Each exercise will consist of a laboratory session. Usually, the lab part will focus on experimental work using simulators. 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 lab (single robot).
Week 5
Collective movements (flocking, formations) in simulation.
Week 6
Multi-robot systems coordination using market-based and threshold-based algorithms.
Week 7
Distributed environmental sensing with static and mobile sensor networks.
Week 8
Homework 1 assistance.
Week 9
Multi-level modeling of distributed robotic systems – Introduction
Week 10
Multi-level modeling of distributed robotic systems – Advanced
Week 11
Particle Swarm Optimization: application to benchmark functions and control shaping for single robot.
Week 12
Particle Swarm Optimization application to noisy problems: benchmark functions and multi-robot problems.
Week 13
Homework 2 assistance.
Week 14
Homework 2 assistance.