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 algorithms to understand its surroundings and complete specific tasks in its environment. This course provides a general understanding of related concepts such as sensing (e.g., visual perception), state estimation (e.g., localisation and mapping) and sequential decision making. The emphasis is on algorithms for probabilistic state estimation, reasoning, control, and coordination, as opposed to electromechanical systems design. Practically useful tools and simulators for developing real robotic systems will also be covered in this course.
At the end of the course, students will develop sufficient skills in the analysis of perception, state estimation, navigation, and decision making algorithms for mobile robots. 
Course description 
Delivery Method:
The course will be delivered through a combination of: (1) live lectures; (2) tutorials; and (3) (optional) online discussion forum. (it originally also said practical labs)
Content/Syllabus:
This is an introductory course in robotics. We will primarily explore a probabilistic treatment of the associated challenges, while also briefly discussing other formulations (e.g., based on logics, hybrid methods). The specific methods discussed may vary depending on how we make progress but the course will most likely focus on the following topics:
 Introduction to probabilistic robotics; refresher of basic mathematical concepts from probability theory.
 Probabilistic state estimation and Bayes filters.
 Kalman filters and extensions: EKF, UKF; Particle filters.
 Sensor models and motion models.
 Basic control theory: openloop and closedloop control.
 Localization based on different sensors (range finders, cameras etc).
 Mapping: environment model, grid map.
 Simultaneous Localization and Mapping (SLAM): basic principles and example algorithms.
 Probabilistic sequential decision making: MDP (also RL), POMDP.
 Logics for robotics; hybrid methods combining logics and probabilistic methods.
 Ethics and practical issues in robotics.

Entry Requirements (not applicable to Visiting Students)
Prerequisites 

Corequisites  
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
Prerequisites  As above. 
High Demand Course? 
Yes 
Course Delivery Information

Academic year 2024/25, 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 implementing and evaluating the methods discussed in the course using ROS simulation software. Written explanations along and discussion will also be evaluated as part of the coursework. Nonassessed example questions will also be used to help students better understand the course material. Feedback for these questions will be provided by the instructor in class or online. 
Feedback 
Practical examples and examlike questions will be discussed during the lecture sessions throughout the course. Feedback for coursework assessments will be provided in class and with the release of the assessment marks. Piazza may be utilized for peerfeedback. 
Exam Information 
Exam Diet 
Paper Name 
Hours & Minutes 

Main Exam Diet S1 (December)  Introduction to Mobile Robotics (INFR10085)  2:120  
Learning Outcomes
On completion of this course, the student will be able to:
 recall and explain the essential facts, concepts, principles and potential ethical concerns of mobile robotics and related concepts, demonstrated through written answers in examination conditions
 describe and evaluate the strengths and weaknesses of some specific sensor and motor hardware; and some specific software for sensory processing and perception, demonstrated through written answers
 employ useful software and tools (e.g. robot simulator, robotic operating system) to solve a core problem of mobile robots, and will show a working system via proofofconcept simulation environments
 in writing a joint report, identify problem criteria and context, discuss design and development, test, analyse and evaluate the behaviour of typical mobile robots they have developed in simulation

Reading List
Much of the material discussed in this course will draw on material from the following book:
(1) Probabilistic Robotics.
Sebastian Thrun, Wolfram Burgard, and Dieter Fox.
MIT Press, 2005.
http://www.probabilisticrobotics.org/
The following books provide additional information:
(2) Robotics: Modelling, Planning and Control.
Bruno Siciliano, Lorenzo Sciavicco, Luigi Villani, and Giuseppe Oriolo.
Springer, 2009.
(3) Introduction to AI Robotics, second edition.
Robin R. Murphy.
MIT Press, 2019.
(4) Knowledge Representation, Reasoning, and the Design of Intelligent Agents: The Answer Set Programming Approach.
Michael Gelfond and Yulia Kahl.
Cambridge University Press, 2014.

Additional Information
Course URL 
https://opencourse.inf.ed.ac.uk/mob 
Graduate Attributes and Skills 
 Critical and analytical thinking: Apply critical and analytical thinking to realworld problems in the context of mobile robotics.
 Problemsolving skills: Develop their problemsolving 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 leadership 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  Robotics,state estimation,perception,control,localisation,mapping,sequential decision making 
Contacts
Course organiser  Dr Mohan Sridharan
Tel:
Email: m.sridharan@ed.ac.uk 
Course secretary  Miss Rose Hynd
Tel: (0131 6)50 5194
Email: rhynd@ed.ac.uk 

