Distributed Intelligent Systems Course Projects 1 General ... - EPFL

Distributed Intelligent Systems and Algorithms Laboratory

EPFL

Distributed Intelligent Systems Course Projects 1 General information Distributed Intelligent Systems will involve a 60h course project (this should include reading, implementation, reporting, oral defense of the project, and reviewing the report of another student team). Students will choose a project from a list of approved topics to be distributed during the sixth week. Projects will be carried out in groups of two (if needed) and three (default) students belonging as much as possible to different teaching sections. Each of the team members will have to defend part of the project in front of the audience and will also be asked to serve as a reviewer for another student project (i.e. ask questions during the defense session). Every project will be supervised by a Teaching Assistant or a Support Staff member. Definitive assignment of course projects will be communicated during the eight week, based on the preferences expressed by the students. Students will be expected to contact their project supervisor by the end of the same week to begin planning their work schedule. During the tenth week, a common kick-off session for the implementation of the course project will be organized during the Wednesday morning exercise hours. At the end of the very same week, students will be required to submit a project work plan describing their understanding of the project topic and its related literature, as well as a concrete implementation plan (1 page total). This will allow their project supervisor to give them feedback in terms of implementation feasibility, problem understanding, and time planning.

2 Topic list The acronyms used for the different sections below are defined as follows:  CSE – Computational Science and Engineering  EL – Electrical Engineering  GM – Mechanical Engineering  IN – Computer Science  MES – Energy Management and Sustainability  MT – Microengineering  SIE – Environmental Science and Engineering  SV – Life Sciences and Technologies  SC – Communication Systems  ED – Doctoral School Learning a ‘following and chain-building’ behavior using PSO Using PSO, optimize a robot-following and chain-creation behavior for the real e-puck platform. The goal is to create a long chain of robots. First, in Webots, develop a Braitenberg type algorithm and implement your learning strategy. Then, implement your optimized behavior on the real platform using 3-4 e-puck robots. Discuss the strengths and weaknesses of this approach and identify possible changes to the heuristics which might improve the transition from the simulated e-puck to the real one. Group leader: Milos Vasic, [email protected] Group 1: Dirk Lauinger (MES), Eloisa Olivarria (IN), Alexandru Ardelan (IN) Group 2: Christoph Körner (EL), Ethienne Thalmann (GM), Alice Concordel (GM) JNP, AM, DIS Course Projects, 6.11.2013

1

Distributed Intelligent Systems and Algorithms Laboratory

EPFL

Group 3: Mikaël Vaivre (SIE), Guillaume Jornod (SIE), Joachim Hugonot (IN)

Multi-level modeling of distributed robotic system In this project, you will explore several levels of a multi-level modeling methodology applied to a distributed robotic system. You will consider a case study concerned with a swarm of simple robots that has to maintain wireless connectivity. First you will implement a submicroscopic level consisting on a realistic simulation (Webots) of the task. You will then develop microscopic and macroscopic models of the task using the robots’ controller as a starting point and quantitatively compare results between levels. Group leader: José Nuno Pereira, [email protected] Group 1: Titus Cieslewski (MT), Ivan Slijepcevic (CSE), Federico Martinez (CSE) Group 2: Duarte Dias (ED), Fabio Stradelli (CSE), Zeynab Talebpour (ED)

Distributed network localization The goal of this project is to develop a solution which localizes a network of static sensor nodes in point-simulation (Matlab). The nodes can sense noisy distances to neighboring nodes, and are also capable of communicating to neighboring nodes. At least three nodes in the whole network are localized at the beginning of the simulation, and the rest of the nodes are unlocalized. The sensing and communication are asynchronous in time, and all position calculations are distributed over the network. You will be given an initial Matlab script with the basic elements in place, and your task is to extend this code (with your method of choice), as well as perform a quantitative analysis of your solution. Group leader: Bahar Haghighat, [email protected] Group 1: Laurent Fasnacht (CSE), Bernard Maccari (IN), Matteo Pagliardini (IN) Group 2: Adèle Dramé-Maigné (SV), Le Tuan Anh (IN), Soudjad Cassam-Chenaï (SC) Group 3: Andrea Di Blasio (CES), Sander Kromwijk (IN), Florian Gandor (SIE)

Simulated flocking algorithm for e-pucks The goal of the project is to implement, test and analyze a flocking algorithm for e-pucks on the Webots simulator. The algorithm should provide the robots with the ability 1) to avoid obstacles within the arena while retaining the collective formation, and 2) to maintain collective formation while 2 different flocks of robots cross each other moving in opposite directions. The analysis should evaluate the performance and the scalability of the algorithm for increasing dimensions of the e-pucks swarm. Group leader: Jorge Soares, [email protected] Group 1: Remy Bertomeu (IN), Julie Pinchon (SIE), Didier Parat (IN) Group 2: Silvia-Mirela Marcu (IN), Jonathan Giezendanner (SIE), Stéphane Martin (IN) Group 3: Christopher Finelli (SV), Paul Cornioley (SIE), Jonas Haggenjos (GM)

JNP, AM, DIS Course Projects, 6.11.2013

2

Distributed Intelligent Systems and Algorithms Laboratory

EPFL

ACO-based robust routing in sensor networks You will be given 10 MICAz sensor nodes. The nodes will be spread out over some area, forming a multi-hop network, cooperating in a typical distributed sensing task (e.g., measuring the temperature of a room). Suppose that the network is heterogeneous - some nodes take longer to process messages, some send messages less frequently (e.g., to preserve energy), and so on. Your goal is to maintain at each node the shortest route (in terms of time) to the network sink, accounting for all of these unknown and possibly changing delays. To this end you will implement and analyze the performance of a routing algorithm based on the Ant Colony Optimization metaheuristic, in which virtual ants are sent through the network as packets, using a virtual pheromone trail to mark efficient routes to the sink. Group leader: Chris Evans, [email protected] Group 1: Fabian Alexander Bernhard (SIE), Georg Schölly (SC), Manuel Schmid (SIE) Group 2: Simona Traykova (IN), Jialei Jin (IN), Chun Xie (GM) Group 3: Orianne Rollier (IN), João Craveiro (IN), Patrick Osterwalder (SIE)

JNP, AM, DIS Course Projects, 6.11.2013

3