674 Pages 702 B/W Illustrations
    by CRC Press

    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.


    Laxmidhar Behera, Swagat Kumar, Prem Kumar Patchaikani, Ranjith Ravindranathan Nair, Samrat Dutta