Intelligent Control of Robotic Systems [Hardcover]

This book illustrates basic principles, along with the development of the advanced algorithms, to realize smart robotic systems. It speaks to strategies by which a robot (manipulators, mobile robot, quadrotor) can learn its own kinematics and dynamics from data. In this context, two major issues have been dealt with; namely, stability of the systems and experimental validations. Learning algorithms and techniques as covered in this book easily extend to other robotic systems as well. The book contains MATLAB- based examples and c-codes under robot operating systems (ROS) for experimental validation so that readers can replicate these algorithms in robotics platforms.

1. Introduction Part 1: Manipulators 2. Kinematic and Dynamic Models of Robot Manipulators 3. Hand-eye Coordination of a Robotic Arm using KSOM Network 4. Model-based Visual Servoing of a 7 DOF Manipulator 5. Learning-Based Visual Servoing 6. Visual Servoing using an Adaptive Distributed Takagi-Sugeno (T-S) Fuzzy Model 7. Kinematic Control using Single Network Adaptive Critic 8. Dynamic Control using Single Network Adaptive Critic 9. Imitation Learning 10. Visual Perception 11. Vision-Based Grasping 12. Warehouse Automation: An Example Part 2: Mobile Robotics 13. Introduction to Mobile Robotics and Control 14. Multi-robot Formation 15. Event Triggered Multi-Robot Consensus 16. Human Tracking Algorithm using SURF Based Dynamic Object Model. Exercises. Bibliography. Index.

Book focusses on development of advanced algorithms to realize smart robotic systems with strategies by which a robot can learn its own kinematics and dynamics from data, discussing stability of the systems and experimental validations.  The book contains MATLAB based examples.Laxmidhar Behera, Swagat Kumar, Prem Kumar Patchaikani, Ranjith Ravindranathan Nair, Samrat DuttaUS? 2020 by Taylor & Francis Group, LLC