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Variational Genetic Programming for Optimal Control System Synthesis of Mobile RobotsThe paper focuses on the problem of autonomous control system synthesis for the mobile robot

Variational Analytic Programming for Synthesis of Optimal Control for Flying Robot on the quality of the chosen basic solution. An example of automatic control system synthesis for flying robot

Modeling tracks and controller for servosila engineer robot in robotics. Models let our opportunity to make mistakes that could break real robot and learn

Synthesised Optimal Control for a Robotic Group by Complete Binary Genetic Programming*The paper continues the study of symbolic regression methods for control learning. The optimal

Solving the Control Synthesis Problem Through Supervised Machine Learning of Symbolic Regression the control synthesis problem for a mobile robot using a supervised machine learning method. A relatively new

Collision avoidance system synthesis for a group of robots in unsupervised learning paradigm [Синтез системы разрешения коллизий для группы роботов в парадигме обучения без учителя] to robots. The control system which is responsible for collision avoidance is only activated when

Исследование методов машинного обучения на основе генетического программирования и символьной регрессии для управления мобильными роботамиIn recent decades, machine learning methods have become a key tool in the development and control

Using symbolic regression techniques for machine learning to control robot movement: Advantages and cons into the application of symbolic regression in the domain of robot movement control, investigating both its advantages

Employing of RL Technology to Develop an Adaptive Motion Controller for a Line Follower Robot follower robot. The controller learning process took place on the basis of the digital twin of the mobile

SELF-LEARNING OF AUTONOMOUS INTELLIGENT ROBOTS IN THE PROCESS OF SEARCH AND EXPLORE ACTIVITIES; [САМООБУЧЕНИЕ АВТОНОМНЫХ ИНТЕЛЛЕКТУАЛЬНЫХ РОБОТОВ В ПРОЦЕССЕ ПОИСКОВО-ИССЛЕДОВАТЕЛЬСКОЙ ДЕЯТЕЛЬНОСТИ∗]-seeking behavior of robots. A self-learning algorithm has been developed for the conditions with a high level

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