual robot control was the thesis theme and Peter Corke was its
author. Here, I am reminded of an excerpt of my comments, which reads, this is a
masterful document, a quality of thesis one would like all of one’s students to strive for,
knowing very few could attain – very well considered and executed.
The connection between robotics and vision has been, for over two decades, the
central thread of Peter Corke’s productive investigations and successful developments
and implementations. This rare experience is bearing fruit in his new book on Robotics,
Vision, and Control. In its melding of theory and application, this new book has con-
siderably benefited from the author’s unique mix of academic and real-world appli-
cation influences through his many years of work in robotic mining, flying, under-
water, and field robotics.
There have been numerous textbooks in robotics and vision, but few have reached
the level of integration, analysis, dissection, and practical illustrations evidenced in
this book. The discussion is thorough, the narrative is remarkably informative and
accessible, and the overall impression is of a significant contribution for researchers
and future investigators in our field. Most every element that could be considered as
relevant to the task seems to have been analyzed and incorporated, and the effective
use of Toolbox software echoes this thoroughness.
The reader is taken on a realistic walkthrough the fundamentals of mobile robots,
navigation, localization, manipulator-arm kinematics, dynamics, and joint-level con-
trol, as well as camera modeling, image processing, feature extraction, and multi-
view geometry. These areas are finally brought together through extensive discus-
sion of visual servo system. In the process, the author provides insights into how
complex problems can be decomposed and solved using powerful numerical tools
and effective software.
The Springer Tracts in Advanced Robotics (STAR) is devoted to bringing to the
research community the latest advances in the robotics field on the basis of their
significance and quality. Through a wide and timely dissemination of critical research
developments in robotics, our objective with this series is to promote more exchanges
and collaborations among the researchers in the community and contribute to fur-
ther advancements in this rapidly growing field.
Peter Corke brings a great addition to our STAR series with an authoritative book,
reaching across fields, thoughtfully conceived and brilliantly accomplished.
Oussama Khatib
Stanford, California
July 2011
Preface
Tell me and I will forget.
Show me and I will remember.
Involve me and I will understand.
Chinese proverb
The practice of robotics and machine vision involves the application of algorithms to
data. The data comes from sensors measuring the velocity of a wheel, the angle of a
robot arm’s joint or the intensities of millions of pixels that comprise an image of the
world that the robot is observing. For many robotic applications the amount of data
that needs to be processed, in real-time, is massive. For vision it can be of the order of
tens to hundreds of megabytes per second.
Progress in robots and machine vision has been, and continues to be, driven by
more effective ways to process data. This is achieved through new and more efficient
algorithms, and the dramatic increase in computational power that follows Moore’s
law. When I started in robotics and vision, in the mid 1980s, the IBM PC had been
recently released – it had a 4.77 MHz 16-bit microprocessor and 16 kbytes (expand-
able to 256 k) of memory. Over the intervening 25 years computing power has doubled
16 times which is an increase by a factor of 65 000. In the late 1980s systems capable
of real-time image processing were large 19 inch racks of equipment such as shown
in Fig. 0.1. Today there is far more computing in just a small corner of a modern
microprocessor chip.
Over the fairly recent history of robotics and machine vision a very large body of
algorithms has been developed – a significant, tangible, and collective achievement of
the research community. However its sheer size and complexity presents a barrier to
somebody entering the field. Given the many algorithms from which to choose the
obvious question is:
What is the right algorithm for this particular problem?
One strategy would be to try a few different algorithms and see which works best
for the problem at hand but this raises the next question:
How can I evaluate algorithm X on my own data without spending days coding and
debugging it from the original research papers?
Fig. 0.1.
Once upon a time a lot of
equipment was needed to do
vision-based robot control. The
author with a large rack full of
image processing and robot
control equipment (1992)
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