|
Showing 1 - 5 of
5 matches in All Departments
"The coverage is unparalleled in both depth and breadth. No other
text that I have seen offers a better complete overview of modern
robotic manipulation and robot control."
-- Bradley Bishop, United States Naval Academy
Based on the highly successful classic, Robot Dynamics and Control,
by Spong and Vidyasagar (Wiley, 1989), Robot Modeling and Control
offers a thoroughly up-to-date, self-contained introduction to the
field. The text presents basic and advanced material in a style
that is at once readable and mathematically rigorous.
Key Features
* A step-by-step computational approach helps you derive and
compute the forward kinematics, inverse kinematics, and Jacobians
for the most common robot designs.
* Detailed coverage of vision and visual servo control enables you
to program robots to manipulate objects sensed by cameras.
* An entire chapter on dynamics prepares you to compute the
dynamics of the most common manipulator designs.
* The most common motion planning and trajectory generation
algorithms are presented in an elementary style.
* The comprehensive treatment of motion and force control includes
both basic and advanced methods.
* The text's treatment of geometric nonlinear control is more
readable than in more advanced texts.
* Many worked examples and an extensive list of problems illustrate
all aspects of the theory.
About the authors
Mark W. Spong is Donald Biggar Willett Professor of Engineering at
the University of Illinois at Urbana-Champaign. Dr. Spong is the
2005 President of the IEEE Control Systems Society and past
Editor-in-Chief of the IEEE Transactions on Control Systems
Technology.
Seth Hutchinson is currently a Professor at the University of
Illinois in Urbana-Champaign, and a senior editor of the IEEE
Transactions on Robotics and Automation. He has published
extensively on the topics of robotics and computer vision.
Mathukumalli Vidyasagar is currently Executive Vice President in
charge of Advanced Technology at Tata Consultancy Services (TCS),
India's largest IT firm. Dr. Vidyasagar was formerly the director
of the Centre for Artificial Intelligence and Robotics (CAIR),
under Government of India's Ministry of Defense.
This book explores important aspects of Markov and hidden Markov
processes and the applications of these ideas to various problems
in computational biology. The book starts from first principles, so
that no previous knowledge of probability is necessary. However,
the work is rigorous and mathematical, making it useful to
engineers and mathematicians, even those not interested in
biological applications. A range of exercises is provided,
including drills to familiarize the reader with concepts and more
advanced problems that require deep thinking about the theory.
Biological applications are taken from post-genomic biology,
especially genomics and proteomics.
The topics examined include standard material such as the
Perron-Frobenius theorem, transient and recurrent states, hitting
probabilities and hitting times, maximum likelihood estimation, the
Viterbi algorithm, and the Baum-Welch algorithm. The book contains
discussions of extremely useful topics not usually seen at the
basic level, such as ergodicity of Markov processes, Markov Chain
Monte Carlo (MCMC), information theory, and large deviation theory
for both i.i.d and Markov processes. The book also presents
state-of-the-art realization theory for hidden Markov models. Among
biological applications, it offers an in-depth look at the BLAST
(Basic Local Alignment Search Technique) algorithm, including a
comprehensive explanation of the underlying theory. Other
applications such as profile hidden Markov models are also
explored.
This self-contained introduction to practical robot kinematics and dynamics includes a comprehensive treatment of robot control. Provides background material on terminology and linear transformations, followed by coverage of kinematics and inverse kinematics, dynamics, manipulator control, robust control, force control, use of feedback in nonlinear systems, and adaptive control. Each topic is supported by examples of specific applications. Derivations and proofs are included in many cases. Includes many worked examples, examples illustrating all aspects of the theory, and problems.
Compressed sensing is a relatively recent area of research that
refers to the recovery of high-dimensional but low-complexity
objects from a limited number of measurements. The topic has
applications to signal/image processing and computer algorithms,
and it draws from a variety of mathematical techniques such as
graph theory, probability theory, linear algebra, and optimization.
The author presents significant concepts never before discussed as
well as new advances in the theory, providing an in-depth
initiation to the field of compressed sensing. An Introduction to
Compressed Sensing contains substantial material on graph theory
and the design of binary measurement matrices, which is missing in
recent texts despite being poised to play a key role in the future
of compressed sensing theory. It also covers several new
developments in the field and is the only book to thoroughly study
the problem of matrix recovery. The book supplies relevant results
alongside their proofs in a compact and streamlined presentation
that is easy to navigate. The core audience for this book is
engineers, computer scientists, and statisticians who are
interested in compressed sensing. Professionals working in image
processing, speech processing, or seismic signal processing will
also find the book of interest.
|
You may like...
Loot
Nadine Gordimer
Paperback
(2)
R398
R330
Discovery Miles 3 300
Loot
Nadine Gordimer
Paperback
(2)
R398
R330
Discovery Miles 3 300
|