Authors V.A. Barhotkin, V.F. Petrov, M.P. Kochetkov
Month, Year 01, 2015 @en
Index UDC 004.93.1
Abstract To increase the effectiveness of promising mobile robots need to increase their degree of autonomy. Required to solve the problem of the rapid identification of the environment to ensure sustainable operation of the mobile robot in an emergency. Recognition methods implemented in the existing vision systems are effective only for certain quite simple objects. Examples are polyhedra, human faces, printed or hand-written characters, numbers of vehicles. These methods can be applied when the objects observed in a sufficiently determined conditions (certain lighting, back-ground and position of the object relative to the television camera) The above conditions are not met, when the robot moves on unprepared terrain. In this regard, the development of new methods and information processing algorithms for computer vision systems of mobile robots is an important scientific and technical challenge. Complexity of the tasks involves the construction of learners recognition systems. Learning is one of the most important problems in the theory of computational intelligence and is an integral part of a recognition process in conditions of high uncertainty of the external environment. Going beyond statistical models leads to the formation of a more general approach to the recognition of images. This approach is based on the theory of fuzzy sets and fuzzy logic, which corresponds to the logic of the human mind, which operates with fuzzy characteristic values and fuzzy inference rules. Initial information to create models of fuzzy classification can be extracted from the training set. The paper presents a method for determining the membership functions of linguistic variable terms on the training set. The implementation of the functions of a fuzzy inference by precedents is considered. The approach to building a knowledge base for object recognition based on This approach simplifies the formalization of the work of an experienced operator, which solves the problem of recognition. Resulting formalization can be converted into the knowledge base and used in the future in the control system of the robot without human intervention.

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Keywords Feature; training set; fuzzy logic; pattern recognition.
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