Support systems for robotics: principles, algorithms and development prospects

Abstract

The article explores the current state and prospects for the development of decision support systems in robotics. It examines the fundamental principles of building such systems, in particular hierarchy, information integration, adaptability, and others. The main algorithmic approaches are analyzed, including Q-learning, neural networks, Markov processes, genetic algorithms, fuzzy logic, and Bayesian networks. Mathematical models and examples of practical applications of each algorithm are provided. A comparative analysis of the effectiveness of various algorithmic solutions is conducted based on the criteria of adaptability, speed, and implementation complexity. The advantages and limitations of each approach are identified in the context of specific robotics tasks. Prospective directions for the development of DSS are outlined, focusing on integration with modern deep learning technologies, cloud computing, and quantum systems. The research has theoretical and practical value for the development and improvement of autonomous robotic systems in various fields of application, from industry to medicine. The results of the work can be used in the design of new and modernization of existing decision support systems in robotics.