ROBOTICS Choice

Nowadays, image processing is omnipresent in information technologies: medicine, biology, agriculture, entertainment, culture, measurement, mechanics...

Image processing consists of applying mathematical transformations to images in order to modify their appearance or extract information from them. More generally, image processing aims to manipulate the underlying information contained in an image. Although it has long been carried out using electronic circuits, image processing is nowadays carried out almost exclusively digitally, i.e. via algorithms generally programmed with an imperative language (C, C++, Java, Python, etc.).

This teaching unit aims to give a solid foundation in image processing. It addresses, among other things, image formation and acquisition, colorimetric transformations, morphological operations, geometric transformations, compression, frequency transformations, recognition and matching techniques, etc. and an introduction to deep learning methods. The courses are complemented by support videos.

The teaching unit is mainly composed of 11 didactic courses covering the basics in the main areas of image processing and 3 practical work sessions whose subjects are to be chosen from 6 proposals. Students can choose to carry out the work on images they bring corresponding to their field of training.

See full page

The module will cover the following:

• Introduction to the Git version control system
• Introduction to ROS Middleware for Robotics Applications
• Modularization of a robotic application

Practical work: Implementation of a ROS application, test on simulator and verification on real robot

See full page

The module will cover the following:

• Introduction to robotics: history, type of robots, series and parallel mechanisms, applications
• Components (sensors and actuators)
• Trajectory generation (in the articular and operational spaces)
• Direct/Inverse Geometric Models, Direct/Inverse Kinematics Model
• Kinematic control and singularities
• Issues and applications in mobile robotics
• Non-holonomic models: unicycle, bicycle, car
• Sensors and odometry
• Distance finder and data fusion location (Kalman filter)
• Mapping (homogeneous transformations and KPIs)
• Navigation (installation regulation, path tracking)

Practical work: implementation of the skills on a real robot (either manipulator arm or wheeled robot), ROS programming with git and python.

See full page