Abstract
The article proposes to implement mobile robots control systems using neuromorphic classifiers, in which biosimi-lar neurons are built on an original neuro-fuzzy model, trained to display the function of the Izhikevich neuron. Us-ing this approach, neuromorphic classifiers of spatial and spatiotemporal patterns were developed. Such a classi-fiers were used in non-contact supervisory control system for mobile robot based on static and dynamic states of the environment. The experiments performed demonstrated the effectiveness of using the neuromorphic classifi-ers, both in a neural interface when recognizing imaginary supervisory commands of the user, and when solving problems of robot navigation in environments with static and dynamic obstacles. At comparative analysis of navi-gation systems implemented using the neuromorphic classifiers, modular fuzzy logic and the adaptive neuro-fuzzy system ANFIS it was shown that the system with the neuromorphic classifiers give the highest average speed of the robot, as well as the shortest travel time. An experiment with the use of the neuromorphic classifiers in the con-trol system based on dynamic states of the environment showed that robot successfully avoids collisions with per-son.
Published Version
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