ENSTA Bretagne : formation en robotique mobile et systèmes embarqués
Julien Ogor

Embedded systems

In this specialization path, the co-operative (apprentice) engineers learn to design and programme complex digital systems by using high-level languages.

ENSTA Bretagne’s specialization in embedded systems trains you in developing and enhancing these complex electronic architectures combining software, electronic hardware, specialist algorithms and telecommunications.

  • Automatics
  • Computer technology
  • Electronic systems
  • Information processing
  • ...

Cell phones, cars, ships, planes, rockets, robots…there are embedded systems hidden in the most common and complex products. They have to contend with key considerations (bearing on autonomy, weight, robustness and security for example) and are becoming ever more complex.

The first two years are spent learning a knowledge set in Information and Communication Science and Technology (mathematics, automation, signal, computer science, analog and digital electronics).

In the final year, teaching will be geared towards helping students to grasp the programming methods for real-time embedded systems and high-performance computing, design of artificial intelligence and the mechanisms of software-defined radio and networks of sensors.

Graduates will be able to find work in a range of sectors where these embedded systems are driving innovation: the defense industry, medicine, shipbuilding, aeronautics, automobile industry and energy.

ENSTA Bretagne : projet Deepdart de reconnaissance d'objets par des robots (intelligence artificielle)

Spotlight on the Deepdart project

The Deepdart project was created by Théo Lagrue and Tony Calvez, two co-operative (apprentice) engineers specializing in "embedded systems".
For several weeks, they worked on artificial intelligence for robot recognition.

Their project included different phases:

  • Learning: 12,000 images were acquired for the database, the numerous calculations being accomplished by a super-computer.
  • Execution: the execution algorithm enables the images to be analyzed (each image being divided into different segments which are analyzed and compared to the data acquired in the previous phase).
  • Detection: when several parts of the image are recognized, an algorithm standardizes the different zones to detect the complete object.

This project was a real experiment in the field of intelligence and embedded systems. It was a challenge to accomplish so many calculations on Nintendo Switch architecture and the result was way above our expectations.


Claire’s testimony, Scrum Master, 2015 co-operative engineer graduate, specializing in embedded systems.

Examples of final year projects accomplished (6 month mission in company)

  • application in the automobile sector : "Development of electromagnetically compatible, risk analysis and design rule definition assistance tools" for PSA Group,
  • application in robotics : "Study and optimization of robotic systems to localize and identify underwater mines" for DGA Techniques Navales,
  • application in the naval sector : "Study of the operation reliability and safety progress path for a submarine program" for Naval Group