M2 Smart Aerospace and Autonomous Systems

M2SAAS Courses

• Apply the scientific and technical knowledge acquired during the program by carrying out a 5 to 6 months project in a company or research lab.
• Develop professional skills in project management, autonomy, scientific communication, and teamwork within an academic or industrial environment.
• Conduct a research or engineering project by defining a problem statement, proposing a methodology, and implementing the theoretical and experimental tools required to achieve the expected objectives.
• Analyze, interpret, and showcase the results obtained, while following a rigorous and scientific approach.
• Write a structured and comprehensive internship report, detailing the context, objectives, methodology, results, and perspectives of the work carried out.
• Present and defend the work during an oral examination at the end of the semester, clearly explaining the contributions, the approach taken, and the significance of the results.
• Demonstrate the level of competence expected from a Master's graduate, assessed through the oral defense, the written report, and the supervisor’s evaluation of the work performed.

M2SAAS courses

The master’s project represents an opportunity to integrate knowledge and skills that have been acquired over the course of the program. It regroups a student team (2 or 3), working together in order to study and develop a topic related to the Master theme and proposed by professors. At first, the students summarize the state of art then study the problematic, find the solution which is implemented and validated.

Based on the proposed project. The reference is provided for each topic by professors.

- Permettre aux étudiants de découvrir d’autres domaines de savoir et de pratique.    - Approfondir des centres d’intérêt personnels ou professionnels.             - Développer des compétences transversales utiles dans tous les milieux (communication, travail en équipe, créativité, esprit critique…).           - Encourager l’ouverture internationale, la recherche ou l’engagement citoyen.   - Répondre à des sollicitations ministérielles sur la diversification et la transformation pédagogique.

Les UE libres sont choisies dans le périmètre des formations accréditées par Paris-Saclay ou par l’établissement référent.

En Anglais: 

Niveau B1/B2 du CECRL : être capable de s’exprimer à l’oral et à l’écrit dans tous types de situations, comprendre tous types de documents sur une thématique donnée.

En FLE

Contenu (français langue étrangère) : 

• Se présenter 
• Se repérer dans le temps 
• Se déplacer et se
• repérer dans l’espace 
• Parler de soi 
• Parler de ses goûts 
• Raconter un évènement 

En Anglais :

Objectifs pédagogiques

Poursuivre et enrichir ses connaissances et compétences linguistiques en anglais dans les domaines étudiés autour des 4 activités langagières que sont la compréhension orale, la production orale, la compréhension écrite et l’expression écrite.

Une préparation au TEST TOEIC s’articulera autour d’un renforcement grammatical et d’un entraînement spécifique à la compréhension de l’anglais à l’oral et à l’écrit. 

Contenu et plan :

2 thématiques dans le semestre :

• La protection des données (Data Privacy issues)
• L’intelligence artificielle

+ Entraînement au TEST TOEIC en laboratoire de langues

Pour les étudiants étrangers, apprendre les rudiments de la langue française en tant qu’adulte. 

Pour les étudiants francophones, perfectionnement en langue anglaise.

1 TD de 2 heures par semaine.

- Les cours entraînent les étudiants au 4 compétences (compréhension de l’oral et de l’écrit, production orale et écrite)

- Présentations orales individuelles de 5mn en début de cours

- Etude de documents (textes, audio, vidéo) et discussion ouverte sur les thématiques qu’ils abordent.

-Un travail spécifique en laboratoire de langues (24 postes maxi) sera proposé aux étudiants pour s’entraîner au TEST TOEIC sur un logiciel dédié (Test Simulator).

PAS D’EXAMEN FINAL. Le contrôle continu de chacune des 4 compétences compte pour 100% de la note.

Seulement 2 absences non justifiées sont autorisées. En cas d’absences régulières non justifiées, l’étudiant passe automatiquement en session de rattrapage.

FLE:

- Communication progressive du français (débutant), CLE International 
- Grammaire progressive du français, CLE international 
- Vocabulaire illustré (débutant), Hachette

Anglais

La grammaire anglaise de l’étudiant, S.BERLAND-DELEPINE & J-L. DUCHET, éditions OPHRYS

Nombreux sites en ligne (news, TED Talks, exercices) indiqués en fonction des thématiques abordées

2 brochures de documents supports remises aux étudiants à la première séance et à apporter à chaque cours

Linear algebra, differential calculus

Course Content Outline

1. Mathematical Tools
   • Basic notions of Euclidean space, orthonormal bases, affine maps
   • Rotations and Euclidean displacements
   • Lie groups and Lie algebras (SO(3), D(E))
   • Angular velocity and change of frames
2. Rigid Body Kinematics
   • Configuration and motion of a rigid body
   • Velocity field of a rigid body
   • Acceleration field
3. Rigid Body Kinetics
   • Mass distribution, center of inertia
   • Inertia tensor and inertia matrix
   • Kinetic moment (momentum) and kinetic energy
4. Rigid Body Dynamics
   • Dynamic wrench
   • Relation between kinetic and dynamic moments
   • Dynamic principle and inertial frames
5. Dynamics of Flying Objects
   • Structure of dynamic equations
   • Natural forces (gravity, aerodynamics)
   • Control forces and modeling strategies
6. Change of Observation Frame
   • Composition of velocities and accelerations
   • Transformation of kinetic and dynamic wrenches
   • Dynamic principle under frame change
7. Systems of Rigid Bodies
   • Kinematic links and constraints
   • Wrenches associated to mechanical links
   • Lagrange’s equations via the Principle of Virtual Power
8. Linearization & Vibration Analysis
   • Stability and linearization around equilibria
   • Divergence, flutter, and resonance instabilities
   • Basics of linear vibration modes and analysis

This course introduces the fundamental mechanical modeling of unconstrained rigid bodies used in aerial and space systems. It covers essential mathematical tools, followed by the kinematics, kinetics, and dynamics of a single rigid body. Specific features related to flying objects and their control are also addressed, as well as frame changes. The course then extends to systems of rigid bodies, including basic vibration and stability analysis. Emphasis is placed on understanding mechanical laws and the physical meaning of model terms. This module complements the Aerial Robots course, which focuses on modeling for control purposes.

The course is provided through lectures, tutorials, and practical sessions.

The practical sessions are graded. The final grade for the module is calculated as follows:

Session 1: Continuous assessment grade: 30% practical work + 70% exam.

Session 2 (retake): 30% of the practical work grade from Session 1 + 70% retake exam.

http://www.personnels.univ-evry.fr/jeanlerbet/wp-content/uploads/sites/5/2017/10/SAAS_021017.pdf

Keywords:

National Airspace, UAV, challenges
Description du contenu de l'enseignement
Abstract
This course presents current and emerging UAV (Unmanned Aerial Vehicles) systems and the implications and opportunities of UAV in the 21st century. Students will learn how today’s UAV systems operate, the challenges facing them, and the markets in which they are and could be used. Finally, attendees will be introduced to the future of UAV systems, how they will differ in capability from those in service today and the emerging technologies that will enable these capabilities.

Course outline
- Current UAV systems
- Emerging UAV systems and opportunities
- Integration into the national airspace
- Challenges.

The course is provided through lectures, tutorials, and practical sessions.

The practical sessions are graded. The final grade for the module is calculated as follows:

Session 1: Continuous assessment grade: 30% practical work + 70% exam.

Session 2 (retake): 30% of the practical work grade from Session 1 + 70% retake exam.

R. Austin 'Unmanned aircraft systems' Wiley 2011 P. Fahlstrom 'introduction to UAV systems' Wiley 2012

Signal and image processing

Course description:

Perception is one of the key processes in autonomous systems. It is based on the integration of perceptual information into a coherent description of the world. Perception consists of collecting, processing, as well as combining data that provide from embedded sensors in order to perform a given task (obstacle detection, scene reconstruction, object recognition, etc.). The objective of the course is to focus on the two main current challenges in perception: vision and fusion. First, the course discusses various sensors and sensor technologies relevant to intelligent and autonomous systems, modern industries and smart product. Then, the course will particularly focus on high level vision algorithms that are based on stereovision or motion analysis. Finally, it discusses the concept and techniques of multi sensor fusion.

Course contents:

General concepts about perception for autonomous systems

Human versus artificial perception.

Perception + decision + action paradigm.

Sensor and sensing. Sensor characteristics and technologies. 

The role of sensors and categorization.

Advanced Vision concepts for autonomous systems

Basic image analysis (enhancement, edge detection, segmentation).

Features (SIFT, SURF, etc.) for image matching.

Matching algorithms.

3D vision and theory of stereovision. 

2D and 3D Motion analysis: optical flow, egomotion estimation.

Tracking of moving objects

The course is provided through lectures, tutorials, and practical sessions.

The practical sessions are graded. The final grade for the module is calculated as follows:

Session 1: Continuous assessment grade: 30% practical work + 70% exam.

Session 2 (retake): 30% of the practical work grade from Session 1 + 70% retake exam.

Some of the references of interest are (but not limited to):

• H.R Everett, Sensors for Mobile Robots Theory and Applications, A.K Peters, Ltd., 1995.
• R. Joshi, and A. C. Sanderson, Multisensor Fusion: A Minimal Representation Framework, World Scientific, 1999.
• D. L. Hall, Mathematical techniques in multisensor data fusion. Boston: Artech House, 1992. 
• Brian D. O. Anderson, John B. Moore, Optimal Filtering. Information and System Science Series. Thomas Kailath Editor, 2005.
• R. Szeliski_, Computer Vision: Algorithms and Applications_, springer, 2010.
• T. Watanabe. High-Level Motion Processing, MIT Press, 1998.

UAV dynamics, classical automatic control, Linear estimation algorithms, Graph theory.

Keywords

Multi-UAV-Multi-Robot Coordination- Control Architecture- Mission coordination planner- Muti-agents distributed estimation and control architectures- Ros interface 

 Course description

Nowadays, multi UAV/UGV operate in partially or completely unknown environments. To achieve this complex task, they must under operational constraints coordinate their control actions and their trajectories to avoid damaging collisions while achieving their goal tasks.

The purpose of this course is to provide students with a broad overview of the related modeling and control challenges involved in the coordination of multiple UAV/UGVs.  The aim of this course is to introduce key concepts in cooperative flight control, mission planning, and autonomous decision-making to ensure safe and efficient operation of UAV/UGV teams in complex and unpredicted environments.

Course content

1. Multi-UAV/UGV Kinematic and dynamical modeling
2. Multi-UAV/UGV  mission planner.
3. Decentralized architecture for multi-robot systems 
4. Control architectures in UAV/UGV 
5. Path  and motion planning methods
6. Navigation and obstacle mapping 
7. Tasks allocation scheme in multi-UAV/UGV context. 
8. Distributed estimation algorithms for multi-UAV/UGV
9. Distributed control architecture for multi- UAV/UGV 
10. Multi-agent models for robots’ control and cooperation. 
11. Sensor network
12. Ros: an open-source robot operating system

The course is provided through lectures, tutorials, and practical sessions.

The practical sessions are graded. The final grade for the module is calculated as follows:

Session 1: Continuous assessment grade: 30% practical work + 70% exam.

Session 2 (retake): 30% of the practical work grade from Session 1 + 70% retake exam.

A Proposal of Multi-UAV Mission Coordination and Control Architecture, Juan Jesús Roldán, Bruno Lansac, Jaime del Cerro, Antonio Barrientos, Springer, 2015

Algorithmic- C,C++, Micro-processor and micro control architecture- Digital signal processing. Digital signal processing algorithms.

Keywords

Software, embedded systems, avionics specification,  UAV, software architecture, embedded control,  Digital signal filtering and processing, Embedded sensor data fusing. 

Course description

The purpose of this course is to introduce different software architectures and their applications in real-world scenarios. The course will highlight and focus on the presentation of the different embedded software architectures   particularly those developed for avionics specifications and Unmanned Aerial Vehicles (UAVs). Students will explore in this course fundamental principles of software design, embedded control system integration, signals filtering, sensors fusing data’s communication protocols, and real-time processing in aeronautical systems. The course emphasizes different aspects such as safety, reliability, and scalability in UAV software architectures. Case studies including simulation exercises, and practical projects will be also handled by the students.

Course Outline

1. Specifying functional requirements of application software.
2. Classical software architectures and development cycles.
3. Different targets panel in embedded software architecture.
4. Model-Based Development and Verification.
5. VHDL programming Language.
6. Embedded signal filtering methods.
7. Embedded systems in avionics and UAV systems.
8. Development of embedded control architecture in UAV system.

The course is provided through lectures, tutorials, and practical sessions.

The practical sessions are graded. The final grade for the module is calculated as follows:

Session 1: Continuous assessment grade: 30% practical work + 70% exam.

Session 2 (retake): 30% of the practical work grade from Session 1 + 70% retake exam.

• Software Processes and Life Cycle Models: An Introduction to Modelling, Using and Managing Agile, Plan-Driven and Hybrid Processes Ralf Kneuper 
• Model driven engineering in practice Jordi Cabot 
• Developing Safety-Critical Software: A Practical Guide for Aviation Software and DO-178C Compliance Leanna Rierson 
• Advances in Aeronautical Informatics: Technologies Towards Flight 4.0.

Programming, Discrete mathematics, math model, probability.

Keyword:

 Autonomous aircraft, obstacle avoidance, Flight plan
Abstract
A basic problem which has to be solved by Aerial autonomous vehicles is the problem of planning. By planning we mean the generation and execution of a plan to move from one location to another location in space to accomplish a desired task. There are four principal tasks: Path planning (Determining an optimal path for vehicle to go while meeting certain objectives and constraints), avoiding obstacles and collision, Trajectory Generation (Determining an optimal control maneuver to take to follow a given path or to go from one location to another) and Task Allocation and Scheduling (Determining the optimal distribution of tasks amongst a group of agents, with time and equipment constraints). Moreover, it is desirable that the plan makes optimal use of the available resources to achieve the goal optimizing some ‘cost’ measure: the time required for the execution of the trajectory, its length, the deviation from a reference trajectory, control effort. In this course, we will focus on deterministic and probabilistic planning approaches.

Course outline
- Path planning
- Obstacle and collision avoidance
- Trajectory generation
- Task allocation and scheduling
- Case studies.

The course is provided through lectures, tutorials, and practical sessions.
The practical sessions are graded. The final grade for the module is calculated as follows:
Session 1: Continuous assessment grade: 30% practical work + 70% exam.
Session 2 (retake): 30% of the practical work grade from Session 1 + 70% retake exam.

Y. Bestaoui Sebbane 'Planning and decision making of aerial robots', Springer 2014 Y. Bestaoui Sebbane ‘lighter than air robots’, Springer 2012.

Linear Control.

keywords
optimal control, Lyapunov, Linear control, back-stepping, Robust control
Description of the course content
Abstract
This course covers nonlinear control design and analysis methods used on Unmanned Aerial Systems. It covers robust stability analysis, optimal control and non-linear control theory used to project state feedback into output feedback. This part forms baseline control algorithms that are augmented with adaptive control in the second part. Lyapunov stability theory is followed by an introduction to the design and analysis of adaptive control systems. Key design points are discussed and illustrated through simulation examples. This course ends with an overview of open problems and future research directions.

Course outline
- Flight modes and Linear control
- Robust control
- Optimal control
- Lyapunov approach
- Back-stepping
- Adaptive control
- Open problems.

The course is provided through lectures, tutorials, and practical sessions.

The practical sessions are graded. The final grade for the module is calculated as follows:

Session 1: Continuous assessment grade: 30% practical work + 70% exam.

Session 2 (retake): 30% of the practical work grade from Session 1 + 70% retake exam.

W. Durham 'aircraft flight dynamics and control', Wiley 2013 

B. Etkin and L.D. Reid. Dynamics of flight. Stability and control. Wiley,1996. 

D. P. Raymer. Aircraft design : a conceptual approach. AIAA, 1989. 

G. F. Franklin et al ‘ Feedback control of dynamic systems ’ AddisonWesley,1991 D. McLean ‘ Automatic flight control systems ’ Prentice Hall, 1990 

J. F. Magni et al ‘ Robust flight control ’ Springer, 1997

Keyword: AI, Neural networks, learning, Aeronautics field

Contents

Artificial intelligence has recently found many applications in aerospace and remote sensing. The objective of this course is to present artificial intelligence algorithms and the needs of aeronautics in this field. How can AI bring improvement in target detection, identification, recognition and tracking, including efficiency assessment?

Content Outline :

-Overview of aerospace field

- Introduction to Artificial Intelligence

- AI application in aerospace

- Introduction to neural networks

- Supervised and unsupervised learning

- Embedded AI in aircraft

The course is provided through lectures, tutorials, and practical sessions.

The practical sessions are graded. The final grade for the module is calculated as follows:

Session 1: Continuous assessment grade: 30% practical work + 70% exam.

Session 2 (retake): 30% of the practical work grade from Session 1 + 70% retake exam.

Using of Artificial Neural Networks (ANN) for Aircraft Motion Parameters Identification, Anatolij Bondarets, Olga Kreerenko, CCIS 43, pp. 246–256, 2009, Springer

Basic knowledge of linear algebra, mathematical statistics, and calculus is required. Knowledge of signals and systems, estimation theory, and electronics.

Contents:
•Definition and Components of Sensor Fusion
•Identification of multisensor data fusion principles, algorithms, and architectures
•Description of the advantages of multisensor data fusion for object discrimination and state estimation
•Identification of the differences between linear and nonlinear models and their implications on sensor fusion,
•Sensors and Least Squares Criterion
•The Filtering Problem and Kalman Filtering
•Formulate sensor and data fusion approaches for many practical applications :Aerospace, automative.

The goal of this course is to provide a strong introduction to the topic with an emphasis on methods that are useful for developing practical solutions to sensor fusion problems. It describes sensor and data fusion methods that improve the probability of correct target detection, classification, and identification. Sensor fusion refers to computational methodology which aims at combining the measurements from multiple sensors such that they jointly give more information on the measured system than any of the sensors alone. Sensor fusion has applications in many different areas of daily life and plays an important role in modern society. For example, it is used to interpret traffic scenes in autonomous cars, for navigation and localization in robotics and for controlling drones in aerial photography and deliveries

The course is provided through lectures, tutorials, and practical sessions.

The practical sessions are graded. The final grade for the module is calculated as follows:

Session 1: Continuous assessment grade: 30% practical work + 70% exam.

Session 2 (retake): 30% of the practical work grade from Session 1 + 70% retake exam.

1. Richard R. Brooks, Sundararaja S. Iyengar “Multi-Sensor Fusion: Fundamentals and Applications with Software”, 1997, Har/Dskt edition

2. Samir Ayman, “Sensor Fusion Using Kalman Filter for a Quadrotor-Attitude Estimation: Basics, Concepts, Modelling, Matlab Code and Experimental Validation”, 2016, Lambert edition

3. Jitendra R. Raol , “Multi-Sensor Data Fusion with MATLAB”, 2010, CRC Press.