робототехніка

The article examines current approaches to training robotic manipulators for executing complex tasks in dynamic and changing environments. It provides a comparative analysis of modern training methods, highlighting their advantages and disadvantages. Additionally, the paper outlines the typical areas in which these methods are applied. Particular attention is given to approaches that involve human instructors, self-learning, and reinforcement learning.

Obstacle avoidance is a fundamental capability for autonomous mobile robots, ensuring safe navigation in dynamic and unstructured environments. This paper presents a novel approach to real-time obstacle avoidance based on an Artificial Potential Field Method (APFM) utilizing a hyperbolic secant function. A mathematical formulation of the proposed model is developed and analyzed. To validate the approach, a simulation framework was implemented using ROS 2, the Gazebo simulator, and the TurtleBot3 Burger platform.

This article presents a comprehensive study of a control model for legged robotic platforms, particularly hexapods, based on the application of deep reinforcement learning techniques. The relevance of employing artificial neural networks to form adaptive robot behavior in undefined conditions is substantiated, enabling greater flexibility and robustness in dynamic environments.

The article focuses on the main problems of the development of the soft (perceptual) metrology as one of the new progressive areas of modern metrology that has appeared as a result of the dissemination of the information measuring technologies into the field of knowledge of non-physical phenomena and processes. An overview and analysis of well-known works on the topic of soft metrology are conducted.