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Semester 1

Introduction to Mobile Robotics (INFR10085)







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Enrolled students are assumed to have:Experience of AI knowledge and representation issues (equivalent to first and second year courses in Informatics);Enough school algebra and geometry (e.g., vectors, rotations, trigonometry etc.).Essential probability theory. Physics to understand Newton's Laws of Motion. They are also expected to be familiar with these mathematical methods: Bayes rule, Gaussian Distribution, Covariance matrices. In addition, students should be comfortable with programming in using Python (or C++) and familiar with Linux systems that are heavily needed for the practical and coursework.

Course Summary

A mobile robot is a machine controlled by software that use sensors and other technology to identify its surroundings and move around its environment. This course provides a general understanding of mobile robotics and related concepts, covering topics such as sensing perception, motion control, and planning. The emphasis is on algorithms, probabilistic reasoning, optimization, inference mechanisms, and behavior strategies, as opposed to electromechanical systems de-sign. Practically useful tools and simulators for developing real robotic systems will also be covered in this course. In the end of the course, students will develop develop sufficient skills in the analysis of predominant mobile robots, being able to understand the perception and navigation system for a self-driving car.

Course Description

Delivery Method:The course will be delivered through a combination of: (1) live lectures, (2) practical labs, (3) tutorials, and (4) an online discussion forum.Content/Syllabus:The exact set of methods and algorithms explored in the course will vary slightly from year to year,but will include many of the following topics:- Introduction of Robotics: concept, use cases, and system architecture on sensing, perception & control. Ethical and privacy implication of robots.- Math refresher: basic operations of matrix, algebra, probability theory, derivatives.- Robot Motion Model: Coordinate transformations and Representation of Rotations; Forward kinematics.- Sensor Model and Measurement: Proprioceptive and exteroceptive models; a case study with cameras, lidar, radar, ultrasonic, inertia etc.- Recursive State Estimation: Kalman filters, EKF etc. - Localization & Tracking: Monte Carlo Localization, Ranging based Triangulation, Fingerprinting etc.- Mapping: environment model, grid map.- Robot Operating System: basic principles, use cases, and examples.- SLAM: Framework & systems, loop closing, pose graph optimization.- Planning and Navigation: Obstacle avoidance, Path planning, receding horizon control.- Self-driving Car Development Platform: Basic understanding of usage of CARLA like platform in sensing, perception and navigation.- Basic Control Theory for Robotics: Open-loop and closed-loop control. Basic Idea on PID control.

Assessment Information

Written Exam 60%, Coursework 40%, Practical Exam 0%

Additional Assessment Information

Coursework will involve comparing and evaluating the methods discussed in the course using the taught ROS and CARLA simulation software. Short written explanations along and discussion will also be evaluated as part of the coursework.Non-assessed quizzes and example questions will also be utilized to help students better understand the course material. Feedback for the quizzes will be immediate, and feedback for example questions will be provided from the instructors or via peer discussion.

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