CONTROL MODELS FOR MOBILE ROBOT PARKING USING DISTANCE SENSOR DATA

Abstract

Relevance. The growing demand for autonomous mobile systems capable of independent navigation and parking is driven by several critical factors. Firstly, the rapid robotization in logistics, security, delivery, and service industries necessitates reliable mechanisms for precise positioning of mobile platforms in spatially constrained environments. Secondly, in the context of autonomous vehicle development, the issue of automatic parking becomes a priority for enhancing safety, reducing energy consumption, and minimizing human involvement in control processes. Currently, a significant number of studies focus on the implementation of automatic parking systems; however, most of them either rely on high-cost sensors (such as LiDARs or deeplearning-based cameras) or fail to ensure the required accuracy under dynamic or unfamiliar environmental conditions. Against this backdrop, the use of ultrasonic sensors represents an effective alternative, enabling a necessary level of adaptability and sensitivity while maintaining low system cost. The relevance of this research is further reinforced by the need to develop a universal control model that is scalable, adaptive, and easily integrable into various types of mobile platforms. This work focuses not only on the theoretical formulation of the control model but also on its experimental validation using data from ultrasonic sensors that reflect the physical environment in real time. Therefore, the development of a mobile robot parking control model based on ultrasonic sensors is a timely and important task that combines scientific novelty with practical significance for the advancement of autonomous systems. The object of research. A parking control system for a mobile robot that operates based on data obtained from ultrasonic distance sensors. This system comprises both hardware components, such as ultrasonic sensors, actuators, and controllers, and software that implements algorithms for environmental analysis, decision-making related to parking maneuvers, and motion control. Purpose of the article. This article presents a comprehensive review of contemporary models for mobile robot parking control based on distance sensor data. The objective is to identify and critically evaluate effective approaches to sensor integration, control algorithm design, and architectural implementation of such systems. Particular attention is given to analyzing their applicability in real-world environments, with the aim of outlining development prospects that enhance system accuracy, reliability, and adaptability under dynamic and constrained conditions. Research results. As a result of the conducted review, it has been established that modern mobile robot parking control systems encompass a wide range of modeling approaches, varying in both mathematical complexity and sensor configurations. The analysis reveals that the choice of control model is directly influenced by the availability of computational resources, the robot’s chassis type, and the nature of the operational environment. Particular attention is given to the comparative assessment of sensors, with ultrasonic sensors remaining dominant in short-range positioning systems due to their low cost, ease of integration, and reliability in controlled conditions. Conversely, LiDAR sensors have demonstrated superior accuracy and spatial resolution, although they present higher implementation and maintenance complexity. Cameras and infrared sensors are regarded as supplementary data sources, functioning effectively only within well-defined conditions and with appropriate software support. The findings of the review confirm that an effective parking control system for mobile robots relies on a holistic approach that integrates sensor selection, control model design, algorithmic implementation, and system architecture. Such integration enables high accuracy and operational reliability even in complex, dynamic, or constrained environments. Conclusions. Effective mobile robot parking control is based on the integration of reliable sensor systems, particularly ultrasonic sensors and adaptive decision-making algorithms. Ultrasonic sensors remain the most suitable option for low-cost and simple systems, whereas hybrid approaches involving LiDAR or camera-based solutions offer higher precision. Among the control algorithms, finite state machines, fuzzy logic, and machine learning methods have demonstrated the greatest effectiveness. The most optimal system architecture is modular, with a clear separation between sensing, computation, and actuation layers, which ensures adaptability, accuracy, and operational stability under real-world conditions.