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Intelligent Systems and Robotics
Intelligent Systems and Robotics focuses on new developments in robotics and intelligent systems and provides insight, guidance and specific techniques vital to those concerned with the design and implementation of robotics and intelligent system applications. Intelligent Systems and Robotics presents information on a 3-D vision for robots and intelligent control of a vision-based reasoning system with a robot manipulator. The reader will find authoritative presentations on autonomous land vehicle navigation, manipulator reachable workspace problems and the formulation of algorithms for their solution. Covered are methods for medical applications utilizing expert adaptive control, the integrated piezoelectric sensor/actuator design for distributed identification and control of smart machines, including theory, experiments, finite element formulation and analysis. Automatic repair of aircraft transparencies and geometric modeling utilized in robot task planning as well as the evaluation of standard fieldbus networks utilized in the factory environment are presented.
R 11,163.97
Robot Learning Human Skills and Intelligent Control Design
In the last decades robots are expected to be of increasing intelligence to deal with a large range of tasks. Especially, robots are supposed to be able to learn manipulation skills from humans. To this end, a number of learning algorithms and techniques have been developed and successfully implemented for various robotic tasks. Among these methods, learning from demonstrations (LfD) enables robots to effectively and efficiently acquire skills by learning from human demonstrators, such that a robot can be quickly programmed to perform a new task.This book introduces recent results on the development of advanced LfD-based learning and control approaches to improve the robot dexterous manipulation. First, there's an introduction to the simulation tools and robot platforms used in the authors' research. In order to enable a robot learning of human-like adaptive skills, the book explains how to transfer a human user’s arm variable stiffness to the robot, based on the online estimation from the muscle electromyography (EMG). Next, the motion and impedance profiles can be both modelled by dynamical movement primitives such that both of them can be planned and generalized for new tasks. Furthermore, the book introduces how to learn the correlation between signals collected from demonstration, i.e., motion trajectory, stiffness profile estimated from EMG and interaction force, using statistical models such as hidden semi-Markov model and Gaussian Mixture Regression. Several widely used human-robot interaction interfaces (such as motion capture-based teleoperation) are presented, which allow a human user to interact with a robot and transfer movements to it in both simulation and real-word environments. Finally, improved performance of robot manipulation resulted from neural network enhanced control strategies is presented. A large number of examples of simulation and experiments of daily life tasks are included in this book to facilitate better understanding of the readers.
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Human-Robot Interaction Control Using Reinforcement Learning
A comprehensive exploration of the control schemes of human-robot interactions In Human-Robot Interaction Control Using Reinforcement Learning, an expert team of authors delivers a concise overview of human-robot interaction control schemes and insightful presentations of novel, model-free and reinforcement learning controllers. The book begins with a brief introduction to state-of-the-art human-robot interaction control and reinforcement learning before moving on to describe the typical environment model. The authors also describe some of the most famous identification techniques for parameter estimation. Human-Robot Interaction Control Using Reinforcement Learning offers rigorous mathematical treatments and demonstrations that facilitate the understanding of control schemes and algorithms. It also describes stability and convergence analysis of human-robot interaction control and reinforcement learning based control. The authors also discuss advanced and cutting-edge topics, like inverse and velocity kinematics solutions, H2 neural control, and likely upcoming developments in the field of robotics. Readers will also enjoy: A thorough introduction to model-based human-robot interaction control Comprehensive explorations of model-free human-robot interaction control and human-in-the-loop control using Euler angles Practical discussions of reinforcement learning for robot position and force control, as well as continuous time reinforcement learning for robot force control In-depth examinations of robot control in worst-case uncertainty using reinforcement learning and the control of redundant robots using multi-agent reinforcement learning Perfect for senior undergraduate and graduate students, academic researchers, and industrial practitioners studying and working in the fields of robotics, learning control systems, neural networks, and computational intelligence, Human-Robot Interaction Control Using Reinforcement Learning is also an indispensable resource for students and professionals studying reinforcement learning.
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Intelligent Planning for Mobile Robotics
Intelligent Planning for Mobile Robotics: Algorithmic Approaches presents content coverage on the basics of artificial intelligence, search problems, and soft computing approaches. This collection of research provides insight on both robotics and basic algorithms and could serve as a reference book for courses related to robotics, special topics in AI, planning, applied soft computing, applied AI, and applied evolutionary computing. It is an ideal choice for research students, scholars, and professors alike.
R 7,557.58
R 489.00
Advanced Engineering and Computational Methodologies for Intelligent Mechatronics and Robotics
The emergence of mechatronics has advanced the engineering disciplines, producing a plethora of useful technical systems. Advanced Engineering and Computational Methodologies for Intelligent Mechatronics and Robotics presents the latest innovations and technologies in the fields of mechatronics and robotics. These innovations are applied to a wide range of applications for robotic-assisted manufacturing, complex systems, and many more. This publication is essential to bridge the gap between theory and practice for researchers, engineers, and practitioners from academia to government.
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