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 exercises.
Week 2
Trail laying and following mechanisms, emphasizing SI concepts; Ant Colony Optimization.
Week 3
Introduction to Webots, an open-source, high-fidelity robotic simulator.
Week 4
Localization methods (odometry and feature-based localization) in Webots.
Week 5
Localization methods (odometry and feature-based localization) in Webots.
Collective movements in Matlab and Webots.
Week 6
Collective movements in Matlab and Webots.
Kick-off of the course project
Week 7
Multi-level modeling of distributed robotic systems ( introduction, Matlab and Webots)
Week 8
Multi-level modeling of distributed robotic systems ( continuation, Matlab and Webots).
Week 9
Particle Swarm Optimization: application to benchmark functions and control shaping for single robot (SwarmViz, Webots).
Week 10
Particle Swarm Optimization application to noisy problems: benchmark functions and multi-robot problems (Webots, Matlab).
Week 11
No exercises.
Week 12
Multi-robot systems coordination using market-based and threshold-based algorithms using Webots.
Week 13
Distributed environmental sensing with static and mobile sensor networks (Webots/Matlab).
Week 14
Course project assistance.