Robotics, Vision and Control Fundamental Algorithms in MATLAB®
Produktdetaljer
Biographical note
Peter Corke is a robotics educator and researcher. He is known for his work in vision-based control and field robotics, as well as open-source robotics software and educational resources such as the QUT Robot Academy. He is a distinguished professor at the Queensland University of Technology, technical advisor to several robotics companies, and has been a consultant to MathWorks. He was the Australian University Teacher of the Year in 2017 and is a fellow of the IEEE, the Australian Academy of Technology and Engineering, and the Australian Academy of Science. He received his PhD from the University of Melbourne.
Witold Jachimczyk manages the computer vision and automated driving development teams at MathWorks. He worked on the Image Processing Toolbox(™) prior to leading the development of Computer Vision Toolbox(™) and Automated Driving Toolbox(™). Before MathWorks, he worked at Data Translation developing machine vision software and at Polaroid on embedded systems. Witold holds a B.S. and an M.S. in electrical engineering from Worcester Polytechnic Institute.
Remo Pillat is the MathWorks development manager for the robotics and autonomous systems team, which focuses on providing a set of robust algorithms and simulation tools for autonomous ground vehicles, robot manipulators, and UAVs. Prior to joining MathWorks, Remo’s research at the University of Central Florida (UCF) focused on multisensor fusion, state estimation, and environment modeling for autonomous ground vehicles. He was the chief software engineer for UCF’s self-driving car and responsible for vehicle control as well as real-time obstacle detection and tracking.
This textbook provides a comprehensive, but tutorial, introduction to robotics, computer vision, and control. It is written in a light but informative conversational style, weaving text, figures, mathematics, and lines of code into a cohesive narrative. Over 1600 code examples show how complex problems can be decomposed and solved using just a few simple lines of code.
This edition is based on MATLAB® and a number of MathWorks® toolboxes. These provide a set of supported software tools for addressing a broad range of applications in robotics and computer vision. These toolboxes enable the reader to easily bring the algorithmic concepts into practice and work with real, non-trivial, problems. For the beginning student, the book makes the algorithms accessible, the toolbox code can be read to gain understanding, and the examples illustrate how it can be used. The code can also be the starting point for new work, for practitioners, students, or researchers, by writing programs based on toolbox functions. Two co-authors from MathWorks have joined the writing team and bring deep knowledge of these MATLAB toolboxes and workflows.
This edition is based on MATLAB® and a number of MathWorks® toolboxes. The code can also be the starting point for new work, for practitioners, students, or researchers, by writing programs based on toolbox functions.
Introduction.-Foundations: Representing Position and Orientation.- Time and Motion.- Mobile Robotics: Mobile Robot Vehicles.- Navigation.- Localization and Mapping.- Robot Manipulators: Robot Arm Kinematics.- Manipulator Velocity.- Dynamics and Control.- Computer Vision: Light and Color.- Images and Image Processing,- Image Feature Extraction,- Image Formation.- Using Multiple Images.- Installing the Toolboxes.- Linear Algebra.- Geometry.- Lie Groups and Algebras.
This edition is based on MATLAB® and a number of MathWorks® toolboxes. These provide a set of supported software tools for addressing a broad range of applications in robotics and computer vision. These toolboxes enable the reader to easily bring the algorithmic concepts into practice and work with real, non-trivial, problems. For the beginning student, the book makes the algorithms accessible, the toolbox code can be read to gain understanding, and the examples illustrate how it can be used. The code can also be the starting point for new work, for practitioners, students, or researchers, by writing programs based on toolbox functions. Two co-authors from MathWorks have joined the writing team and bring deep knowledge of these MATLAB toolboxes and workflows.
Serves as tutorial introduction, now in a completely revised, extended, and updated third edition
Includes a lot of Matlab examples and figures and is written in a light but informative style, easy-to-read, and absorb
Explains how to choose right algorithm to decompose and solve complex problems, using just a few simple lines of code
Produktdetaljer
Biographical note
Peter Corke is a robotics educator and researcher. He is known for his work in vision-based control and field robotics, as well as open-source robotics software and educational resources such as the QUT Robot Academy. He is a distinguished professor at the Queensland University of Technology, technical advisor to several robotics companies, and has been a consultant to MathWorks. He was the Australian University Teacher of the Year in 2017 and is a fellow of the IEEE, the Australian Academy of Technology and Engineering, and the Australian Academy of Science. He received his PhD from the University of Melbourne.
Witold Jachimczyk manages the computer vision and automated driving development teams at MathWorks. He worked on the Image Processing Toolbox(™) prior to leading the development of Computer Vision Toolbox(™) and Automated Driving Toolbox(™). Before MathWorks, he worked at Data Translation developing machine vision software and at Polaroid on embedded systems. Witold holds a B.S. and an M.S. in electrical engineering from Worcester Polytechnic Institute.
Remo Pillat is the MathWorks development manager for the robotics and autonomous systems team, which focuses on providing a set of robust algorithms and simulation tools for autonomous ground vehicles, robot manipulators, and UAVs. Prior to joining MathWorks, Remo’s research at the University of Central Florida (UCF) focused on multisensor fusion, state estimation, and environment modeling for autonomous ground vehicles. He was the chief software engineer for UCF’s self-driving car and responsible for vehicle control as well as real-time obstacle detection and tracking.