Written by two of Europe's leading robotics experts, this book provides the tools for a unified approach to the modelling of robotic manipulators, whatever their mechanical structure. No other publication covers the three fundamental issues of robotics: modelling, identification and control. It covers the development of various mathematical models required for the control and simulation of robots.·World class authority·Unique range of coverage not available in any other book·Provides a complete course on robotic control at an undergraduate and graduate level
As the use and relevance of robotics for countless scientific purposes grows all the time, research into the many diverse elements of the subject becomes ever more important and in demand. This volume examines in depth the most topical, complex issues of modelling and identification in robotics. The book is divided into three main parts. The !first part is devoted to robot dynamics modelling and identification of robot and load parameters, incorporating friction torques, discussing identification schemes, and presenting simulations and experiment al results of robot and load dynamic parameters identification. A general concept of robot programming language for research and educational purposes is examined and there is a detailed outline of its basic structures along with hardware requirements, which both constitute an open robot controller architecture. Finally a hybrid controller is derived, and several experimental results of this system are outlined. This impressive discussion of the topic covers both the theoretical and practical, illustrated throughout by examples and experimental results, and will be of value to anyone researching or practising within the field of robotics, automation and system i dentification or to control engineers.
The objective of this dissertation is to advance the state-of-the-art in the kinematic modeling, identification, and control of robotic manipulators with rigid links in an effort to improve robot kinematic performance. The positioning accuracy of commercially-available industrial robotic manipulators depends upon a kinematic model which describes the robot geometry in a parametric form. Manufacturing error in the machining and assembly of manipulators lead to discrepancies between the design parameters and the physical structure. Improving the kinematic perfor mance thus requires the identification of the actual kinematic parameters of each individual robot. The identified kinematic parameters are referred to as the arm signature. Existing robot kinematic models, such as the Denavit-Hartenberg model, are not directly applicable to kinematic parameter identification. In this dissertation we introduce a new kinematic model, called the 5-Model, which is applicable to kinematic parameter identification, and use it as the foundation for our development of a general technique for identifying the kinematic parameters of any robot with rigid links.
Based on the successful Modelling and Control of Robot Manipulators by Sciavicco and Siciliano (Springer, 2000), Robotics provides the basic know-how on the foundations of robotics: modelling, planning and control. It has been expanded to include coverage of mobile robots, visual control and motion planning. A variety of problems is raised throughout, and the proper tools to find engineering-oriented solutions are introduced and explained. The text includes coverage of fundamental topics like kinematics, and trajectory planning and related technological aspects including actuators and sensors. To impart practical skill, examples and case studies are carefully worked out and interwoven through the text, with frequent resort to simulation. In addition, end-of-chapter exercises are proposed, and the book is accompanied by an electronic solutions manual containing the MATLAB® code for computer problems; this is available free of charge to those adopting this volume as a textbook for courses.
Fundamental and technological topics are blended uniquely and developed clearly in nine chapters with a gradually increasing level of complexity. A wide variety of relevant problems is raised throughout, and the proper tools to find engineering-oriented solutions are introduced and explained, step by step. Fundamental coverage includes: Kinematics; Statics and dynamics of manipulators; Trajectory planning and motion control in free space. Technological aspects include: Actuators; Sensors; Hardware/software control architectures; Industrial robot-control algorithms. Furthermore, established research results involving description of end-effector orientation, closed kinematic chains, kinematic redundancy and singularities, dynamic parameter identification, robust and adaptive control and force/motion control are provided. To provide readers with a homogeneous background, three appendices are included on: Linear algebra; Rigid-body mechanics; Feedback control. To acquire practical skill, more than 50 examples and case studies are carefully worked out and interwoven through the text, with frequent resort to simulation. In addition, more than 80 end-of-chapter exercises are proposed, and the book is accompanied by a solutions manual containing the MATLAB code for computer problems; this is available from the publisher free of charge to those adopting this work as a textbook for courses.
This self-contained introduction to practical robot kinematics and dynamics includes a comprehensive treatment of robot control. It provides background material on terminology and linear transformations, followed by coverage of kinematics and inverse kinematics, dynamics, manipulator control, robust control, force control, use of feedback in nonlinear systems, and adaptive control. Each topic is supported by examples of specific applications. Derivations and proofs are included in many cases. The book includes many worked examples, examples illustrating all aspects of the theory, and problems.
This estimation reference text thoroughly describes matrix factorization methods successfully employed by numerical analysts, familiarizing readers with the techniques that lead to efficient, economical, reliable, and flexible estimation algorithms. Topics include a review of least squares data processing and the Kalman filter algorithm; positive definite matrices, the Cholesky decomposition, and some of their applications; Householder orthogonal transformations; sequential square root data processing; mapping effects and process noise; biases and correlated process noise; and covariance analysis of effects due to mismodeled variables and incorrect filter a priori statistics. The concluding chapters explore SRIF error analysis of effects due to mismodeled variables and incorrect filter a priori statistics as well as square root information smoothing. Geared toward advanced undergraduates and graduate students, this pragmatically oriented and detailed presentation is also a useful reference, featuring numerous helpful appendixes throughout the text.
Deep Learning for Robot Perception and Cognition introduces a broad range of topics and methods in deep learning for robot perception and cognition together with end-to-end methodologies. The book provides the conceptual and mathematical background needed for approaching a large number of robot perception and cognition tasks from an end-to-end learning point-of-view. The book is suitable for students, university and industry researchers and practitioners in Robotic Vision, Intelligent Control, Mechatronics, Deep Learning, Robotic Perception and Cognition tasks. - Presents deep learning principles and methodologies - Explains the principles of applying end-to-end learning in robotics applications - Presents how to design and train deep learning models - Shows how to apply deep learning in robot vision tasks such as object recognition, image classification, video analysis, and more - Uses robotic simulation environments for training deep learning models - Applies deep learning methods for different tasks ranging from planning and navigation to biosignal analysis
This book discusses the latest developmens in modelling, simulation and control of flexible robot manipulators. Coverage includes an overall review of previously developed methodologies, a range of modelling approaches including classical techniques, parametric and neuromodelling approaches and numerical modelling/simulation techniques.
