Original Article
The integration of Artificial Intelligence (AI) and the Internet of Things (IoT) is reshaping robotics into intelligent, adaptive, and human–interactive systems. In this work, we, as part of the Research and Development team at Myra Academy, present an IoT and AI-vision enabled gesture recognition framework for intelligent robot navigation. Dedicated to advancing STEAM education, Myra Academy has developed this system as a superior alternative to traditional methods such as Standard Firmata, which connects Python environments to embedded systems via serial communication, and Bluetooth-based approaches for AI–embedded interfaces. These older techniques are limited in range, speed, and reliability, whereas our design exploits IoT-enabled Wi-Fi connectivity powered by the Xtensa LX6/LX7 processor architecture, offering robust long-range communication and improved accuracy through AI-vision strategies. The proposed framework employs computer vision techniques with OpenCV and Mediapipe to detect and track hand landmarks, while an AI-driven decision module interprets gestures to control the robotic platform in real time. Experimental evaluation confirms low-latency transmission, reliable gesture detection under varying lighting conditions, and smooth navigation performance. Beyond its technical capabilities, this innovation serves as a powerful educational tool, enabling students to explore IoT and AI-based robot navigation with enhanced connectivity and intuitive interaction. By bridging advanced AI-vision techniques with IoT frameworks, the system has the potential to redefine STEAM education, strengthening AI and robotics curriculum while promoting innovation in smart environments and assistive robotics.
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