Механизмы интеллектуальных обратных связей, обработки знаний и самообучения в системах управления автономными роботами и мультиагентными робототехническими группировками

Abstract

The article is devoted to analysis of various approaches aimed to ensure adaptive properties of the automatic control systems designed for maintenance of the demanded reliability and functioning quality in the presence of disturbances and uncertainty factors. A review is provided of the development of the creation principles for the automatic control systems - from organization of an internal feedback, up to creation of intelligent feedback contours with self-training mechanisms. Examples of various robots equipped with intelligent self-learning control systems are presented. A list of tasks of self-learning of the intelligent control systems is presented both for the autonomous robots and for the multi-agent robot systems. A generalized structure of a control system of the autonomous mobile robots is offered based on the intelligent feedback as means of new knowledge formation. Knowledge formation is possible in a self-learning mode on the basis of processing of the accumulated sensory information. The main problems with creation of the intelligent self-learning control systems are discussed. It is shown that one of the most perspective approaches to realization of the self-learning process in the intelligent control systems of the autonomous robots and multi-robot systems is related to the method of classification of tree formation. The article gives an example of a practical solution to the self-learning problem of the autonomous mobile robots. The solution is based on the automatic knowledge formation of the passability characteristics of the surface underlying a robot. The results of a complex modeling are presented, which testify to the expediency and efficiency of incorporation of the self-learning methods in the structure of a mobile robot intelligent onboard control system for improvement of its adaptation properties. The prospects of the self-learning processes are described for the autonomous robots operating in the structure of the multi-agent systems. As a prove experimental estimates of the efficiency are given for application of the self-learning methods aimed at adaptation of the autonomous mobile robots under conditions of environmental uncertainty on the basis of a mutual exchange of the terrain passability knowledge.