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Using LSTM Recurrent Neural Networks to Predict the Trajectory of Human Hand Movement in the Working Area of a Collaborative Robot-Manipulator

Svitlana Maksymova (Department of Computer-Integrated Technologies, Automation and Robotics, Kharkiv National University of Radio Electronics, Ukraine) · Ahmad Alkhalaileh (Senior Developer Electronic Health Solution, Amman, Jordan) · Dmytro Gurin (Department of Computer-Integrated Technologies, Automation and Robotics, Kharkiv National University of Radio Electronics, Ukraine) · Vladyslav Yevsieiev (Department of Computer-Integrated Technologies, Automation and Robotics, Kharkiv National University of Radio Electronics, Ukraine)

Журнал универсальных научных исследований · 17-32 · 2024

Аннотация

The article examines the use of LSTM recurrent neural networks for predicting the trajectory of human hand movement in the working area of a collaborative robot-manipulator. The results demonstrate high prediction accuracy for slow movements, but reveal certain limitations for fast and complex trajectories. The proposed approach is aimed at improving the safety and efficiency of the joint work of humans and robots within the framework of the concept of Industry 5.0.

Доказательный фрагмент

«The article examines the use of LSTM recurrent neural networks for predicting the trajectory of human hand movement in the working area of a collaborative robot-manipulator.»

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Демо-превью

The article examines the use of LSTM recurrent neural networks for predicting the trajectory of human hand movement in the working area of a collaborative robot-manipulator. Полный текст публикации CiteX не хранит — доступ к первоисточнику открывается по DOI согласно лицензии издателя и выбранному плану.

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Журнал
Страна
Узбекистан
Ключевые слова
examines, lstm, recurrent, neural, networks, predicting, trajectory
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