Course syllabus

AI Search Methods for Mobile Robots, 3 credits

Course code: DT702A Credits: 3
Main field of study: Computer Science Progression: A1N
    Last revised: 11/09/2020
Education cycle: Second cycle Approved by: Head of school
Established: 02/07/2019 Reading list approved: 11/09/2020
Valid from: Spring semester 2021 Revision: 2

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 completion of this course, the student will have knowledge about three advanced applications of AI reasoning, namely: resource scheduling, robot motion planning, and multi-robot coordination. The student will understand the computational bottlenecks of different algorithms, and will have gained a deeper understanding of the limits of current state of the art methods.

Applied knowledge and skills
Completing this course, the student will be able to formulate real-world problems as search problems, and sketch methods to solve them based on heuristic, sampling-based and constraint-based search algorithms. The student will be able to develop solutions for particular applications that are relevant in industry, namely, scheduling, robot motion planning, and coordination of fleets of autonomous robots.

Making judgments and attitudes
Completing this course, the student will be able to judge the suitability of a particular approach to automated reasoning for a given problem, have an understanding of the capabilities and limitations of the considered algorithms. Furthermore, the student will understand how problem structure relates to computational overhead.

Main content of the course

  • Overview of systematic and local search methods.
  • Introduction to constraint reasoning, backtracking search, k-consistency.
  • Temporal constraint reasoning, constraint-based resource scheduling.
  • Lattice- and sampling-based robot motion planning algorithms.
  • State of the art methods for multi-robot motion planning, coordination and control.

Teaching methods

The course will be given in the form of lectures and practical project work. Three lectures will require physical presence at Örebro University, the others will be given remotely. Practical project work will involve the use of a state of the art tool for scheduling, motion planning or multi-robot coordination. The tools are explained by the instructors during the lectures, and one particular tool for carrying out the project is to be chosen by the student.

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

Exercises, 1.5 credits (Code: A001)
Examination is done based on written reports on obligatory task assignments.

Presentation at Seminar, 1.5 credits (Code: A002)
Examination is based on oral presentation at seminars.


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 (U), Pass (G) or Pass with Distinction (VG).

Exercises
Grades used are Fail (U), Pass (G) or Pass with Distinction (VG).

Presentation at Seminar
Grades used are Fail (U), Pass (G) or Pass with Distinction (VG).

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

Specific entry requirements

At least 180 credits including 15 credits programming as well as qualifications corresponding to the course "English 5"/"English A" 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

Additional Reading

Dechter, Rina (2003)
Constraint Processing The Morgan Kaufmann Series in Artificial Intelligence
Elsevier Science, 480 sidor

LaValle, Steven (2006)
Planning algorithms
Cambridge university press, 831 sidor

Russell, Stuart, Norvig, Peter (2010)
Artificial Intelligence, A modern Approach Prentice Hall
Prentice Hall, 1132 sidor

Additional material (research articles) will be distributed during the course.