2023-2024 / INFO0948-2

Introduction to intelligent robotics

Duration

30h Th, 4h Pr, 80h Proj.

Number of credits

 Master of Science (MSc) in Data Science5 crédits 
 Master of Science (MSc) in Electrical Engineering5 crédits 
 Master of Science (MSc) in Computer Science and Engineering5 crédits 
 Master of Science (MSc) in Computer Science and Engineering (double degree programme with HEC)5 crédits 
 Master of Science (MSc) in Data Science and Engineering5 crédits 
 Master of Science (MSc) in Computer Science5 crédits 
 Master of Science (MSc) in Computer Science (joint-degree programme with HEC)5 crédits 
 Master of Science (MSc) in Mechanical Engineering (EMSHIP+, Erasmus Mundus)5 crédits 

Lecturer

Pierre Sacré

Language(s) of instruction

English language

Organisation and examination

Teaching in the second semester

Schedule

Schedule online

Units courses prerequisite and corequisite

Prerequisite or corequisite units are presented within each program

Learning unit contents

The course provides an introduction to advanced informatics tools to collect and interpret information from sensors, and to plan and perform actions based on this information.

The following topics are addressed:
- Robot direct and inverse kinematics;
- Control and planning methods;
- Sensor interpretation;
- SLAM: Simultaneous localization and mapping;
- Applications: Mobile robots + arms and grippers.

Learning outcomes of the learning unit

At the end of the course, the student will understand the breadth and challenges faced in the field of robotics and have entry-points to the literature and current work about robotics, sensor processing and robot control applied to a variety of autonomous robotic tasks.
 
This course contributes to the learning outcomes I.1, I.2, II.1, II.2, II.3, III.1, III.2, III.3, IV.1, IV.3, VI.1, VI.2, VII.1, VII.2, VII.3, VII.4, VII.5 of the MSc in data science and engineering.
 
This course contributes to the learning outcomes I.1, I.2, II.1, II.2, II.3, III.1, III.2, III.3, IV.1, IV.3, IV.8, VI.1, VI.2, VII.1, VII.2, VII.3, VII.4, VII.5 of the MSc in electrical engineering.
 
This course contributes to the learning outcomes I.1, I.2, II.1, II.2, II.3, III.1, III.2, III.3, IV.1, VI.1, VI.2, VII.1, VII.2, VII.3, VII.4, VII.5 of the MSc in computer science and engineering.
 
This course contributes to the learning outcomes I.1, I.2, II.1, II.2, II.3, III.1, III.2, III.3, IV.1, VI.1, VI.2, VII.1, VII.2, VII.3, VII.4, VII.5 of the MSc in mechanical engineering.

Prerequisite knowledge and skills

The course relies on basic knowledge of probability, statistics, and algorithmics, and programming skills.

Planned learning activities and teaching methods

The course includes both ex-cathedra lectures, exercise sessions, and group projects.

Mode of delivery (face to face, distance learning, hybrid learning)

Face-to-face.

Recommended or required readings

The course material will be made available as the semester progresses.

Exam(s) in session

Any session

- In-person

oral exam

Written work / report


Additional information:

The assessment is based on the written report and the oral presentation of the group project. Students must be able to explain the theoretical concepts seen during the course and connect them with their implementation in the project.

Work placement(s)

Organisational remarks and main changes to the course

Contacts

Lecturer: Pierre Sacré (p.sacre@uliege.be).
Webpage: https://people.montefiore.uliege.be/sacre/INFO0948/.

Association of one or more MOOCs