UDC 004.032.26
DOI: 10.36871/2618-9976.2023.11.006
Authors
Arseniy V. Frolov,
Student of the Department of Information Systems and Technologies, Yaroslavl State Technical University,
Yaroslavl, Russia
Alexander V. Kungurov,
Student of the Department of Information Systems and Technologies, Yaroslavl State Technical University,
Yaroslavl, Russia
Elena I. Voevodina,
Senior Lecturer at the Department of Information Systems and Technologies of Yaroslavl State Technical
University, Yaroslavl, Russia
Vladimir A. Kvasha,
Director, Yaroslavl Branch of the Financial University under the Government of the Russian Federation,
Yaroslavl, Russia
Valery D. Larionov,
Student in the field of study "Economics", Yaroslavl branch of the Financial University under
the Government of Russia, Yaroslavl, Russia
Abstract
Autonomous navigation of unmanned vehicles is a rapidly evolving field, and the application of intelligent systems plays a pivotal role within it. In this scientific paper, we introduce a unique approach based on fuzzyneural networks, with a focus on ensuring safety and enhancing the efficiency of autonomous navigation. The developed fuzzy inference system provides optimal control of vehicle movement, strictly adhering to traffic regulations, and actively avoiding collisions with diverse obstacles. Within our methods, we establish fuzzy "ifthen" rules, and the system learns from essential input data such as the current position of the autonomous vehicle, its velocity, and information about nearby objects.
Keywords
Unmanned vehicle, ANFIS, Fuzzy system, Neural network, Autonomous navigation, Safety, Efficiency, Obstacles, Traffic regulations
