Undergraduate Course: Introduction to Mobile Robotics (INFR10090)
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 | 20 |
ECTS Credits | 10 |
| Summary | A mobile robot is an integrated system comprising sensors, actuators, and software that uses the sensors and actuators to understand its surroundings and complete specific tasks. This course provides a strong grounding of the core concepts such as sensing (e.g., visual perception), state estimation (e.g., localisation and mapping) and sequential decision making. The emphasis is on the underlying mathematical concepts and algorithms as opposed to electromechanical systems design. Practical tools and simulators for developing real robotic systems will also be covered in this course.
At the end of the course, students will develop a good grounding in the core concepts, and in the design and implementation of fundamental algorithms in robotics. |
| Course description |
This is an introductory course in robotics. We will primarily explore a probabilistic treatment of the corresponding topics, while also briefly discussing other formulations (e.g., based on logics, hybrid methods). The specific methods discussed may vary but the topics will include:
- Refresher of basic mathematical concepts in probability theory.
- Probabilistic state estimation and Bayes filters.
- Kalman filters and extensions: EKF, UKF; Particle filters.
- Sensor models and motion models.
- Basic control theory: open-loop and closed-loop control.
- Localization based on different sensors (range finders, cameras etc).
- Mapping and Simultaneous Localization and Mapping (SLAM).
- Probabilistic sequential decision making: MDP (also RL), POMDP.
- Logics for robotics; hybrid methods combining logics and probabilistic methods.
- Ethics and practical issues in robotics.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | Students are expected to be proficient in mathematical concepts, particularly: Probability theory and statistics; Linear algebra and geometry (e.g., vectors, matrices, rotations, trigonometry); Calculus (e.g., integration, differentiation). Students are also expected to be proficient in object-oriented programming and familiar with working in Linux OS systems. |
Information for Visiting Students
| Pre-requisites | Students are expected to be proficient in mathematical concepts, particularly: Probability theory and statistics; Linear algebra and geometry (e.g., vectors, matrices, rotations, trigonometry); Calculus (e.g., integration, differentiation). Students are also expected to be proficient in object-oriented programming and familiar with working in Linux OS systems.
This is a third-year honours level course; students are expected to have an academic profile equivalent to the first two years of this degree programme. Assessment of eligibility for honours level courses will be made on an individual basis.
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| High Demand Course? |
Yes |
Course Delivery Information
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| Academic year 2025/26, Available to all students (SV1)
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Quota: None |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
196 )
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| Assessment (Further Info) |
Written Exam
60 %,
Coursework
40 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
Written exam 60%
Coursework 40%
Coursework involves the implementation of localization algorithms and decision-making algorithms in a realistic simulator. |
| Feedback |
Example problem sets and revision lectures will be provided to help with exam preparation. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- design and explain algorithms for perception, reasoning, and control on a robot.
- implement key algorithms for robot state estimation and sequential decision making.
- identify and apply the appropriate mathematical formulation to solve core robot state estimation or decision making problems.
- design and execute experiments to compare and critically analyse algorithms for robot state estimation and sequential decision making.
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Reading List
Course will primarily draw on material from:
(1) Probabilistic Robotics.
Sebastian Thrun, Wolfram Burgard, and Dieter Fox.
MIT Press, 2005.
http://www.probabilistic-robotics.org/
The following 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 |
Problem solving, critical/analytical thinking, handling ambiguity, knowledge integration, planning and organizing, independent learning, creativity, written communication. |
| Keywords | MOB,Robotics,State estimation,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 |
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