Computer Science, Second Cycle, Research Methodologies for Intelligent Systems, 30 credits

Aims and objectives

General aims for second cycle education

Second-cycle courses and study programmes shall involve the acquisition of specialist knowledge, competence and skills in relation to first-cycle courses and study programmes, and in addition to the requirements for first-cycle courses and study programmes shall

• further develop the ability of students to integrate and make autonomous use of their knowledge
• develop the students' ability to deal with complex phenomena, issues and situations, and
• develop the students' potential for professional activities that demand considerable autonomy, or for research and development work.

(Higher Education Act, Chapter 1, Section 9)

Course objectives

Knowledge and understanding
After the course the student should be able to account for

• control schemes commonly used in modern robotic systems,
• control requirements in terms of controllability and stability,
• different types of sensors commonly used on mobile robotic platforms and the basic principles of operation of different types of sensors, and
• the fundamental concepts in machine learning, the different classes of machine learning algorithms, and ways to chose and apply different basic machine learning algorithms

Applied knowledge and skills
After the course the student should be able to

• synthesize and tune control schemes for the robotic application at hand,
• write software for robot applications using modern tools,
• configure, calibrate and use modern sensors in the context of mobile robots, and
• prepare data and apply machine learning methods to achieve a learning goal within an intelligent system.

Making judgments and attitudes
After the course the student should be able to

• make judgments with regards to relevant scientific, societal and ethical aspects, and show awareness of ethical aspects of research and development,
• judge the possibilities and limitations of science, its role in society and people's responsibility for how it is being used, and
• search and evaluate scientific information.

Main content of the course

Introduction to Robotics and Intelligent Systems, 7,5 Credits
Academic reading and writing robot programming and middleware, ethics in robotics and its applications, probability theory and state estimation, fundamentals of computer science from a robotics point of view, actuators and sensors, and robotics history.

Sensors and Sensing, 7,5 Credits
The role of sensors in a probabilistic robotic framework, positioning sensors: encoders and accelerometers, range sensors: sonars, radars and laser range finders, image sensors: cameras, global positioning sensors: GPS and indoor localization systems, 3D range sensors: ToF, structured light and stereo vision, chemical sensors, calibration, noise modelling and characterization, and noise filtering and sensor data processing.

Machine Learning, 7,5 Credits

• Core concepts and algorithms used for supervised and unsupervised learning,
• application and implementation of machine learning algorithms for the tasks of regression, classification, and clustering,
• methods for data preprocessing including normalisation, feature extraction and selection, and dimensionality reduction,
• practical recommendations for applying machine learning algorithms,
• evaluation and analysis of the performance of machine learning algorithms.

Robot Modelling and Control, 7,5 Credits
Analysis of linear (and in part of nonlinear) systems, stability criteria,observability and controllability , common motion control schemes applied to robotic systems (PID control, linearization and decoupling, predictive control, passivity-based control), and overview of interaction control schemes (force control, impedance control).

Teaching methods

Students who have been admitted to and registered on a course have the right to receive tuition and/or supervision for the duration of the time period specified for the particular course to which they were accepted (see, the university's admission regulations (in Swedish)). After that, the right to receive tuition and/or supervision expires.

Examination methods

Introduction to Robotics and Intelligent Systems, Theory, 2.5 credits (Code: A001)
Individual oral and written reporting of literature assignments.

Introduction to Robotics and Intelligent Systems, Laboratory Work, 5 credits (Code: A002)
Individual oral and written reporting of lab assignments.

Sensors, Theory, 3 credits (Code: A003)
Written exam. A retake will be scheduled to take place within eleven weeks of the regular examination.

Sensors, Laboratory Work, 4.5 credits (Code: A004)
Individual written report of laboratory work.

Machine Learning, Theory, 2 credits (Code: A005)
Individual oral presentations at seminars.

Machine Learning, Laboratory work, 4 credits (Code: A006)
Individual written reports.

Machine learning, Project work, 1.5 credits (Code: A007)
Individual oral presentation and written report.

Robot Modelling and Control, Theory, 3 credits (Code: A008)
Written exam. A retake will be scheduled to take place within eleven weeks of the regular examination.

Robot Modelling and Control, Laboratory Work, 4.5 credits (Code: A009)
Individal written reports on laboratory work.

For students with a documented disability, the university may approve applications for adapted or other forms of examinations.

For further information, see the university's local examination regulations (in Swedish).

Grades

According to the Higher Education Ordinance, Chapter 6, Section 18, a grade is to be awarded on the completion of a course, unless otherwise prescribed by the university. The university may prescribe which grading system shall apply. The grade is to be determined by a teacher specifically appointed by the university (an examiner).

In accordance with university regulations regarding grading systems for first and second-cycle courses (Vice-Chancellor’s decision ORU 2018/00929), one of the following grades shall be used: Fail (U), Pass (G) or Pass with Distinction (VG). For courses that are included in an international Master’s programme (60 or 120 credits) or offered to the university’s incoming exchange students, the grading scale of A-F shall be used. The vice-chancellor, or a person appointed by the vice-chancellor, may decide on exceptions from this provision for a specific course, if there are special grounds.

Grades used on course are Fail (F), Sufficient (E), Satisfactory (D), Good (C), Very Good (B) or Excellent (A).

Introduction to Robotics and Intelligent Systems, Theory
Grades used are Fail (F), Sufficient (E), Satisfactory (D), Good (C), Very Good (B) or Excellent (A).

Introduction to Robotics and Intelligent Systems, Laboratory Work
Grades used are Fail (U) or Pass (G).

Sensors, Theory
Grades used are Fail (U) or Pass (G).

Sensors, Laboratory Work
Grades used are Fail (F), Sufficient (E), Satisfactory (D), Good (C), Very Good (B) or Excellent (A).

Machine Learning, Theory
Grades used are Fail (U) or Pass (G).

Machine Learning, Laboratory work
Grades used are Fail (F), Sufficient (E), Satisfactory (D), Good (C), Very Good (B) or Excellent (A).

Machine learning, Project work
Grades used are Fail (U) or Pass (G).

Robot Modelling and Control, Theory
Grades used are Fail (U) or Pass (G).

Robot Modelling and Control, Laboratory Work
Grades used are Fail (F), Sufficient (E), Satisfactory (D), Good (C), Very Good (B) or Excellent (A).

For further information, see the university's local examination regulations (in Swedish).

Comments on grades

To obtain a passing grade for the course as a whole, a minimum of grade E or G is required for all components on the course. To arrive at a course grade, the grades awarded for each examination assignment with grades A-E, are first converted to the numerical values 5-1. An average value is then calculated, also taking into account the number of credits for each module in relation to the total number of credits for the course. The course grade is thus awarded by means of a weighted average of the examination assignments included on the course. Grades that fall exactly between two marks are rounded up.

Specific entry requirements

First-cycle degree of 180 credits with Computer Science as the main field of study and 15 credits in mathematics (analysis and algebra). Or first-cycle degree of 180 credits and 30 credits in mathematics (analysis and algebra) as well as 15 credits in Computer Science or Informatics (which includes programming). The applicant must also have qualifications corresponding to the course "English 6" or "English B" from the Swedish Upper Secondary School.

For further information, see the university's admission regulations (in Swedish).

Transfer of credits for previous studies

Students who have previously completed higher education or other activities are, in accordance with the Higher Education Ordinance, entitled to have these credited towards the current programme, providing that the previous studies or activities meet certain criteria.

For further information, see the university's local credit transfer regulations (in Swedish).

Other provisions

The course is given in English.

Reading list and other teaching materials

Introduction to Robotics and Intelligent Systems, 7,5 Credits
Reference literature
Siciliano, Bruno, Khatib, Oussama (eds.) (2008)
Springer handbook of robotics
Springer

Lin, Patrick; Abney, Keith, Bekey, George A. (2012)
Robot ethics: the ethical and social implications of robotics

Sensors and Sensing, 7,5 Credits
Referenslitteratur
De Silva, C. W. (2015)
Sensors and Actuators: Engineering System Instrumentation, Second Edition
CRC Press

Stoyanov, Todor (2016)
Sensors and Sensing: Course Notes
online: http://www.aass.oru.se/Research/mro/courses/sens/notes.pdf

Machine Learning, 7,5 Credits
Reference literature
Bishop, C. M. (2007)
Pattern Recognition and Machine Learning
Springer

Robot Modelling and Control, 7,5 Credits
Reference literature
Lewis, F. , Dawson,D. M. and Abdallah, C. T. (2003)
Robot Manipulator Control - Theory and Practice CRC Press

Ytterligare material utdelas under kursens gång.