Each exercise will consist of a laboratory session. Usually, the lab part will focus on experimental work using simulators or real hardware platforms. The student will have to collect data and sometimes write a few lines of code. The balance between practice and theory will, of course, be completely dependent on the topic of the lab.
The labs are posted on Moodle a few days before the lab session.
Week 1
No excercises.
Week 2
Trail laying and following mechanisms, emphasizing SI concepts; Ant Colony Optimization
Week 3
Introduction to Webots, a high-fidelity, submicroscopic robotic simulator.
Week 4
Introduction to the e-puck robot. Illustrate key concepts of the course for basic behavior using different reactive control architectures (Artificial Neural Network, linear Braitenberg, behavior-based, rule-based). Simple localization algorithms based on odometry.
Week 5 & 6
Multi-robot localization, coordinated and collective movements in microscopic model (matlab/point-simulator visualized with Webots)/Webots, includes some collective movement analysis.
Week 6 & 7
Multi-level modeling of distributed robotic systems.
Week 8
Particle Swarm Optimization: application to benchmark functions and control shaping for single and multi-robot (in simulation).
Week 9
Particle Swarm Optimization application to noisy problems: benchmark functions and multi-robot problems.
Week 10
Multi-robot systems coordination using market-based and threshold-based algorithms using Webots/point-simulator.
Week 11
Introduction to DISAL Arduino Xbee kit. Collective decision-making with static and robotic nodes (implementation in reality and Webots).
Week 12
Week 13
No exercises.
Week 14
Course project.