Exercises

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

Lab 1

Tutorial 1

Week 3

Introduction to Webots, a high-fidelity, submicroscopic robotic simulator.

Lab 2

Tutorial 2

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.

Lab 3

Tutorial 3

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.

Lab 4

Tutorial 4

Week 6 & 7

Multi-level modeling of distributed robotic systems.

Lab 5

Tutorial 5

Week 8

Particle Swarm Optimization: application to benchmark functions and control shaping for single and multi-robot (in simulation).

Lab 6

Tutorial 6

Week 9

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

Lab 7

Tutorial 7

Week 10

Multi-robot systems coordination using market-based and threshold-based algorithms using Webots/point-simulator.

Lab 8

Tutorial 8

Week 11

Introduction to DISAL Arduino Xbee kit. Collective decision-making with static and robotic nodes (implementation in reality and Webots).

Lab 9

Tutorial 9

Week 12

Lab verification test

Week 13

No exercises.

Week 14

Course project.