The automatic extraction of knowledge from the world by a robotic system as human beings interpret their environment through their senses is still an unsolved task in Artificial Intelligence. A robotic agent is in contact with the world through its sensors and other electronic components which obtain and process mainly numerical information. Sonar, infrared and laser sensors obtain distance information. Webcams obtain digital images that are represented internally as matrices of red, blue and green (RGB) colour coordinate values. All this numerical values obtained from the environment need a later interpretation in order to provide the knowledge required by the robotic agent in order to carry out a task. Similarly, light wavelengths with specific amplitude are captured by cone cells of human eyes obtaining also stimulus without meaning. However, the information that human beings can describe and remember from what they see is expressed using words, that is qualitatively. The exact process carried out after our eyes perceive light wavelengths and our brain interpret them is quite unknown. However, a real fact in human cognition is that people go beyond the purely perceptual experience to classify things as members of categories and attach linguistic labels to them. As the information provided by all the electronic components incorporated in a robotic agent is numerical, the approaches that first appeared in the literature giving an interpretation of this information followed a mathematical trend. In this thesis, this problem is addressed from the other side, its main aim is to process these numerical data in order to obtain qualitative information as human beings can do. The research work done in this thesis tries to narrow the gap between the acquisition of low level information by robot sensors and the need of obtaining high level or qualitative information for enhancing human-machine communication and for applying logical reasoning processes based on concepts. Moreover, qualitative concepts can be added a ...
Qualitative Distances and Qualitative Description of Images for Indoor Scene Description and Recognition in Robotics
2012-01-12
TDX (Tesis Doctorals en Xarxa)
Theses
Electronic Resource
English
DDC: | 629 |
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