book

A Systematic Approach to Learning Robot Programming with ROS

Name: A Systematic Approach to Learning Robot Programming with ROS
Author: Wyatt Newman
ISBN: 9781498777872

by Wyatt Newman

September 2017

Intermediate to advanced

530 pages

15h 8m

English

Chapman and Hall/CRC

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Cover
Halftitle
Title
Copyright
Table of Contents
List of Figures
Preface
Acknowledgments
Author
Section I: ROS Foundations

Chapter 1: Introduction to ROS: ROS tools and nodes
1.1 Some ROS concepts1.2 Writing ROS nodes1.2.1 Creating ROS packages1.2.2 Writing a minimal ROS publisher1.2.3 Compiling ROS nodes1.2.4 Running ROS nodes1.2.5 Examining running minimal publisher node1.2.6 Scheduling node timing1.2.7 Writing a minimal ROS subscriber1.2.8 Compiling and running minimal subscriber1.2.9 Minimal subscriber and publisher node summary1.3 More ROS tools: catkin_simple, roslaunch, rqt_console, and rosbag1.3.1 Simplifying CMakeLists.txt with catkin_simple1.3.2 Automating starting multiple nodes1.3.3 Viewing output in a ROS console1.3.4 Recording and playing back data with rosbag1.4 Minimal simulator and controller example1.5 Wrap-up
Chapter 2: Messages, Classes and Servers
2.1 Defining custom messages2.1.1 Defining a custom message2.1.2 Defining a variable-length message2.2 Introduction to ROS services2.2.1 Service messages2.2.2 ROS service nodes2.2.3 Manual interaction with ROS services2.2.4 Example ROS service client2.2.5 Running example service and client2.3 Using C++ classes in ROS2.4 Creating library modules in ROS2.5 Introduction to action servers and action clients2.5.1 Creating an action server package2.5.2 Defining custom action-server messages2.5.3 Designing an action client2.5.4 Running the example code2.6 Introduction to parameter server2.7 Wrap-Up
Section II: Simulation and Visualization in ROS
Chapter 3: Simulation in ROS
3.1 Simple two-dimensional robot simulator3.2 Modeling for dynamic simulation3.3 Unified robot description format3.3.1 Kinematic model3.3.2 Visual model3.3.3 Dynamic model3.3.4 Collision model3.4 Introduction to Gazebo3.5 Minimal joint controller3.6 Using Gazebo plug-in for joint servo control3.7 Building mobile-robot model3.8 Simulating mobile-robot model3.9 Combining robot models3.10 Wrap-Up
Chapter 4: Coordinate Transforms in ROS
4.1 Introduction to coordinate transforms in ROS4.2 Transform listener4.3 Using Eigen library4.4 Transforming ROS datatypes4.5 Wrap-Up
Chapter 5: Sensing and Visualization in ROS
5.1 Markers and interactive markers in rviz5.1.1 Markers in rviz5.1.2 Triad display example5.1.3 Interactive markers in rviz5.2 Displaying sensor values in rviz5.2.1 Simulating and displaying LIDAR5.2.2 Simulating and displaying color-camera data5.2.3 Simulating and displaying depth-camera data5.2.4 Selection of points in rviz5.3 Wrap-Up
Section III: Perceptual Processing in ROS
Chapter 6: Using Cameras in ROS
6.1 Projective transformation into camera coordinates6.2 Intrinsic camera calibration6.3 Intrinsic calibration of stereo cameras6.4 Using OpenCV with ROS6.4.1 Example OpenCV: finding colored pixels6.4.2 Example OpenCV: finding edges6.5 Wrap-Up
Chapter 7: Depth Imaging and Point Clouds
7.1 Depth from scanning LIDAR7.2 Depth from stereo cameras7.3 Depth cameras7.4 Wrap-Up
Chapter 8: Point Cloud Processing
8.1 Simple point-cloud display node8.2 Loading and displaying point-cloud images from disk8.3 Saving published point-cloud images to disk8.4 Interpreting point-cloud images with PCL methods8.5 Object finder8.6 Wrap-Up
Section IV: Mobile Robots in ROS
Chapter 9: Mobile-Robot Motion Control
9.1 Desired state generation9.1.1 From paths to trajectories9.1.2 A trajectory builder library9.1.3 Open-loop control9.1.4 Desired state publishing9.2 Robot state estimation9.2.1 Getting model state from Gazebo9.2.2 Odometry9.2.3 Combining odometry, GPS and inertial sensing9.2.4 Combining odometry and LIDAR9.3 Differential-drive steering algorithms9.3.1 Robot motion model9.3.2 Linear steering of a linear robot9.3.3 Linear steering of a non-linear robot9.3.4 Non-linear steering of a non-linear robot9.3.5 Simulating non-linear steering algorithm9.4 Steering with respect to map coordinates9.5 Wrap-Up
Chapter 10: Mobile-Robot Navigation
10.1 Map making10.2 Path planning10.3 Example move-base client10.4 Modifying navigation stack10.5 Wrap-Up
Section V: Robot Arms in ROS
Chapter 11: Low-Level Control
11.1 A one-DOF prismatic-joint robot model11.2 Example position controller11.3 Example velocity controller11.4 Example force controller11.5 Trajectory messages for robot arms11.6 Trajectory interpolation action server for a seven-DOF arm11.7 Wrap-Up
Chapter 12: Robot Arm Kinematics
12.1 Forward kinematics12.2 Inverse kinematics12.3 Wrap-Up
Chapter 13: Arm Motion Planning
13.1 Cartesian motion planning13.2 Dynamic programming for joint-space planning13.3 Cartesian-motion action servers13.4 Wrap-Up
Chapter 14: Arm Control with Baxter Simulator
14.1 Running Baxter simulator14.2 Baxter joints and topics14.3 Baxter’s grippers14.4 Head pan control14.5 Commanding Baxter joints14.6 Using ROS joint trajectory controller14.7 Joint-space record and playback nodes14.8 Baxter kinematics14.9 Baxter Cartesian moves14.10 Wrap-Up
Chapter 15: An Object-Grabber Package
15.1 Object-grabber code organization15.2 Object manipulation query service15.3 Generic gripper services15.4 Object-grabber action server15.5 Example object-grabber action client15.6 Wrap-Up
Section VI: System Integration and Higher Level Control
Chapter 16: PerceptionBased Manipulation
16.1 Extrinsic camera calibration16.2 Integrated perception and manipulation16.3 Wrap-Up
Chapter 17: Mobile Manipulation
17.1 Mobile manipulator model17.2 Mobile manipulation17.3 Wrap-Up
Chapter 18: Conclusion
Bibliography
Index

Content preview from A Systematic Approach to Learning Robot Programming with ROS

Arm Control with Baxter Simulator

14.1 Running Baxter simulator

14.2 Baxter joints and topics

14.3 Baxter’s grippers

14.4 Head pan control

14.5 Commanding Baxter joints

14.6 Using ROS joint trajectory controller

14.7 Joint-space record and playback nodes

14.8 Baxter kinematics

14.9 Baxter Cartesian moves

14.10 Wrap-Up

INTRODUCTION

In the context of learning about control of robot arms, the Baxter robot simulator, which runs in Gazebo, is helpful. The Baxter simulator, composed by the manufacturer ReThink Robotics, is a reasonably faithful model of the robot. The simulator and the physical robot have identical ROS interfaces, so programs developed under simulation can be ported quickly to the corresponding physical robot. ...

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A Systematic Approach to Learning Robot Programming with ROS

by Wyatt Newman

CHAPTER 14

Arm Control with Baxter Simulator