Lecture notes and reading material
Preliminary lecture notes will be available in PDF format before the class (usually Monday evening) while definitive lecture notes will be available only after the class has been held, in a timely fashion (usually, at latest a couple of days after the lecture).
Lecture notes will be complemented by possible reading material listed on the syllabus and further pointers, all available on the student area. Due to copyright issues, electronic copies of the material are only available to EPFL students officially enrolled in this course. Students interested in downloading this material can do so from the student area by logging in using with their GASPAR account.
Week 1
TOPIC
Course organization (credits, workload, logistics) and content overview. Introduction to Swarm Intelligence (SI) and key principles, natural and artificial examples. Foraging, trail laying/following mechanisms. Open-space, multi-source foraging experiments: biological data and microscopic models. Collective movements, flocking in natural societies.
LECTURE
Week 2
TOPIC
Ant-based algorithms applied to classical operational research problems (e.g., TSP) and routing in telecommunication networks: the AS, ACS, ACS-3-Opt, and Ant-Net algorithms; the Ant Colony Optimization (ACO) metaheuristic as an example of successful translation of Swarm Intelligence principles to powerful metaheuristic algorithms.
LECTURE
Week 3
TOPIC
Introduction to mobile robotics: basic concepts centered around the differential drive vehicle considered in the course (e-puck) and the high-fidelity, open-source robotic simulator (Webots). Introduction to control architecture for mobile robots with special focus on reactive control architectures.
LECTURE
Week 4
TOPIC
Localization methods in mobile robotics: positioning systems, odometry-based and feature-based localization. Sources of localization uncertainties and corresponding handling methods for mobile robots.
LECTURE
Week 5
TOPIC
Collective movements in artificial systems: Reynolds’ virtual agents (Boids), experiments with multi-robot systems on flocking and formation (behavior-based); graph-based distributed control for spatial consensus (rendez-vous, formation).
LECTURE
Week 6
TOPIC
Division of labor and task-allocation mechanisms: threshold-based and market-based algorithms.
LECTURE
Week 7
TOPIC
Distributed sensing: introduction to basic concepts (sensing, mapping); static and mobile sensor networks.
LECTURE
Week 8
TOPIC
Distributed sensing: introduction to basic concepts (exploration, search, coverage); robotic sensor networks (focus on gas sensing, 2D vs. 3D).
LECTURE
Week 9
TOPIC
Introduction to multi-level modeling techniques (underlying theoretical framework, levels, assumptions, principles). Linear and nonlinear modeling case studies.
LECTURE
Week 10
TOPIC
Calibration of model parameters. Additional selected multi-level modeling case studies. Combined modeling and learning methods for control optimization; diversity and specialization metrics.
LECTURE
Week 11
TOPIC
Introduction to evaluative machine-learning techniques for automatic design and optimization: terminology and classification. Particle Swarm Optimization (PSO): algorithm and performance evaluation. Application of metaheuristic learning techniques to automatic control design and optimization of single-robot systems.
LECTURE
Week 12
TOPIC
Noisy and expensive optimization problems. Application of metaheuristic learning techniques to automatic control design and optimization of multi-robot systems. Specific issues for automatic control design and optimization in distributed systems (e.g., credit assignment problem).
LECTURE
Week 13
TOPIC
Selected topics in distributed intelligent systems. General take home messages of the course.
LECTURE
Week 14
TOPIC
Course project presentations.
LECTURE