Lecture

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. From real to virtual ants: Ant System (AS), the first combinatorial optimization algorithm based on ant trail/following principles. Application to a classical operational research problem: the Traveling Salesperson Problem (TSP).

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Week 2

TOPIC

From AS to Ant Colony Optimization (ACO). Ant-based algorithms (ABC, Ant-Net) applied to routing in telecommunication networks.

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Week 3

TOPIC

Introduction to mobile robotics: basic concepts centered around the differential drive vehicle used 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.

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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.

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Week 5

TOPIC

Collective movements in natural societies; focus on flocking phenomena. Collective movements in artificial systems: Reynolds’ virtual agents (Boids) and behavior-based algorithms for flocking and formation in multi-robot systems.

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Week 6

TOPIC

Graph-based distributed control for continuous consensus algorithms (spatial rendezvous, formation).

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Week 7

TOPIC

Introduction to multi-level modeling techniques (underlying methodological framework, levels, assumptions, principles). Linear and nonlinear modeling case studies.

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Week 8

TOPIC

Calibration of model parameters; an additional challenging multi-level modeling case study (distributed seed assembly). Combined modeling and machine-learning methods for control optimization; diversity and specialization metrics.

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Week 9

TOPIC

Introduction to unsupervised multi-agent machine-learning techniques for automatic design and optimization: terminology and classification, Particle Swarm Optimization (PSO), performance comparison with Genetic Algorithms. Application of machine-learning techniques to automatic control design and optimization of single-robot systems.

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Week 10

TOPIC

Noisy and expensive optimization problems; noise-resistant algorithms. Application of machine-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).

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Week 11

TOPIC

Division of labor and task-allocation mechanisms: threshold-based algorithms and market-based algorithms.

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Week 12

TOPIC

Division of labor and task-allocation mechanisms: comparisons between threshold-based and market-based algorithms. Distributed environmental sensing using static wireless sensor networks.

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Week 13

TOPIC

Distributed environmental sensing using static wireless sensor networks.

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Week 14

TOPIC

Distributed environmental sensing using robotic sensor networks. General take home messages of the course. Discussion about student feedback for the course.

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