Robotics

The module will cover the following topics:

• The Relationship Between Transfer Functions and Differential Equations
• Representation and Continuous Status Feedback (Eigenvalues, Stability)
• Sampled State Representation and Feedback
• Feedback Control Without and With Full-Loop Control, LQR Control
• State observers
• Nonlinear Control with Examples

Practical exercises: applying what you've learned to real-world examples (e.g., electric motors), programming in Python (using the NumPy and Control libraries).

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This course complements a basic education in signal processing with in-depth knowledge of deterministic and random digital signals. This knowledge is essential in all engineering disciplines, as digital signal processing is currently used in the majority of applications.

In the first part (10:30 a.m. lectures, 6 hours of lab work), the course covers the sampling and quantization 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 application to real deterministic signals.

The second part of the course (9 hours of lectures, 4 hours and 30 minutes of tutorials, 3 hours of lab) is devoted to random signals and how the properties of certain random signals can be used either to reduce the random component of a signal when the deterministic component is of primary interest (filtering, increasing the signal-to-noise ratio, etc.), to improve information transmission, or to identify linearized complex systems.

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• This course complements the foundational training in analog electronics with in-depth knowledge of signal filtering, amplification, and modulation. This knowledge is essential for understanding and designing analog electronic systems in all fields of engineering.
• Instruction is organized in the form of lectures, tutorials, and lab sessions, which provide opportunities for mini-projects.

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This course, which focuses on the fundamentals of digital electronics, is structured in a unique way around a technical project—carried out individually or in pairs—whose progress will follow the pace of the associated lectures.

Each project topic will be assigned at the beginning of the course.

The key concepts of digital electronics will be explored in depth through lectures, and lab sessions will supplement the theoretical aspects to guide the project’s progress.

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This course consists of several sections. The first section covers the power electronics structures required to power an electronic system. The second section will focus on current- or voltage-based control of these structures. A third section will cover the conversion functions required to control MCC and brushless DC actuators.

The final section presents actuator topologies for robotics and their implementation. The control of a DC motor and the self-driving control of a synchronous motor will illustrate this final section.

Hands-on exercises will allow students to observe the principles and implementation of controlled systems for electronics and actuators. This course unit may serve as the 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, from both a hardware and software perspective.

This field has become fundamental to engineering sciences—whether in electronics, robotics, signal processing, or measurement—due to the significant role that computers have come to play in all of these areas.

This module aims to help students develop computer code on a scale commensurate with that of a complete software application. The volume of code involved naturally creates a need to structure the code so that it remains manageable, and the concepts related to code structuring will therefore be introduced or reinforced.
Instruction is thus organized primarily around practical exercises and projects. The context largely involves core topics of the EEA program: signal processing (data acquisition chain), instrument interfacing, and data transmission over the Internet via an embedded Linux platform. The topic of event-driven programming through the development of graphical user interfaces will also be covered. The programming languages used will be LabVIEW and Python. Students may choose to incorporate elements of C/C++ into their projects.

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- Controller overview.

- Robust synthesis and risk management.

- Representation and synthesis of synchronous machines.

- Description/synthesis language.

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

- Behavioral and structural descriptions.

- Simulation (Testbench).

- Reprogrammable circuits (CPLDs, FPGAs).

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A tutorial course in specialized English and communicative English designed to foster professional autonomy in the English language.

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A project in partnership with a research laboratory and/or a company, highlighting the student’s scientific skills, independence, and adaptability.

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

• A 2- to 3-month internship (maximum 5 months) to be completed in a research laboratory or at a company;
• or a 3-month final project in a research laboratory or an educational project room.

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Description*:

1 - The goal is to help students understand the importance of a well-prepared application that matches an internship or job posting, or aligns with the activities of a professional organization in the case of an unsolicited application; to write resumes and cover letters; to gain a better understanding of their own personalities; to use new technologies (social media and job boards); and to tailor their job search to their career goals. Finally, to know how to prepare for and conduct themselves during job interviews.

2 - The goal is to enable students to write a scientific article following the completion of a project. To do so, they must understand the article’s objectives and characteristics, the outline to follow, the various stages of the writing process, and the formatting guidelines. Next, to present their project orally, students must understand and be able to apply the general structure of a presentation; select appropriate and relevant visual aids; follow the rules of oral communication to express themselves correctly and professionally (vocabulary, syntax, etc.); and adopt behaviors that energize their speech and help engage their audience.

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Today, image processing is ubiquitous in information technology: medicine, biology, agriculture, entertainment, culture, measurement, mechanical engineering …

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 was long performed using electronic circuits, image processing is now carried out almost exclusively digitally—that is, via algorithms typically programmed in an imperative language (C, C++, Java, Python, etc.).

This course aims to provide a solid foundation in image processing. Among other topics, it covers image formation and acquisition, color transformations, morphological operations, geometric transformations, compression, frequency transformations, recognition and matching techniques, … and an introduction to deep learning methods. The lectures are supplemented by supporting videos.

The course consists primarily of 11 lectures covering the fundamentals of the main areas of image processing and 3 lab sessions, for which students must choose a topic from among 6 options. Students may choose to complete the lab assignments using images they provide that are relevant to their field of study.

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The module will cover the following topics:

• Introduction to the Git Version Control System
• Introduction to ROS Middleware for Developing Robotics Applications
• Modularization of a Robotics Application

Hands-on exercises: Setting up a ROS application, testing on a simulator, and verifying on a real robot

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The module will cover the following topics:

• Introduction to Robotics: History, Types of Robots, Serial and Parallel Mechanisms, Applications
• Components (sensors and actuators)
• Trajectory Generation (in the Articular and Operational Spaces)
• Direct/inverse geometric models, direct/inverse kinematic models
• Kinematics and Singularities
• Issues and Applications in Mobile Robotics
• Non-holonomic models: unicycle, bicycle, car
• Sensors and Odometry
• Localization using a rangefinder and data fusion (Kalman filter)
• Mapping (Homogeneous Transformations and ICP)
• Navigation (course control, path tracking)

Hands-on exercises: applying what you've learned to a real robot (either a robotic arm or a wheeled robot), ROS programming with Git and Python.

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Advanced Programming

• object-oriented programming (C++)
• classes
• attributes/methods
• legacy
• pointers
• templates
• C++11 standards

Artificial Intelligence

• Machine Learning: State of the Art, Challenges, Applications
• PCA (Principal Component Analysis)
• SVM (Support Vector Machines)
• Generations 1, 2, and 3 of neural networks (spike technologies, etc.)
• neural network learning
• convolutional neural networks
• reinforcement learning
• genetic algorithms

Practical Work

• Development of a Logic Simulator for Microelectronics
• Implementation (in C++) and integration (in ROS) of robotics algorithms
• Introduction to AI-Based Classification Tools
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• Advanced Programming

  • object-oriented programming (C++)
  • classes
  • attributes/methods
  • heritage
  • pointers
  • templates
  • C++11 Standards

  Artificial Intelligence

  • Machine Learning: State of the Art, Problems, Applications
  • PCA (Principal Component Analysis)
  • SVM (Support Vector Machines)
  • Neural networks of generations 1, 2, and 3 (spike technologies, etc.)
  • Convolutional neural networks
  • Reinforcement learning
  • Genetic Algorithms

  Laboratory Practicals

  • Implementation of a Logical Simulator for Microelectronics
  • Implementation (in C++) and integration (in ROS) of robotic algorithms
  • Introduction to Classification Tools Based on Artificial Intelligence

   

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Optimization

• Linear Optimization
• Nonlinear Optimization (gradient method, optimal-step gradient, Lagrange multipliers)
• Optimization Applied to Robotics (Optimal Control Based on Quadratic Programming Under Linear Constraints)

Embedded Systems

• Harvard and Von Neumann Architectures
• Understanding and implementing the main features of a microcontroller
• Selecting and sizing an embedded programming solution based on a specific need
• Programming a Raspberry Pi Board in C
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• Optimization

  • Linear Optimization
  • Nonlinear optimization (gradient descent, Lagrange multipliers)
  • Applying Optimization in Robotics (Optimal Control Based on Quadratic Programming Under Linear Constraints)

  Embedded Systems

  • Harvard & Von Neumann Architectures
  • Understanding and Implementation of the Main Functions of a Microcontroller
  • Selection and Implementation of an Embedded Programming Solution Tailored to Specific Design Specifications
  • C Programming on a Raspberry Pi

   

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This course covers a range of topics in robotics, from the micro to the macro scale, including micro-manipulators, cable-driven robots, surgical robots, underwater robots, flying robots, and humanoid robots, as well as teleoperation, virtual and augmented reality, and operational safety. The content of each topic is detailed below. Mini-projects on the aforementioned topics will be conducted to reinforce the fundamentals taught, using both simulation software and real robots.

Microrobotics: Microrobotics involves the design, modeling, and control of miniaturized robotic systems capable of performing manipulation tasks on objects ranging in size from 1 µm to 1 mm. Application areas include all fields that require high precision (assembly of mechanical, electronic, or optical microsystems; microsurgery; etc.). At these scales, robots cannot be created simply by homothetic miniaturization of conventional robots. New robot concepts and new actuation principles must be employed. This course is an introduction to microrobotics and presents the essential concepts, including: the scale effect, the physics of the microworld, deformable and soft robotics, and microactuators.

Surgical Robotics: The objective of this course is to provide students with an introduction to the field of surgical robotics. The goal is to enable students to understand the needs expressed by clinicians and to illustrate, through several examples, the process that led to the design and development of robots used in surgical procedures. Several design elements and control architectures will be discussed, with an emphasis on the need to ensure the safety of both the patient and the medical team.

Underwater and Flying Robots: Mobile robotics designed for aerial and underwater environments relies on specific characteristics that will be introduced in this course. Current solutions and outstanding challenges will be discussed. Topics related to modeling and nonlinear control applied to under-, iso-, and over-actuated systems will be covered.

Humanoid Robotics: This course will cover advanced geometric and kinematic modeling methods for tree-like robotic structures, such as humanoid robots. Basic concepts will also be covered, including the center of mass, the center of pressure, the ZMP, static stability, and dynamic stability. A study on bipedal walking control will be conducted, covering walking models, trajectory generation, and ZMP/COM control, as well as dynamic stabilization of the robot. The second part of the course will focus on the kinematic control of highly redundant structures (underdetermined systems of the Ax=b type) using methods based on constrained optimization techniques (LP, QP) as well as hierarchical control based on projection into null space or task hierarchies based on QP or LP hierarchies.

Parallel Cable Robots: This course introduces the principles of Parallel Cable Robots (PCR), followed by a review of the state of the art, including application examples, PCR demonstrators, and commercial PCRs. The geometric, kinematic, and dynamic models of PRCs are then developed. Based on these models, the various types of PRCs, several definitions of their workspace, the main concepts useful for their design, and simple control methods will finally be presented.

Virtual and Augmented Reality: Augmented Reality (AR) and Virtual Reality (VR) technologies involve the interactive simulation of a 3D environment in which the user is immersed. This simulation is generally primarily visual in nature, though it may also include other perceptual information through various sensory modalities: spatialized sound, haptic or force feedback, somatosensory input, etc. This course is an introduction to the various techniques used in VR/AR systems: we will cover the main 3D rendering libraries (OpenGL, Vulkan), devices currently available on the market, the basics of physics engines, and the techniques used to track the user’s position and estimate their point of view in real time.

Reliability and Operational Safety: This course focuses on the reliability of a robotic system, particularly during the operational phase. When a robot operates in a complex and partially unknown environment, unforeseen events may occur to which the system must respond in order to ensure its own safety and that of its environment. This course will introduce the basic concepts of operational safety and present examples of reliability mechanisms applied to mobile robotics.

Teleoperation: This section provides a brief introduction to the history of teleoperation development, the modeling of teleoperation components, and their schematics. Performance evaluation criteria for teleoperation are defined. Methods for performance analysis and control design are also introduced. The course covers applications of teleoperation in the field of surgical robotics, as well as open questions and remaining challenges.

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This course covers a range of specialties in robotics, spanning from the micro to the macro scale, including micro-manipulation, surgical, underwater, flying, humanoid, and cable-driven robots, as well as teleoperation, virtual and augmented reality, and operational safety. The content of each sub-unit is detailed below. Projects on the topics mentioned will be carried out to reinforce the fundamental concepts using both simulation software and real robots.

Micro-robotics: Micro-robotics involves the design, modeling, and control of miniaturized robotic systems capable of performing manipulation tasks on objects ranging in size from 1 µm to 1 mm. Application areas include all fields requiring high precision (assembly of mechanical, electronic, or optical microsystems; microsurgery; etc.). At these scales, robots cannot be fabricated simply by homothetic miniaturization of conventional robots. New robot concepts and new actuation principles must be employed. This course serves as an introduction to micro-robotics and covers the essential concepts of scale effects, the physics of the micro-world, deformable and flexible robotics, and micro-actuators.

Surgical Robotics: The objective of this subunit is to provide students with an introduction to the field of surgical robotics. It focuses on helping students understand the needs expressed by clinicians and, through a few examples, illustrate the process that led to the development of robots used in surgical procedures. Certain design elements and control architectures will be discussed, with an emphasis on ensuring the safety of the patient and the medical team.

Underwater and flying robots: The specific characteristics of underwater and aerial robotics will be presented. Current solutions and outstanding issues will be discussed. The basic elements required for control design for this type of vehicle—from modeling to nonlinear control techniques—will be addressed, depending on whether the systems are under-, iso-, or over-actuated.

Humanoid robotics: This sub-unit covers advanced kinematic and differential kinematics modeling methods for humanoid robots. It addresses the fundamentals of the center of mass (COM), the center of pressure, the zero-moment point (ZMP), static stability, and dynamic stability. A study on bipedal gait control will be conducted, including gait models, trajectory generation, and ZMP/COM control, as well as dynamic stabilization of the robot. The second part of the subunit focuses on the differential kinematic control of highly redundant structures (underdetermined systems of the form Ax = b) using methods based on optimization techniques (LP, QP) under constraints, as well as on hierarchical control based on projection into the null space or task hierarchies based on QP or LP hierarchies.

Cable-driven parallel robots: This section presents the basic principle of cable-driven parallel robots (CDPRs), followed by a review of the current state of the art, including application examples, CDPR demonstrators, and commercial CDPRs. Geometric, kinematic, and dynamic models of CDPRs are then developed. Based on these models, the different types of CDPRs, various definitions of their workspace, the main concepts relevant to their design, and simple control strategies will be presented.

Virtual and Augmented Reality: AR and VR involve providing the user with an interactive simulation of a 3D world, where physics can be simulated and enhanced with additional data visualization. This simulation is typically graphical in nature, but it can also incorporate other perceptual information across multiple sensory modalities: spatialized sound, haptics, somatosensory feedback, etc. This course serves as an introduction to the various techniques involved in creating an AR/VR system. We will cover current 3D technologies (OpenGL, Vulkan), available devices, the basics of physics engines, and the localization and vision techniques used to track the user’s movements in real time and compute their point of view.

Operational Safety of Robots: This section focuses on the reliability of robotic systems, primarily during the operational phase. When a robot moves in a complex and partially unknown environment, unforeseen events can occur. The system must respond to these events to ensure its own safety and that of its environment. This course will introduce the basic concepts of dependability and present examples of safety mechanisms applied to mobile robotics.

Teleoperation: This section provides a brief introduction to the history of development, the typical structures of teleoperation systems, and the modeling of teleoperation components. Based on system modeling, the criteria for evaluating teleoperation performance are defined, and methods for performance analysis and control design are introduced accordingly. The course also covers the applications of teleoperation in the field of robotic surgery, as well as the outstanding issues and challenges involved in practical implementation.

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The purpose of this course is to study and implement perception systems for mobile, manipulative, and humanoid robots, among others. The course focuses on proprioceptive and exteroceptive perception systems, with a strong emphasis on vision systems. Lectures cover the general principles of perception and the operation of the most commonly used sensors (cameras, projectors, distance, motion, and position sensors, etc.). A series of lab sessions accompanies this course in the form of a long-term project divided into sub-goals that address different parts of the course.

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This course covers the perception systems commonly used in all types of robots (e.g., mobile robots, manipulators, humanoids). The course covers proprioceptive and exteroceptive sensors, with a focus on vision. We begin by introducing the general principles of perception, and then explain the modeling and operating principles of the main robot sensors: monocular cameras, stereo cameras, distance, position, and motion sensors, etc. The lab sessions consist of a robotics project with sub-goals that address the various steps of the course.

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This course covers the techniques and tools required for kinematic and dynamic modeling and control in manipulation robotics. The course content is structured around the following four areas:

1) Modeling of robotic manipulators: homogeneous transformations, direct and inverse geometric models, kinematic modeling, study of singularities

2) Introduction to the dynamics of robotic manipulators: Euler-Lagrange formalism, Newton-Euler formalism, algorithms for calculating dynamics

3) Articular and operational control in free space

4) Motion control in confined spaces: interaction and compliance models, position/force control, impedance and admittance control, motion generation, application examples.

Several examples of all these techniques will be covered in tutorials and hands-on exercises using MATLAB and V-REP on various manipulation robots (6- and 7-axis robots) as well as on a real humanoid robot named “Poppy.” 

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This course covers the techniques and tools necessary for kinematic and dynamic modeling and the control of robot manipulators. The lectures are organized around the following four topics:

1) Modeling of robot manipulators: homogeneous transformations, direct and inverse kinematic models, differential kinematic modeling, study of singularities

2) Introduction to the dynamics of robot manipulators: Euler-Lagrange formalism, Newton-Euler formalism, algorithms for computing dynamics

3) Joint space and operational space controls in free space

4) Control of movements in constrained space: interaction and compliance models, hybrid position/force control, impedance and admittance control, movement generation, application examples.

Several examples of all these techniques will be covered in supervised lab sessions and practical exercises using MATLAB and V-REP tools on various manipulation robots (6- and 7-axis robots) as well as on a real humanoid robot named "Poppy."

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A project conducted in partnership with a research laboratory and/or a company, highlighting the student's scientific skills, independence, and adaptability.

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A 5- to 6-month internship to be completed in a research laboratory or at a company, highlighting the student's scientific skills, independence, and adaptability.

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Preparation for entering the workforce.

The course is taught by a senior HR consultant, a former HR manager at major corporations, who draws on her extensive recruitment experience in her teaching.

A teaching approach that encourages the sharing of experiences and addresses students' situations and questions.

General insights on recruitment from A to Z, how to conduct a more effective job search, and perspectives on the approaches taken by end-hiring companies, recruitment firms, and staffing agencies.

Small-group mock job interviews with personalized debriefing led by the instructor.

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A tutorial course in specialized English and communicative English designed to foster professional autonomy in the English language.

To reinforce and consolidate the knowledge gained in the first year of the master's program.

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