Introduction to Autonomous Robots Description
This book offers students with a sophomore level of linear algebra and probability theory an algorithmic perspective on autonomous robots. At the nexus of mechanical engineering, electrical engineering, and computer science is a growing field called robotics. Making robots intelligent is becoming more and more of a focus of interest and the most difficult frontier in robotics research as computers get more powerful. While there are several textbooks available to sophomore-level undergraduates on the physics and dynamics of robots, books that offer a wide algorithmic approach are typically only available to graduate students. Therefore, this book was created to enable us to teach robotics to the third and fourth year undergraduates at the Department of Computer Science at the University of Colorado, not to build “yet another textbook, but better than the others.”
Standard AI techniques are not adequate to address situations that entail uncertainty, such as a robot’s interaction with the real world, even if they fall under the category of “Artificial Intelligence.” The kinematic equations for manipulators and mobile robots are developed in this book using basic trigonometry before path planning, sensing, and uncertainty are added. By properly defining error propagation, the robot localization problem is established. This leads to Markov localization, particle filtering, the extended kalman filter, and simultaneous localization and mapping. The emphasis of the book is on a progressive, step-by-step development of concepts through recurrent instances that capture the essence of a problem rather than on state-of-the-art solutions to a specific sub-topic. Even if the suggested solutions may not be the greatest, the community finds them to be broadly accepted and simple to understand. As motivating examples for error propagation and the Kalman filter in a localization context, odometry and line-fitting, respectively, are used to demonstrate forward kinematics and least-squares solutions.
Notably, the book expressly rejects robots, demonstrating the relevance of foundational ideas. Instead, a number of potential project-based curricula are discussed in an appendix and made publicly accessible online. These range from a maze-solving competition that can be realised with the majority of camera-equipped differential-wheel robots to manipulation experiments with a robotic arm, all of which can be completely carried out in simulation to teach the majority of the core concepts.
The Creative Commons License is in effect for this open book (CC BY-NC-ND). Free PDF ebook “Introduction to Autonomous Robots” is available for download (13.3 MB).
