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Introduction to Autonomous Robots: Mechanisms, Sensors, Acutators, and Algorithms

(Hardback)


Publishing Details

Full Title:

Introduction to Autonomous Robots: Mechanisms, Sensors, Acutators, and Algorithms

Contributors:

By (Author) Nikolaus Correll
By (author) Christopher Heckman

ISBN:

9780262047555

Publisher:

MIT Press Ltd

Imprint:

MIT Press

Publication Date:

7th February 2023

Country:

United States

Classifications

Readership:

General

Fiction/Non-fiction:

Non Fiction

Dewey:

629.8932

Physical Properties

Physical Format:

Hardback

Number of Pages:

360

Dimensions:

Width 178mm, Height 229mm

Description

A comprehensive introduction to the field of autonomous robotics aimed at upper-level undergraduates and offering additional online resources. Textbooks that provide a broad algorithmic perspective on the mechanics and dynamics of robots almost unfailingly serve students at the graduate level. Introduction to Autonomous Robots offers a much-needed resource for teaching third- and fourth-year undergraduates the computational fundamentals behind the design and control of autonomous robots. The authors use a class-tested and accessible approach to present progressive, step-by-step development concepts, alongside a wide range of real-world examples and fundamental concepts in mechanisms, sensing and actuation, computation, and uncertainty. Throughout, the authors balance the impact of hardware (mechanism, sensor, actuator) and software (algorithms) in teaching robot autonomy. Features- Rigorous and tested in the classroomWritten for engineering and computer science undergraduates with a sophomore-level understanding of linear algebra, probability theory, trigonometry, and statisticsQR codes in the text guide readers to online lecture videos and animationsTopics include- basic concepts in robotic mechanisms like locomotion and grasping, plus the resulting forces; operation principles of sensors and actuators; basic algorithms for vision and feature detection; an introduction to artificial neural networks, including convolutional and recurrent variantsExtensive appendices focus on project-based curricula, pertinent areas of mathematics, backpropagation, writing a research paper, and other topicsA growing library of exercises in an open-source, platform-independent simulation (Webots)

Author Bio

Nikolaus Correll is Associate Professor of Computer Science at the University of Colorado Boulder. Bradley Hayes is Assistant Professor of Computer Science at the University of Colorado Boulder. Christoffer Heckman is Assistant Professor of Computer Science at the University of Colorado Boulder. Alessandro Roncone is Assistant Professor of Computer Science at the University of Colorado Boulder.

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