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 competely dependent on the topic of the lab.
Week 1
No lab
Week 2
Trail laying and following mechanisms, emphasizing SI concepts; Ant Colony Optimization
Week 3
Introduction to Webots, a realistic, embodied, sensor-based 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
Multi-robot localization, coordinated and collective movements in microscopic model (matlab/point-simulator visualized with Webots)/Webots, includes some collective movement analysis.
Week 6
Multi-robot systems coordination using market-based and threshold-based algorithms using Webots/Matlab/point-simulator.
Week 7
Introduction to Mica-z sensor nodes. Simulated (Webots with Omnet++ plugin) and real (e-puck and Mica-z) sensor and actuator networks: networking static sensor nodes with mobile robots for performing collective decisions.
Week 8
Practical lab verification test, subject: lab 1 to 6.
Lab verification test I assignment
Week 9
Multi-level modeling of distributed robotic systems.
Week 10
Particle Swarm Optimization. Application to benchmark functions and control shaping for single robot (in simulation).
Week 11
Particle Swarm Optimization application to multi-robot systems (Webots), task obstacle avoidance, tight collaborative task (e.g., formation 2 robots).
Week 12
Distributed sensing with static, mobile, and robotic nodes (implementation in Webots).
Week 13
Practical lab verification test, subject: lab 7 to 10.
Lab verification test II assignment