Undergraduate Course: Introduction to Mobile Robotics (INFR10085)
Course Outline
| School | School of Informatics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 10 (Year 3 Undergraduate) |
Availability | Available to all students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | A mobile robot is a machine controlled by software that uses 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, computer vision (i.e., visual perception), state estimation (e.g., localisation and mapping) and motion planning. The emphasis is on algorithms, probabilistic reasoning, optimization, inference mechanisms, and behavior strategies, as opposed to electromechanical systems design. Practically useful tools and simulators for developing real robotic systems will also be covered in this course. More course information can be found on its corresponding LEARN page here: https://www.learn.ed.ac.uk/ultra/courses/_99697_1/cl/outline
At the end of the course, students will develop sufficient skills in the analysis of predominant mobile robots, being able to understand the visual 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.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | Students MUST NOT also be taking
Introduction to Vision and Robotics (INFR09019)
|
Other requirements | 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. |
Information for Visiting Students
| Pre-requisites | 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. |
| High Demand Course? |
Yes |
Course Delivery Information
|
| Academic year 2022/23, Available to all students (SV1)
|
Quota: None |
| Course Start |
Semester 1 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 17,
Seminar/Tutorial Hours 2,
Supervised Practical/Workshop/Studio Hours 3,
Feedback/Feedforward Hours 2,
Summative Assessment Hours 2,
Revision Session Hours 1,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
71 )
|
| Assessment (Further Info) |
Written Exam
60 %,
Coursework
40 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
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. |
| Feedback |
Three of the live lecture sessions will be devoted to discussing practical examples and exam-like ques-tions. We will provide feedback on student answers at the course level. Feedback at the course level will al-so be provided for the assessed and non-assessed assignments/coursework.
Piazza will be utilized for peer-feedback |
| Exam Information |
| Exam Diet |
Paper Name |
Hours & Minutes |
|
| Main Exam Diet S1 (December) | Introduction to Mobile Robotics (INFR10085) | 2:00 | |
Learning Outcomes
On completion of this course, the student will be able to:
- Students will be able to recall and explain the essential facts, concepts, principles and potential ethi-cal concerns of mobile robotics and related concepts, demonstrated through written answers in exam-ination conditions.
- Students will be able to describe and evaluate the strengths and weaknesses of some specific sensor and motor hardware; and some specific software for sensory processing and perception, demonstrat-ed through written answers.
- Students will be able to employ useful software and tools (e.g. robot simulator, robotic operating sys-tem) to solve a core problem of mobile robots, and will show a working system via proof-of-concept simulation environments.
- Students will, in writing a joint report, identify problem criteria and context, discuss design and de-velopment, test, analyse and evaluate the behaviour of typical mobile robots they have developed in simulation.
|
Reading List
Books that may be useful, but are not required:
1. Bongard, Josh. "Probabilistic robotics. Sebastian Thrun, Wolfram Burgard, and Dieter Fox. (2005, MIT Press.) 647 pages.
2. Ulrich Nehmzoe, Mobile Robotics: A Practical Introduction, 2nd Edition
3. Robin R. Murphy, Introduction to AI Robotics, MIT Press, 2000, ISBN: 0262133830 |
Additional Information
| Graduate Attributes and Skills |
- Critical and analytical thinking: Apply critical and analytical thinking to real-world problems in the context of mobile robotics.
- Problem-solving skills: Develop their problem-solving skills so they can better create, identify, and evaluate options in order to solve other complex system problems in a similar spirit.
- Knowledge integration: The knowledge base obtained from multiple studied courses in the first and second years can be consolidated considering that (mobile) robotics and autonomous systems is a highly interdisciplinary subject across multiple areas.
- Leadership and teamwork skills: Course work in the form of small teams can cultivate the leader-ship and team spirits needed toward solving a complex system problem.
- Recognise and understand the ethical questions (e.g., privacy compromise due to drones) related to the application of mobile robotics as a concrete instance of embodied artificial intelligence. |
| Keywords | MOB,Mobile Robotics,Sensing and Perception,State Esti-mation,Localization and Mapping,Autonomous |
Contacts
| Course organiser | Dr Chris Lu
Tel:
Email: xiaoxuan.lu@ed.ac.uk |
Course secretary | Mrs Michelle Bain
Tel: (0131 6)51 7607
Email: michelle.bain@ed.ac.uk |
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