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.
Trail laying and following mechanisms, emphasizing SI concepts; Ant Colony Optimization
Introduction to Webots, a high-fidelity, submicroscopic robotic simulator.
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.
Multi-robot localization, coordinated and collective movements in microscopic model (matlab/point-simulator visualized with Webots)/Webots, includes some collective movement analysis.
Multi-robot systems coordination using market-based and threshold-based algorithms using Webots/Matlab/point-simulator.
Practical lab verification test, subject: lab 1 to 5.
Course project Kick-off session.
Multi-level modeling of distributed robotic systems.
Particle Swarm Optimization: application to benchmark functions and control shaping for single and multi-robot (in simulation).
Particle Swarm Optimization application to noisy problems: benchmark functions and multi-robot problems.
Introduction to Mica-z sensor nodes. Simulated and real sensor and actuator networks. Distributed sensing with static, mobile, and robotic nodes.
Project session: last clarifications before report submission.
Course project defenses (SG 0213).