2020-2021 / 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 diplômation avec 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 (double diplômation avec 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

  • Basics: SE(3) geometry, sensors, actuators, controllers, kinematics.
  • Mobile robots: locomotion, localization, navigation, SLAM.
  • Arms and grippers: reaching, grasping, grasp learning.
  • Computer vision: feature extraction (Edge, Harris), curve fitting (Ransac, Hough), tracking (Kalman, Nonparametric), object recognition (PCA, probabilistic model).

Learning outcomes of the learning unit

  • Extract information from video streams (identity/position of objects/persons, body pose, 3D structure).
  • Plan actions from sensory data (navigation, grasping, via optimization, learning or control).
  • Translate these actions into a sequence of motor commands that can be executed on the robot.
A group project will allow students to practice the concepts studied in class. Students will program a robotic agent capable of processing images, plan actions, and execute these actions on a robot. The agent will be evaluated in a robot simulator (V-REP).

Prerequisite knowledge and skills

  • Programming skills
  • Basic math
  • Elements of probabilities, statistics, and algorithmics

Planned learning activities and teaching methods

  • Oral courses
  • Exercises
  • Group project

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

Face-to-face.

Organisational adjustments related to the current health context

Recommended or required readings

The course notes et the slides will be available on the course's webpage at the beginning of the semester: http://www.montefiore.ulg.ac.be/~sacre/INFO0948/.

Assessment methods and criteria

Group project (evaluating the assimilation of practical notions).

Work placement(s)

Organizational remarks

Contacts

Lecturer: Pierre Sacré (p.sacre@uliege.be).
Webpage: http://www.montefiore.ulg.ac.be/~sacre/INFO0948/.