Peer Review

Introduction to Autonomous Robots

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Reviewed:: Sep 8, 2017 by Computer Science

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Overview:: This book introduces concepts in mobile, autonomous robotics to 3rd-4th year students in Computer Science or a related discipline. The book covers principles of robot motion, forward and inverse kinematics of robotic arms and simple wheeled platforms, perception, error propagation, localization and simultaneous localization and mapping. The cover picture shows a wind-up toy that is smart enough to not fall off a table just using intelligent mechanism design and illustrate the importance of the mechanism in designing intelligent, autonomous systems. This book is open source, open to contributions, and released under a creative common license.
Type of Material:: Open (Access) Textbook
Recommended Uses:: This book introduces concepts in mobile, autonomous robotics to 3rd-4th year students in Computer Science or a related discipline. This could be best used as a support material in lectures.
Technical Requirements:: - A web browser - Any PDF reader
Identify Major Learning Goals:: This book provides an algorithmic perspective to autonomous robotics to students with a sophomore-level of linear algebra and probability theory. Robotics is an emerging ﬁeld at the intersection of mechanical and electrical engineering with computer science. With computers becoming more powerful, making robots smart is getting more and more into the focus of attention and robotics research most challenging frontier. While there are a large number of textbooks on the mechanics and dynamics of robots that address sophomore-level undergraduates available, books that provide a broad algorithmic perspective are mostly limited to the graduate level. This book has therefore been developed not to create “yet another textbook, but better than the others”, but to allow the author to teach robotics to the 3rd and 4th year undergraduates at the Department of Computer Science at the University of Colorado.
Target Student Population:: The main audience is 3rd and 4th year undergraduate computer science students or a related discipline.
Prerequisite Knowledge or Skills:: A sophomore-level of linear algebra and probability theory

Content Quality

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Strengths:: Neatly organized with clear algorithms succinctly presented.

Potential Effectiveness as a Teaching Tool

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Strengths:: It will be a very useful tool for lecturers who want to teach robotics at the college level. This can also be used as self-paced learning by self-taught professionals.
Concerns:: A certain degree of knowledge background is needed, thus it's best used with teacher's guidance.

Ease of Use for Both Students and Faculty

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Strengths:: All the algorithms are explained clearly making it easy for the user to grasp the concepts fast.

Other Issues and Comments:: Some technical knowledge and abbreviations used through the text, which might be unfamiliar for readers.
Creative Commons: