robotics

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.