The dissertation was defended at the public meeting of the Dissertation Defence Council of the Scientific Field of Mechanical Engineering in the SRA-I Meeting Hall of Vilnius Gediminas Technical University at 2 p.m. on 3 June 2024.
The dissertation deals with the problem of robot position errors. The main object of research is the application of machine learning methods to determine the kinematic parameters of a robot (or robotic system) and to create an error compensation method. The main goal of the dissertation is to create a trajectory accuracy assessment system for a robotic system using inertial measurement unit sensors, with the help of machine learning methods, to determine the optimal compensatory values of the system’s kinematic parameters and to apply that system to the robot for trajectory accuracy evaluation. The dissertation addresses three main tasks: machine learning algorithms are used to create and propose a method to compensate for the kinematic errors of the robot, and to evaluate the effectiveness of the created machine learning process with a specific position and load of the robot. The dissertation consists of an introduction, three chapters, general conclusions, a list of used literature and a list of the author’s publications on the subject of the dissertation. The introductory chapter discusses the research problem and the relevance of the dissertation, describes the research object, formulates the purpose and tasks of the research, describes the research methodology, the scientific novelty of the dissertation, the practical significance of the dissertation results, and the defended statements. At the end of the introduction, the author’s publications and conference presentations on the subject of the dissertation are presented, as well as the structure of the dissertation. The first chapter focuses on literature analysis. It provides an overview of machine learning types and algorithms, an analysis of robot errors and the use of inertial sensors in robotics in the scientific literature. The chapter closes with conclusions and clarifies dissertation tasks. The second chapter presents the methodology and analysis of robot positioning errors, as well as the methodology of compensating identified errors using machine learning. The third chapter provides the results of experimental studies, which allow for the evaluation of the effectiveness of the proposed machine-learning algorithm to compensate for robot errors. Eight scientific articles were published on the subject of the dissertation: two in scientific journals included in the list of Clarivate Analysis Web of Science database with citation index; one in conference materials, referenced in Clarivate Analytics Web of Science database, and five in journals of other international databases. The research results were publicised at three international scientific conferences in Lithuania and abroad.
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