![Introduction to Autonomous Robots: Mechanisms, Sensors, Actuators, and Algorithms](/uploads/posts/2022-12/1671465178_6771_introduction_to_autonomous_robots__m_chanisms__s_nsors__actuators__and_algorithms.jpg)
Автор: Nikolaus Correll, Bradley Hayes, Christoffer Heckman, Alessandro Roncone
Издательство: The MIT Press
Год: 2022
Страниц: 268
Язык: английский
Формат: epub (true)
Размер: 13.8 MB
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. Artificial neural networks (ANNs) are part of a class of machine learning techniques that are loosely inspired by neural operation in the human brain; in robotics, they are generally used to classify or regress data for the dual purposes of perception and control.