Machine Learning and Robot Perception

357 Pages·2005·9.678 MB·English

by  Mario Mata, Jose Maria Armingol (auth.), Professor Bruno Apolloni, Professor Ashish Ghosh, Professor Ferda Alpaslan, Professor Lakhmi C. Jain, Professor Srikanta Patnaik (eds.)

BrunoApolloni,AshishGhosh,FerdaAlpaslan,LakhmiC.Jain,
SrikantaPatnaik(Eds.)
MachineLearningandRobotPerception
StudiesinComputational Intelligence,Volume7
Editor-in-chief
Prof.JanuszKacprzyk
SystemsResearchInstitute
PolishAcademyofSciences
ul.Newelska6
01-447Warsaw
Poland
E-mail:[email protected]
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Vol.7.BrunoApolloni,AshishGhosh,Ferda
Alpaslan,LakhmiC.Jain,SrikantaPatnaik
(Eds.)
MachineLearningandRobotPerception,
2005
ISBN3-540-26549-X
Bruno Apolloni
Ashish Ghosh
Ferda Alpaslan
Lakhmi C. Jain
Srikanta Patnaik
(Eds.)
Machine Learning
and Robot Perception
ABC
ProfessorBrunoApolloni ProfessorLakhmiC.Jain
DepartmentofInformationScience SchoolofElectrical&InfoEngineering
UniversityofMilan UniversityofSouthAustralia
ViaComelico39/41 Knowledge-BasedIntelligentEngineering
20135Milan MawsonLakesCampus
Italy 5095Adelaide,SA
E-mail:[email protected] Australia
E-mail:[email protected]
ProfessorAshishGhosh ProfessorSrikantaPatnaik
MachineIntelligenceUnit DepartmentofInformation
IndianStatisticalInstitute andCommunicationTechnology
203BarrackporeTrunkRoad F.M.University
Kolkata700108 VyasaVihar
India Balasore-756019
E-mail:[email protected] Orissa,India
E-mail:[email protected]
ProfessorFerdaAlpaslan
FacultyofEngineering
DepartmentofComputerEngineering
MiddleEastTechnicalUniversity-METU
06531Ankara
Turkey
E-mail:[email protected]
LibraryofCongressControlNumber:2005929885
ISSNprintedition:1860-949X
ISSNelectronicedition:1860-9503
ISBN-10 3-540-26549-XSpringerBerlinHeidelbergNewYork
ISBN-13 978-3-540-26549-8SpringerBerlinHeidelbergNewYork
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Preface
This book presents some of the most recent research results in the
area of machine learning and robot perception. The book contains
eight chapters.
The first chapter describes a general-purpose deformable model
based object detection system in which evolutionary algorithms are
used for both object search and object learning. Although the
proposed system can handle 3D objects, some particularizations
have been made to reduce computational time for real applications.
The system is tested using real indoor and outdoor images. Field
experiments have proven the robustness of the system for
illumination conditions and perspective deformation of objects. The
natural application environments of the system are predicted to be
useful for big public and industrial buildings (factories, stores), and
outdoor environments with well-defined landmarks such as streets
and roads.
Fabrication of space-variant sensor and implementation of vision
algorithms on space-variant images is a challenging issue as the
spatial neighbourhood connectivity is complex. The lack of shape
invariance under translation also complicates image understanding.
The retino-cortical mapping models as well as the state-of-the-art of
the space-variant sensors are reviewed to provide a better
understanding of foveated vision systems in Chapter 2. It is argued
that almost all the low level vision problems (i.e., shape from
shading, optical flow, stereo disparity, corner detection, surface
interpolation etc.) in the deterministic framework can be addressed
using the techniques discussed in this chapter. The vision system
must be able to determine where to point its high-resolution fovea. A
proper mechanism is expected to enhance image understanding by
strategically directing fovea to points which are most likely to yield
important information.
In Chapter 3 a discrete wavelet based model identification method
has been proposed in order to solve the online learning problem. The
vi Preface
method minimizes the least square residual parameter estimation in
noisy environments. It offers significant advantages over the
classical least square estimation methods as it does not need prior
statistical knowledge of measurement of noises. This claim is
supported by the experimental results on estimating the mass and
length of a nonholonomic cart having a wide range of applications in
complex and dynamic environments.
Chapter 4 proposes a reinforcement learning algorithm which allows
a mobile robot to learn simple skills. The neural network
architecture works with continuous input and output spaces, has a
good resistance to forget previously learned actions and learns
quickly. Nodes of the input layer are allocated dynamically. The
proposed reinforcement learning algorithm has been tested on an
autonomous mobile robot in order to learn simple skills showing
good results. Finally the learnt simple skills are combined to
successfully perform more complex skills called visual approaching
and go to goal avoiding obstacles.
In Chapter 5 the authors present a simple but efficient approach to
object tracking combining active contour framework and the optical-
flow based motion estimation. Both curve evolution and polygon
evolution models are utilized to carry out the tracking. No prior
shape model assumptions on targets are made. They also did not
make any assumption like static camera as is widely employed by
other object tracking methods. A motion detection step can also be
added to this framework for detecting the presence of multiple
moving targets in the scene.
Chapter 6 presents the state-of-the-art for constructing geometrically
and photometrically correct 3D models of real-world objects using
range and intensity images. Various surface properties that cause
difficulties in range data acquisition include specular surfaces,
highly absorptive surfaces, translucent surfaces and transparent
surfaces. A recently developed new range imaging method takes into
account of the effects of mutual reflections, thus providing a way to
construct accurate 3D models. The demand for constructing 3D models of
various objects has been steadily growing and we can naturally predict that
it will continue to grow in the future.
Preface vii
Systems that visually track human motion fall into three basic
categories: analysis-synthesis, recursive systems, and statistical
methods including particle filtering and Bayesian networks. Each of
these methods has its uses. In Chapter 7 the authors describe a
computer vision system called DYNA that employs a three-
dimensional, physics-based model of the human body and a
completely recursive architecture with no bottom-up processes. The
system is complex but it illustrates how careful modeling can
improve robustness and open the door to very subtle analysis of
human motion. Not all interface systems require this level of
subtlety, but the key elements of the DYNAarchitecture can be tuned
to the application. Every level of processing in the DYNAframework
takes advantage of the constraints implied by the embodiment of the
observed human. Higher level processes take advantage of these
constraints explicitly while lower level processes gain the advantage
of the distilled body knowledge in the form of predicted probability
densities.
Chapter 8 advocates the concept of user modelling which involves
dialogue strategies. The proposed method allows dialogue strategies
to be determined by maximizing mutual expectations of the pay-off
matrix. The authors validated the proposed method using iterative
prisoner's dilemma problem that is usually used for modelling social
relationships based on reciprocal altruism. Their results suggest that
in principle the proposed dialogue strategy should be implemented
to achieve maximum mutual expectation and uncertainty reduction
regarding pay-offs for others.
We are grateful to the authors and the reviewers for their valuable
contributions. We appreciate the assistance of Feng-Hsing Wang
during the evolution phase of this book.
June 2005 Bruno Apolloni
Ashish Ghosh
Ferda Alpaslan
Lakhmi C. Jain
Srikanta Patnaik

Table of Contents
1 Learning Visual Landmarks for Mobile Robot Topological Navigation 1
Mario Mata, Jose Maria Armingol, and Arturo de la Escalera
2 Foveated Vision Sensor and Image Processing – A Review ............... 57
Mohammed Yeasin andRajeev Sharma
3 On-line Model Learning for Mobile Manipulations ............................ 99
Yu Sun, Ning Xi, and Jindong Tan
4 Continuous Reinforcement Learning Algorithm for Skills Learning in an
Autonomous Mobile Robot ............................................................... 137
Mª Jesús López Boada, Ramón Barber, Verónica Egido, and
Miguel Ángel Salichs
5 Efficient Incorporation of Optical Flow into Visual Motion Estimation
in Tracking ......................................................................................... 167
Gozde Unal, Anthony Yezzi, and Hamid Krim
6 3-D Modeling of Real-World Objects Using Range
and Intensity Images .......................................................................... 203
Johnny Park and Guilherme N. DeSouza
7 Perception for Human Motion Understanding ................................... 265
Christopher R. Wren
8 Cognitive User Modeling Computed by a Proposed Dialogue Strategy
Based on an Inductive Game Theory ................................................. 325
Hirotaka Asai, Takamasa Koshizen, Masataka Watanabe,
Hiroshi Tsujin and Kazuyuki Aihara
1 Learning Visual Landmarks for Mobile Robot
Topological Navigation
Mario Mata1, Jose Maria Armingol2, Arturo de la Escalera2
1. Computer Architecture and Automation Department, Universidad
Europea de Madrid, 28670 Villaviciosa de Odon, Madrid, Spain.
[email protected]
2. Systems Engineering and Automation Department. Universidad
Carlos III de Madrid, 28911 Leganés, Madrid, Spain.
{armingol,escalera}@ing.uc3m.es
1.1 Introduction
Relevant progress has been done, within the Robotics field, in mechanical
systems, actuators, control and planning. This fact, allows a wide applica-
tion of industrial robots, where manipulator arms, Cartesian robots, etc.,
widely outcomes human capacity. However, the achievement of a robust
and reliable autonomous mobile robot, with ability to evolve and accom-
plish general tasks in unconstrained environments, is still far from accom-
plishment. This is due, mainly, because autonomous mobile robots suffer
the limitations of nowadays perception systems. A robot has to perceive its
environment in order to interact (move, find and manipulate objects, etc.)
with it. Perception allows making an internal representation (model) of the
environment, which has to be used for moving, avoiding collision, finding
its position and its way to the target, and finding objects to manipulate
them. Without a sufficient environment perception, the robot simply can’t
make any secure displacement or interaction, even with extremely efficient
motion or planning systems. The more unstructured an environment is, the
most dependent the robot is on its sensorial system. The success of indus-
trial robotics relies on rigidly controlled and planned environments, and a
total control over robot’s position in every moment. But as the environ-
ment structure degree decreases, robot capacity gets limited.
Some kind of model environment has to be used to incorporate percep-
tions and taking control decisions. Historically, most mobile robots are
based on a geometrical environment representation for navigation tasks.
This facilitates path planning and reduces dependency on sensorial system,
but forces to continuously monitor robot’s exact position, and needs precise
M.Mataetal.:Learning Visual Landmarks for Mobile Robot Topological Navigation,Studies
inComputationalIntelligence(SCI)7,1–55(2005)
www.springerlink.com (cid:1)c Springer-VerlagBerlinHeidelberg2005