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teaching:mfe:ia [2011/03/18 11:55] bersini |
teaching:mfe:ia [2011/03/23 08:31] mdorigo [Adaptive collective alignment with a swarm of e-puck robots] |
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| * Contact : [[http://code.ulb.ac.be/iridia.people.php?id=1|Hugues Bersini (IRIDIA)]] | * Contact : [[http://code.ulb.ac.be/iridia.people.php?id=1|Hugues Bersini (IRIDIA)]] | ||
| - | ===== > Développer un programme informatique permettant une analyse statistique en vue de l'évaluation d'un module psychothérapeutique. ===== | + | ===== Développer un programme informatique permettant une analyse statistique en vue de l'évaluation d'un module psychothérapeutique. ===== |
| Ce mémoire se fera en collaboration avec l'équipe médicale du centre pour l'anorexie et la boulimie de l'hôpital Erasme. Il consistera en l'analyse informatisée des données récoltées lors d'entretiens avec le patient et sa famille au cours du traitement. | Ce mémoire se fera en collaboration avec l'équipe médicale du centre pour l'anorexie et la boulimie de l'hôpital Erasme. Il consistera en l'analyse informatisée des données récoltées lors d'entretiens avec le patient et sa famille au cours du traitement. | ||
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| Among the possible generalisations of the problem there is the Vertex Weighted and Edge Weighted Maximum Clique which asks to find the clique of maximum weight. Being generalisations they are also NP-hard. The goal of the project is to devise heuristic algorithms or adapt existing algorithms of the Maximum Clique for weighted version. | Among the possible generalisations of the problem there is the Vertex Weighted and Edge Weighted Maximum Clique which asks to find the clique of maximum weight. Being generalisations they are also NP-hard. The goal of the project is to devise heuristic algorithms or adapt existing algorithms of the Maximum Clique for weighted version. | ||
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| + | Required skills: good knowledge of C or C++ programming. | ||
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| + | * Contacts : | ||
| + | * [[http://iridia.ulb.ac.be/~stuetzle|Thomas Stuetzle (IRIDIA)]] | ||
| + | * [[http://iridia.ulb.ac.be/~fmascia|Franco Mascia (IRIDIA)]] | ||
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| + | ===== Stochastic Local Search heuristics for solving NP-complete puzzles. ====== | ||
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| + | This project is about single player games (puzzles) and the design of algorithms for tackling hard combinatorial optimisation problems. | ||
| + | Example puzzles are: <a href="http://en.wikipedia.org/wiki/Light_Up">Light Up</a>, <a href="http://en.wikipedia.org/wiki/Mastermind_(board_game)">Mastermind</a>, <a href="http://en.wikipedia.org/wiki/Minesweeper_(video_game)">Minesweeper</a>, etc. | ||
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| + | The student will learn how to design and implement a Stochastic Local Search algorithm to solve NP-complete puzzles. The student will also learn how to analyse the performaces of the algorithm and perform statistically sound comparisons with the other algorithms available in literature. | ||
| Required skills: good knowledge of C or C++ programming. | Required skills: good knowledge of C or C++ programming. | ||
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| * Contacts: [[http://iridia.ulb.ac.be/~mbiro|Mauro Birattari]], Marco Dorigo, Arne Brutschy, Giovanni Pini (IRIDIA) | * Contacts: [[http://iridia.ulb.ac.be/~mbiro|Mauro Birattari]], Marco Dorigo, Arne Brutschy, Giovanni Pini (IRIDIA) | ||
| - | ===== Studying collaboration between flying robots and ground-based robots ===== | + | ===== Collaboration between flying robots and ground-based robots ===== |
| - | In previous studies, it has been shown that multiple ground-based robots can autonomously form various patterns by attaching to each other. These robots used simple rule sets and local communication to form pre-defined or random patterns. In this thesis, the student will study how flying robots can collaborate with ground-based robots to select and control the pattern formation process. The student will implement the results of his study and various other algorithms that would facilitate such a collaboration. In order to gain a sound understanding of the matter, the student will first study and benchmark collaboration techniques used in existing robotic systems including flying and ground-based robots. | + | Current research in self-assembling robots mainly focuses on systems composed of identical (i.e., homogeneous) robots. In this thesis, however, we consider a system composed of robots with varying capabilities and different sensors. In particular, we consider a heterogeneous self-assembling system composed of both ground-based robots and flying robots. The ground-based robots can respond to various task contingencies by autonomously connecting to each other and forming collective structures. The flying robots can use their large field of view (from their elevated positions) to assist the ground-based robots in their tasks. |
| - | A possible candidate student must be very motivated, ready to invest extra hours into the thesis, and have a good grasp of C++. The working language is English. | + | In this thesis, the student will focus on the flying robots in the system. The student will explore how the flying robots can i) run internal simulations on possible connections between the ground-based robots to determine the response structure to a task and ii) apply machine learning techniques to let the flying robot use previous, successful experiences to learn about tasks and their possible response structures. The results of the study can be tested on real flying and ground-based robots. |
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| + | Concrete ideas will be developed together with the student. A candidate student must be very motivated, independent, have a good knowledge of machine learning techniques, and have a good grasp of C++. The working language is English. | ||
| * Contacts: [[http://iridia.ulb.ac.be/~mbiro|Mauro Birattari]], Marco Dorigo, Nithin Mathews (IRIDIA) | * Contacts: [[http://iridia.ulb.ac.be/~mbiro|Mauro Birattari]], Marco Dorigo, Nithin Mathews (IRIDIA) | ||
| - | ===== Adaptive collective alignment with a swarm of e-puck robots ===== | + | ===== Recruitment strategies for collective decision making in swarm robotics ===== |
| - | Flocking is a fascinating behavior that birds are able to achieve without a leader or a common frame of reference. Moreover, in some cases, the group goes in the correct direction even if only a small proportion of the group knows the goal direction. This allows birds to avoid a predator even if only a subset of the flock sees it. We want to study one of the most interesting aspects of this mechanism, that is how a group can align collectively to a common direction and change this direction over time according to some stimuli perceived only by a small minority of individuals. | + | Studies of ants and bees have led to different models of collective |
| + | decision making methods in social insects. Swarms of cooperating | ||
| + | robots also have to find consensus decisions and thus face similar | ||
| + | problems as social insects. It is an interesting research question if | ||
| + | the biological models can be applied to create decentralized and | ||
| + | robust decision making methods for swarms of robots. More precisely, | ||
| + | we assume that robots are able to estimate their confidence | ||
| + | about their own decision. Thus, if a group of robots is unsure about a | ||
| + | decision they shall recruit more robots into the decision process to | ||
| + | assure a certain quality in the overall decision. | ||
| - | The goal of this project is to apply a methodology, so far studied only in simulation, to the e-puck robots, in order to tackle the adaptive collective alignment problem. A group of e-pucks has to reach consensus and turn to a common random heading direction, using a common light source as reference point. Furthermore, when an obstacle is perceived by a small minority of the group, consensus should be achieved in order to align to a new direction which allows them to avoid the obstacle. | + | The goal of this master thesis project is to study different |
| - | + | recruitment strategies for decision making in swarms of robots. The | |
| - | Required skills: The candidates should be acquainted with C/C++ programming and have a working knowledge of the English language. | + | following application scenario will be implemented. A group |
| + | of robots need to classify an object in order to operate on it. | ||
| + | Through its sensors the single robots can classify an object with a | ||
| + | certain accuracy. This opinion can then be shared in a group to reach | ||
| + | consensus. If the individual robot's opinions differ strongly from the | ||
| + | one of other | ||
| + | robots or the robots do not have the necessary skills/sensors they | ||
| + | might not be able to reach a final decision. In this case they can | ||
| + | recruit other robots and involve them in the decision making process. | ||
| + | Required skills: the candidates should be acquainted with C/C++ | ||
| + | programming and have a working knowledge of the English language. | ||
| - | * Contact: [[http://iridia.ulb.ac.be/~mbiro|Mauro Birattari]], Marco Dorigo, Eliseo Ferrante, Ali Emre Turgut (IRIDIA) | + | * Contact: [[http://iridia.ulb.ac.be/~mbiro|Mauro Birattari]], Marco Dorigo, Manuele Brambilla, Alexander Scheidler (IRIDIA) |
| ===== Scalable aggregation in swarm robotics without global information or environmental clues ===== | ===== Scalable aggregation in swarm robotics without global information or environmental clues ===== | ||