A Systematic Approach to Learning Robot Programming with ROS
Textbook – 502 Pages – 50 Color Illustrations
ISBN 9781498777827 – CAT# K29786
- Teaches ROS from the ground up without dependence on detailed prior expertise
- Introduces the reader to ROS incrementally, building on a coherent sequence of topics
- Assumes rudimentary knowledge of C++, but does not assume expertise in details of programming, robotics, machine vision, kinematics/dynamics, or software engineering
- Presents ROS programming together with Gazebo simulations
- Provides a survey of robotics topics, including kinematics, machine vision, point-cloud processing, mapping, navigation, and user interfaces
A Systematic Approach to Learning Robot Programming with ROS provides a comprehensive, introduction to the essential components of ROS through detailed explanations of simple code examples along with the corresponding theory of operation. The book explores the organization of ROS, how to understand ROS packages, how to use ROS tools, how to incorporate existing ROS packages into new applications, and how to develop new packages for robotics and automation. It also facilitates continuing education by preparing the reader to better understand the existing on-line documentation.
The book is organized into six parts. It begins with an introduction to ROS foundations, including writing ROS nodes and ROS tools. Messages, Classes, and Servers are also covered. The second part of the book features simulation and visualization with ROS, including coordinate transforms.
The next part of the book discusses perceptual processing in ROS. It includes coverage of using cameras in ROS, depth imaging and point clouds, and point cloud processing. Mobile robot control and navigation in ROS is featured in the fourth part of the book
The fifth section of the book contains coverage of robot arms in ROS. This section explores robot arm kinematics, arm motion planning, arm control with the Baxter Simulator, and an object-grabber package. The last part of the book focuses on system integration and higher-level control, including perception-based and mobile manipulation.
This accessible text includes examples throughout and C++ code examples are also provided at https://github.com/wsnewman/learning_ros
Wyatt Newman is a professor in the department of Electrical Engineering and Computer Science at Case Western Reserve University, where he has taught since 1988. His research is in the areas of mechatronics, robotics and computational intelligence, in which he has 12 patents and over 150 technical publications. He received the S.B. degree from Harvard College in Engineering Science, the S.M. degree in Mechanical Engineering from M.I.T. in thermal and fluid sciences, the M.S.E.E. degree from Columbia University in control theory and network theory, and the Ph.D. degree in Mechanical Engineering from M.I.T. in design and control.
A former NSF Young Investigator in robotics, Prof. Newman has also held appointments as: a senior member of research staff, Philips Laboratories; visiting scientist at Philips Natuurkundig Laboratorium; visiting faculty at Sandia National Laboratories, Intelligent Systems and Robotics Center; NASA summer faculty fellow at NASA Glenn Research Center; visiting fellow in neuroscience at Princeton University; distinguished visiting fellow at Edinburgh University, School of Informatics, and the Hung Hing Ying Distinguished Visiting Professor at the University of Hong Kong. Prof. Newman led robotics teams competing in the 2007 DARPA Urban Challenge and in the 2015 DARPA Robotics Challenge, and he continues to be interested in wide-ranging aspects and applications of robotics.