Motor controller of an autonomous mobile robot with neuro-fuzzy control

Currently, an autonomous mobile robot (MR) is an artificial intelligent vehicle that can bypass obstacles and move to a given point along a predetermined route. In the external environment, one of the main problems is the implementation of a mobile robot on wheels, which moves from one point to another with a detour of obstacles. Such robots are equipped with sensors or a camera, so the MR should be able to detect emerging obstacles. This article proposes the principles of constructing an intelligent controller based on ASNL (adaptive system of neuro-fuzzy logic) of an autonomous mobile robot, allowing to achieve the goal along a predetermined route. The mathematical model of MR and the developed controller in the environment of Matlab-Simulink is implemented. Comparison of the proposed controller with a known fuzzy controller is performed according to the following criteria: path length (DT) and curvature parameter (PC). In this article, the simulation results show that the proposed ASNL controller reduces DT and PC compared to a controller with fuzzy logic (NL), therefore it has better performance indicators. A mobile robot using the proposed ASNL controller is capable of moving toward a given target and avoiding random obstacles without collisions in its path.

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