2024-2025 / INFO0948-2

Introduction to intelligent robotics

Duration

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

Number of credits

 Master MSc. in Computer Science, professional focus in computer systems security5 crédits 
 Master MSc. in Data Science, professional focus5 crédits 
 Master MSc. in Data Science and Engineering, professional focus5 crédits 
 Master MSc. in Computer Science and Engineering, professional focus in management5 crédits 
 Master Msc. in computer science and engineering, professional focus in intelligent systems5 crédits 
 Master Msc. in computer science and engineering, professional focus in intelligent systems (double diplômation avec HEC)5 crédits 
 Master MSc. in Computer Science, professional focus in management5 crédits 
 Master Msc. in Mechanical engineering, professional focus in mechatronics5 crédits 
 Master MSc. in Mechanical Engineering, professional focus in sustainable automotive engineering5 crédits 
 Master Msc. in Electrical Engineering, professional focus in Neuromorphic Engineering5 crédits 
 Master MSc. in Computer Science and Engineering, professional focus in computer systems and networks5 crédits 
 Master MSc. in Computer Science, professional focus in intelligent systems5 crédits 
 Master MSc. in Computer Science, professional focus in intelligent systems (double diplômation avec HEC)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.

Course materials and 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