Real-time cognitive algorithm for adapting the actions of multipurpose robotic systems

V. Voronin; I. Khamidullin; E. Semenishchev; M. Zhdanova; N. Gapon; M. Kazaryan

doi:10.1117/12.3038686

22 November 2024 Real-time cognitive algorithm for adapting the actions of multipurpose robotic systems

V. Voronin, I. Khamidullin, E. Semenishchev, M. Zhdanova, N. Gapon, M. Kazaryan

Proceedings Volume 13239, Optoelectronic Imaging and Multimedia Technology XI; 132391Z (2024) https://doi.org/10.1117/12.3038686
Event: SPIE/COS Photonics Asia, 2024, Nantong, Jiangsu, China

Conference Poster

Abstract

Industry 4.0 marks a shift toward fully automated digital production, where intelligent systems manage processes in realtime and interact continuously with their environment. Central to this evolution is robotic technology, which enhances productivity and precision in manufacturing. A key aspect of this advanced production model is human-robot interaction, where operators and robots work together on complex tasks. Ensuring safe collaboration between humans and robots is a primary objective. This paper proposes a method for human gesture recognition based on multi-sensor data fusion. By incorporating data from multiple sensors, we achieve a more complete and robust representation of gestures. Our approach involves an algorithm that classifies human movements in real-time using visual data. The process consists of several steps: data preprocessing, feature extraction, data integration, and gesture classification. By employing machine learning and deep learning techniques for feature extraction and analysis, we aim to achieve high accuracy in recognizing gestures.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

V. Voronin, I. Khamidullin, E. Semenishchev, M. Zhdanova, N. Gapon, and M. Kazaryan "Real-time cognitive algorithm for adapting the actions of multipurpose robotic systems", Proc. SPIE 13239, Optoelectronic Imaging and Multimedia Technology XI, 132391Z (22 November 2024); https://doi.org/10.1117/12.3038686

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