1 Department of Mechanical Engineering, DRR Government polytechnic, Davangere-577004, Karnataka, India.
2 Department of Computer Science Engineering, DRR Government polytechnic, Davangere-577004, Karnataka, India.
3 Department of Science, DRR Government polytechnic, Davangere-577004, Karnataka, India.
Research Article
World Journal of Advanced Research and Reviews, 2023, 20(02), 1498-1506
Received on 02 November 2023; revised on 26 November 2023; accepted on 30 November 2023
The integration of artificial intelligence (AI) with vision-based gesture control systems has significantly transformed human-robot interaction in industrial environments. This paper explores recent advancements in AI-driven gesture recognition for industrial robots, focusing on its role in improving human-robot collaboration, efficiency, and safety. AI-powered gesture control enables intuitive and contactless operation, reducing the need for physical controllers and enhancing ergonomics in industrial workflows. The study examines key components of AI-driven gesture recognition, including deep learning models, convolutional neural networks (CNNs), and recurrent neural networks (RNNs) for real-time gesture classification. Additionally, various sensor technologies such as RGB cameras, depth sensors, and LiDAR are analyzed for their effectiveness in detecting and interpreting human gestures with high precision. Real-time data processing techniques, including edge computing and cloud-based AI inference, are discussed to highlight their impact on reducing latency and improving system responsiveness. Despite its potential, AI-based gesture control systems face challenges related to accuracy, adaptability, and security. Variability in gesture execution, environmental conditions, and user differences can affect recognition accuracy. Adaptability concerns arise when deploying these systems across diverse industrial applications, requiring robust training datasets and adaptive learning models. Furthermore, security risks such as unauthorized access and potential cyber threats necessitate strong encryption and authentication measures. To validate the effectiveness of AI-driven gesture control, experimental results are presented, supported by figures, tables, and bar charts. These results demonstrate improvements in operational efficiency, accuracy, and safety compared to conventional control methods such as manual operation and joystick-based interfaces. The findings highlight the transformative potential of AI-powered gesture recognition in industrial robotics and provide insights into future research directions for optimizing human-robot collaboration.
AI-driven gesture recognition; human-robot collaboration (HRC); deep learning, machine learning; computer vision; convolutional neural networks (CNNs); recurrent neural networks (RNNs); transformer models; industrial automation.
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Anupama A, Ramya S Yamikar and Renuka K M. AI-driven gesture control for industrial robots: A vision-based approach for enhancing human-robot collaboration. World Journal of Advanced Research and Reviews, 2023, 20(2), 1498-1506. Article DOI: https://doi.org/10.30574/wjarr.2023.20.2.2254