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.

Lab 1

Tutorial 1

Week 3

Introduction to Webots, an open-source, high-fidelity robotic simulator.

Lab 2

Tutorial 2

Week 4

Localization lab (single robot).

Lab 3

Tutorial 3

Week 5

Collective movements (flocking, formations) in simulation.

Lab 4

Tutorial 4

Week 6

Multi-robot systems coordination using market-based and threshold-based algorithms.

Lab 5

Tutorial 5

Week 7

Distributed environmental sensing with static and mobile sensor networks.

Lab 6

Tutorial 6

Week 8

Homework 1 assistance.

Week 9

Multi-level modeling of distributed robotic systems – Introduction

Lab 7

Tutorial 7

Week 10

Multi-level modeling of distributed robotic systems – Advanced

Lab 8

Tutorial 8

Week 11

Particle Swarm Optimization: application to benchmark functions and control shaping for single robot.

Lab 9

Tutorial 9

Week 12

Particle Swarm Optimization application to noisy problems: benchmark functions and multi-robot problems.

Lab 10

Tutorial 10

Week 13

Homework 2 assistance.

Week 14

Homework 2 assistance.