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Today, the urgent problem is the autonomous mobile systems navigation in a space where disturbances are possible. The problem is that various disturbances that occur during the robot motion do not allow movement along a pre-planned route and require ongoing re-planning in accordance with the situation received from the sensors. For autonomous systems, the problem is complicated by the need to automatically generate the current situation model based on data from sensors and integrate this situation model with real-time planning and control models. The subject of the research is the knowledge-based models of processing data from sensors used in the autonomous mobile systems navigation. The purpose of the study is the implementation of a rule-based perception cognitive model for the situational control tasks class and re-planning the autonomous robot motion along a route under interference. Investigate the model ability to meet the autonomous systems requirements. Results. Perception data model from sensors is represented by multilevel facts set, in verbal form at generalization different levels, describing the current situation in the robot environment. The knowledge base that the robot uses when navigating is represented by the fuzzy rules five-level hierarchical structure: knowledge about the goals, route and plan for obstacles avoidance, cartographic knowledge about the workspace, strategies and specific control actions necessary to achieve the goal. An algorithm and a program in which the perception model and the modified Takagi-Sugeno model are integrated, which implements situational control with route re-planning have been developed. To study the model, an artificial environment was developed and the results of computer experiments on moving the robot along a given route surrounded by obstacles are presented. Conclusions. The consistency of the perception model implementation for the robot navigation tasks class is shown. The perception model, integrated with the modified Takagi-Sugeno model, solves the situational control problems with route re-planning and satisfies the autonomous systems requirements and has advantages over the program methods and heuristic management according to the criteria of flexibility, scalability and processing of uncertainty.
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