Published September 24, 2024
| Version v1
Journal article
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Human Recognition in a Collaborative Robot-Manipulator Working Area Based on MobileNetV2 Deep Neural Network in Real Time
Creators
- 1. Department of Computer-Integrated Technologies, Automation and Robotics, Kharkiv National University of Radio Electronics, Ukraine
- 2. Senior Developer Electronic Health Solution, Amman, Jordan
Description
The article deals with the development of a human recognition system in a collaborative robot-manipulator working area based on MobileNetV2 deep neural network. The purpose of the research is to implement an accurate and fast real-time recognition algorithm to improve security and work efficiency. Using the MobileNetV2 model allows you to achieve high accuracy with minimal resource consumption. The results of the experiments demonstrate the high reliability of the system in conditions of changing lighting and moving obstacles, which opens up new opportunities for the integration of recognition in industrial collaborative robot.
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86-105 Svitlana Maksymova .pdf
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Additional details
References
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- 2. Yevsieiev, V., & et al. (2024). Object Recognition and Tracking Method in the Mobile Robot's Workspace in Real Time. Technical science research in Uzbekistan, 2(2), 115-124.
- 3. Samoilenko, H., & et al. (2024). Review for Collective Problem-Solving by a Group of Robots. Journal of Universal Science Research, 2(6), 7-16.
- 4. Bortnikova, V., & et al. (2019). Structural parameters influence on a soft robotic manipulator finger bend angle simulation. In 2019 IEEE 15th International Conference on the Experience of Designing and Application of CAD Systems (CADSM), IEEE
- 5. Gurin, D., & et al. (2024). Using Convolutional Neural Networks to Analyze and Detect Key Points of Objects in Image. Multidisciplinary Journal of Science and Technology, 4(9), 5-15.