Aprendizaje cooperativo de conceptos para sistemas Multi-Robot
Autonomous learning of objects using visual information is important to robotics as it can be used for local and global localization problems, and for service tasks such as searching for objects in unknown places. In a robot team, the learning process can be distributed among robots to reduce traini...
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| Format: | master thesis |
| Status: | Versión aceptada para publicación |
| Publication Date: | 2009 |
| Country: | México |
| Institution: | Instituto Nacional de Astrofísica, Óptica y Electrónica |
| Repository: | Repositorio Institucional del INAOE |
| Language: | Spanish |
| OAI Identifier: | oai:inaoe.repositorioinstitucional.mx:1009/429 |
| Online Access: | http://inaoe.repositorioinstitucional.mx/jspui/handle/1009/429 |
| Access Level: | Open access |
| Keyword: | info:eu-repo/classification/Sistemas Multi-robots/Multi-robot systems info:eu-repo/classification/Sistemas cooperativos/Cooperative systems info:eu-repo/classification/Sistemas de aprendizaje/Learning systems info:eu-repo/classification/cti/1 info:eu-repo/classification/cti/12 info:eu-repo/classification/cti/1203 |
| Summary: | Autonomous learning of objects using visual information is important to robotics as it can be used for local and global localization problems, and for service tasks such as searching for objects in unknown places. In a robot team, the learning process can be distributed among robots to reduce training time and produce more accurate models. This work introduces a new learning framework where individual representations of objects are learned on-line and off-line by a group of robots while traversing an environment without prior knowledge on the number or nature of the objects to learn. Individual concepts are shared among robots to improve their own concepts, combining information from other robots that saw the same object, and to acquire a new representation of an object not seen by the robot. Since the robots do not know in advance how many objects they will encounter, they need to decide whether they are seeing a new object or a known object, i.e., a previously learned object or an object learned by another robot. Objects are characterized by local and global features. Then a Bayesian approach that combines both, global and local features, is applied by robots to recognize objects. We empirically evaluated our approach with a real world robot team with very promising results. |
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