RESEARCH OF METHODS AND MEANS OF AUTOMATIC LOCATION OF A MOBILE DEVICE IN SPACE IN CONDITIONS OF PARTIAL OR FULL AUTONOMY - KORPAN, YAROSLAV; NECHYPORENKO, OLGA; IVCHENKO, PETRO (Herald of Khmelnytskyi National University. Technical sciences, 2025)
The rapid development of robotics, in particular mobile robots, raises a wide range of problems regarding their localization and navigation. The paper presents the results of a study of methods and means used to localize a mobile device in space. Today, mobile devices (robotic systems, drones) operate in various areas of human activity under direct control, semi automatically or in an autonomous mode. Many localization and mapping (SLAM) methods and algorithms have been developed for their productive operation. Many of the developed methods have been successfully implemented, but they were usually developed to work in specific environments and under certain operating conditions. Therefore, a specific SLAM algorithm is selected for different conditions or, if necessary, developed/improved. This article proposes an updated and supplemented classification of SLAM algorithms. The paper classifies and summarizes some of the SLAM algorithms that have been proposed by scientists around the world and described in their publications.   A model of the localization process implementation of a mobile object in space is presented in the form of conditional blocks, which allows their compilation depending on the degree of importance and feasibility of use, and can also be used to create new SLAM algorithms. That is, the presented model allows you to determine parts of the methods of localization of an object in space, which can be replaced partially or completely, depending on the current operating conditions. Also, it is possible to switch between SLAM algorithms, which allows you to use the most optimal algorithm for specific operating conditions. For example, switching between algorithms for localization in a closed space to an algorithm for working in an open space. Switching can be both one-time and repeated.  The approaches to implementing the SLAM algorithm specified in the classification allow, if necessary, to use them in combination or separately for mapping, localization, navigation and augmented reality. Thanks to the combination of SLAM implementation approaches, mobile devices can use the most effective implementation of the localization process in their work that suits the current situation.
Keywords: mobile devices; SLAM classification