M1 - Electronics, Electrical Engineering, Automation - Robotics profile

The module will cover the following points:

• Transfer function link and differential equation
• Representation and continuous status feedback (eigenvalues, stability)
• Representation and sampled status feedback
• Feedback control without and with full-loop feedback, LQR control
• State observers
• Nonlinear control with examples

Practical work: applying knowledge to real-world examples (e.g., electric motors), programming in Python (numpy and control libraries).

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This teaching unit complements basic training in signal processing with in-depth knowledge of deterministic or random digital signals. This knowledge is essential in all engineering sciences, as digital signal processing is currently used in the majority of applications.

In the first part (10.5 hours of lectures, 6 hours of practical work), the course covers the sampling and quantification of continuous signals and the relationship between digital signals and the original continuous signal. It defines the discrete Fourier transform of digital signals, its estimation, and its use on real deterministic signals.

The second part of the course (9 hours of lectures, 4.5 hours of tutorials, 3 hours of practical work) is dedicated to random signals and how the properties of certain random signals can be used either to reduce the random part of a signal whose deterministic part we wish to favor (filtering, increasing the signal-to-noise ratio, etc.) or to improve information transmission or even identify complex linearized systems.

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• This teaching unit complements basic training in analog electronics with in-depth knowledge of signal filtering, amplification, and modulation. This knowledge is essential for understanding and implementing analog electronic systems in all fields of engineering science.
• Teaching is organized in the form of lectures, tutorials, and practical work, opening up the possibility for mini-projects.

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This teaching unit, devoted to the fundamentals of digital electronics, is structured in an original way around a technical project, carried out individually or in pairs, the progress of which will follow the progression of the associated courses.

Each project topic will be assigned at the beginning of the teaching unit.

The main concepts of digital electronics will be explored in depth through lectures, and practical work may supplement the theoretical aspects to guide the progress of the project.

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This teaching unit consists of several parts, the first of which deals with the power electronics structures required to power an electronic system. The second part will focus on the current or voltage regulation of these structures. A third part will deal with the conversion functions required to control MCC and DC Brushless actuators.

The last section presents actuator topologies for robotics and their implementation. DC motor control and autopilot control of a synchronous motor will illustrate this last section.

Practical work will enable students to observe the principle and implementation of regulated systems for electronics and actuators. This course unit may serve as a basis for M1 project topics.

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Computer engineering is the discipline that deals with the design, development, and manufacture of computer systems, both hardware and software.

This discipline has become fundamental in engineering sciences, whether in electronics, robotics, signal processing, measurement, etc., due to the important role that computers now play in all these fields.

This module aims to encourage students to develop computer code on a scale corresponding to that of a complete software program. The quantity of code involved naturally creates a need to structure the code so that it remains viable, and the concepts associated with code structuring will therefore be addressed or reinforced.
Teaching is therefore organized mainly around practical work and projects. The context largely concerns the core themes of the EEA: signal processing (acquisition chain), instrument interfacing, and data retrieval via the internet on an embedded Linux platform. The topic of event-driven programming through the development of graphical interfaces will also be covered. The languages used will be Labview and Python. Portions of C/C++ may be used in projects at the students' initiative.

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- Controller summary.

- Robust synthesis and risk management.

- Representation and synthesis of synchronous machines.

- Description/synthesis language.

- The basics of the VHDL language (entity, architecture, etc.).

- Behavioral and structural descriptions.

- Simulation (Testbench).

- Reprogrammable circuits (CPLD, FPGA).

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Tutorial courses in specialized English and English for communication, aimed at developing professional autonomy in the English language.

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Project in partnership with a research laboratory and/or a company, highlighting the student's scientific skills, autonomy, and adaptability.

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The internship or final project should highlight the student's scientific skills, independence, and adaptability:

• Internship lasting 2 to 3 months (maximum 5 months) to be carried out in a research laboratory or within a company;
• or a 3-month final project in a research laboratory or teaching project room.

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Description:

1 - The aim is to enable students to understand the importance of a well-prepared application that is tailored to a specific internship or job advertisement, or linked to the activities of a professional organization in the case of a speculative application; to write resumes and cover letters; to gain a better understanding of their personality; to use new technologies (social networks and job boards) and to tailor their search to their career plans. Finally, to know how to prepare for and behave during job interviews.

2 - The aim is to enable students to write a scientific article following the completion of a project. To do this, they must be familiar with the objectives and characteristics of the article, the plan to be followed, the different stages of completion, and the rules of presentation. Next, in order to present their project orally, students must know and be able to apply the general presentation structure; define appropriate and relevant visual aids; follow the rules of oral expression in order to express themselves correctly and professionally (vocabulary, syntax, etc.); and adopt behaviors that energize their speech and engage their audience.

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Nowadays, image processing is ubiquitous in information technology: medicine, biology, agriculture, entertainment, culture, measurement, mechanics, etc.

Image processing involves 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. While it has long been performed using electronic circuits, image processing is now carried out almost exclusively digitally, i.e., using algorithms generally programmed with an imperative language (C, C++, Java, Python, etc.).

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

The teaching unit consists mainly of 11 lectures covering the basics of the main areas of image processing and three practical sessions, with topics to be chosen from six proposals. Students can choose to carry out the work on images they bring in that are relevant to their field of study.

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The module will cover the following points:

• Introduction to the Git version control system
• Introduction to ROS middleware for building robotic applications
• Modularization of a robotic application

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

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The module will cover the following points:

• Introduction to robotics: history, types of robots, serial and parallel mechanisms, applications
• Components (sensors and actuators)
• Trajectory generation (in joint and operational spaces)
• Direct/inverse geometric models, direct/inverse kinematic model
• Kinematics control and singularities
• Issues and applications in mobile robotics
• Non-holonomic models: unicycle, bicycle, car
• Sensors and odometry
• Location by rangefinder and data fusion (Kalman filter)
• Mapping (homogeneous transformations and ICP)
• Navigation (positioning control, path tracking)

Practical work: applying acquired knowledge to a real robot (either a manipulator arm or a wheeled robot), ROS programming with Git and Python.

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