FORMAT
LOCATION
Prerequisites
Basics in programming language
Capacity
About this minor
- Summary
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Learning Outcomes
The first objective of this module is to program a connected system. The case study is to program a Cherokey robot with the Arduino language to go from point A to point B, avoiding all obstacles in its path and being as fast as possible. It will have to cross the circuit first independently and then with a Bluetooth command.
The second objective is to design a sensor network for data collecting and integrating machine learning algorithms for making decision for smart environment (home, building, …).The evolution of sensor technology and communication protocols has made it possible to realize connected objects whose application domains are in full expansion.
This teaching unit aims to provide theoretical and practical bases for the development of connected objects by the design of the Cherokey 4WD arduino mobile robot. It is a versatile mobile robot that is compatible with popular microcontrollers such as the arduino UNO, arduino MEGA 2560, Romeo, etc.
In order to address the network aspect, sensor nodes (M5Stack family) will monitor physical data and forward the collected information to a central node (Raspberry Pi) in charge of decision for actuator nodes. The central node will rely on machine learning algorithms (KNN, RF, SVM, CNN, …) for making centralized decisions. The efficiency of different algorithm of ML will be compared.
This course will be divided into two parts:- Robot building and controlling and Bluetooth communication
- A first step will be devoted to understanding how the robot works, discovering the Arduino language and its functions.
- The second step is dedicated to the programming the Cherokey Robot and to find the function that will allow the robot to avoid obstacles.
- The last step will be to use the bluetooth function and the camera to move the robot.
- Design of sensor network
- the first step is to build a sensor network and a data collection infrastructure based on WIFI communication, a broker (MQTT) and nodered.
- The second step is to integrate machine learning (ML) algorithms for making decision bases on collected data. The different steps of the ML deployment will be studied
- At the end, the student will develop a solution for societal challenges for example for the management of energy (heating, electricity, …), water consumption, … for smart environment (home, building, …).
- Robot building and controlling and Bluetooth communication
- Lecturer
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- Cécile Belleudy, Maîtresse de conférence, Université Côte d'Azur, LEAT laboratory
- Jean-Marc Ribero, Professor, Université Côte d'Azur, LEAT laboratory
- Evaluation
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- Oral presentation (50 % of the final grade) - 11/12/2025 (duration: 15+10 min) - SophiaTech Campus, Templiers, room F201
- Final written report (50 % of the final grade) - Submission deadline (on Moodle): 15/12/2025
SCHEDULE FALL 2025
Mind the evaluation modalities and deadlines in the "Evaluation" tab above.
Date |
Time slot |
Course titleCourse title |
Lecturer |
Room |
9/10/2025 | 9h00-12h00 | Introduction/ Arduino Environment | JM Ribero | Campus SophiaTech Templiers, room F201 |
16/10/2025 | 9h00-12h00 | Concept of robot and first tests | JM Ribero | Campus SophiaTech Templiers, room F201 |
23/10/2025 | 9h00-12h00 | Mini-project - Tutorial | JM Ribero | Campus SophiaTech Templiers, room F201 |
06/11/2025 | 9h00-12h00 | Mini-project | JM Ribero | Campus SophiaTech Templiers, room F201 |
13/11/2025 | 9h00-12h00 | Sensor Communication | C. Belleudy | Campus SophiaTech Templiers, room B211 |
20/11/2025 | 9h00-12h00 | Sensor Network Building | C. Belleudy | Campus SophiaTech Templiers, room B211 |
04/12/2025 | 9h00-12h00 | AI integration and making decision | C. Belleudy | Campus SophiaTech Templiers, room B211 |
11/12/2025 | 9h00-12h00 | Evaluation : oral presentation | JM Ribero and C. Belleudy | Campus SophiaTech Templiers, room F201 |