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Features of Using Data Fusion With Extended Kalman Filter in Industry 5.0 Concepts
(«UKRLOGOS Group», 2025) Nevliudov, I. Sh.; Yevsieiev, V.; Elgun Jabrayilzade
The relevance of studying the features of using Data Fusion technologies in combination with Extended Kalman Filter (EKF) in the Industry 5.0 concept is determined by the need to ensure high accuracy, reliability, and adaptability of collaborative robotic systems that interact with humans and operate in a dynamic environment. Industry 5.0 involves the deep integration of artificial intelligence, sensor networks, and cognitive control algorithms, enabling the creation of robotic complexes with increased autonomy and safety. In conditions of multi-source data acquisition from machine vision cameras, inertial measurement units, ultrasonic and tactile sensors, there is a need for methods capable of effectively combining information, minimizing measurement errors and compensating for noise. Data fusion in the context of a collaborative manipulation robot is the process of integrating data from heterogeneous sensors to obtain a single, more accurate, and informative assessment of the system state than is possible using separate information sources