robotics

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 goal of this article is to present evaluation results for a proposed modification of the Artificial Potential Field Method (APFM). The mathematical model employs Laplace functions to compute repulsive fields to simplify calculations. Additionally, the study introduces a comprehensive evaluation framework using Gazebo and ROS2, designed to test various obstacle avoidance algorithms in simulated environments. Experiments have been conducted in a virtual room containing static obstacles of diverse shapes.

Robotization is one of the crucial directions of modernizing today's industrial production. Robotic systems offer solutions to many different challenges. However, their implementation is constrained by limited accuracy, which is inferior to conventional machine tools. A way to improve industrial robots' accuracy is to calibrate them, i.e., eliminate factors that affect accuracy by refining the mathematical models for software correction of manufacturing and assembly errors, as well as elastic and thermal deformations.

Industrial robots refer to the most complex products of mechanical engineering and electronic equipment in terms of their labor intensity, accuracy, and a class of manufacture as well as quality requirements. Both static and dynamic positioning inaccuracies occur during their operation. Static positioning depends mainly on such parameters as joint axis geometry and angle offset. Non-geometric parameters include compliance (elasticity of joints and bonds), gear form errors (eccentricity and gear errors), gear backlash, and temperature-related expansion.

The main stages of robotics development in the world are analyzed in the work. It has been shown that robotics has become an integral part of various areas of human production and research, the attention to which is constantly growing. Thanks to the development of robotics, new colors have taken over in people’s lives: robots now perform most of the routine work, significantly reduce production time, promote human development and solve a number of unsolved problems. Robots fly into space, help the disabled, the elderly, explore the bowels of the Earth and work in factories and plants.

Synthesis methods of controllers based on the use of frequency characteristics or root hodographs are considered classic or traditional. Frequency methods are available in practical applications, and most control systems are designed based on various modifications to these methods. A distinctive feature of these methods is the so-called robustness, which means that the characteristics of a closed system are insensitive to the minor errors of the model of the real system.

In this paper new approaches for solving dynamic Travelling Salesman Problem (TSP) in conditions of partly unknown input data are given.