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teaching:mfe:ia [2010/03/19 17:01] mbiro |
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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) | ||
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| + | ===== Studying collaboration between flying robots and ground-based robots ===== | ||
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| + | 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. | ||
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| + | 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. | ||
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| + | * Contacts: [[http://iridia.ulb.ac.be/~mbiro|Mauro Birattari]], Marco Dorigo, Nithin Mathews (IRIDIA) | ||
| ===== Adaptive collective alignment with a swarm of e-puck robots ===== | ===== Adaptive collective alignment with a swarm of e-puck robots ===== | ||
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| * 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, Eliseo Ferrante, Ali Emre Turgut (IRIDIA) | ||
| - | ===== A comparison of decision making strategies for adaptive foraging in swarm robotics ===== | + | ===== A comparison of decision-making strategies for adaptive foraging in swarm robotics ===== |
| Group of social insects are able to efficiently find the (shortest) path to the a food source and even to differentiate between the quality of two food sources. Studies with ants showed that this mechanism is driven by the perception of stimuli from chemical substances like pheromone. Moreover ants are able to collectively modify their choices if there are changes in the environment, that is, if a source becomes better than another. These ideas have been a source of inspiration for several algorithms in swarm robotics which solves a similar problem (retrieval of objects) by using different types of stimuli such as the encounter rate of objects. | Group of social insects are able to efficiently find the (shortest) path to the a food source and even to differentiate between the quality of two food sources. Studies with ants showed that this mechanism is driven by the perception of stimuli from chemical substances like pheromone. Moreover ants are able to collectively modify their choices if there are changes in the environment, that is, if a source becomes better than another. These ideas have been a source of inspiration for several algorithms in swarm robotics which solves a similar problem (retrieval of objects) by using different types of stimuli such as the encounter rate of objects. | ||