AI smart light robot merges Jetson Nano GPU processing, ROS 2 coordination, and Arduino sensor control to analyze illumination and color patterns in real time. OpenCV and TensorFlow execute light tracking, color segmentation, and brightness mapping to simulate visual perception in robotics. ROS 2 DDS middleware links LiDAR, IMU, and light sensors to form an adaptive AI control system.
Wi-Fi and Bluetooth AIoT interfaces provide seamless communication with multiple devices for remote control and real-time monitoring. Students learn how AI systems process optical data to perform motion decisions, environmental adjustments, and interactive lighting tasks. MoveIt integration supports path optimization and gesture-based response through dynamic vision input.
AI light recognition robot teaches students how AI interprets light, color, and shadows as decision variables. The open-source platform allows custom Python and C++ programming for experiments in AI vision, edge computing, and IoT-based illumination. Aluminum body ensures durability, stability, and heat balance during continuous research operation. This educational AI kit connects vision, electronics, and programming in a single comprehensive learning experience.
- AI Vision and Light Recognition System AI vision integrates OpenCV and TensorFlow to detect light intensity, direction, and color in real time. Powered by the Jetson Nano GPU, it performs fast image analysis for autonomous navigation and visual mapping. Students can explore color-based control, visual AI tracking, and light-driven robotics, gaining foundational skills in computer vision and machine learning.
- ROS 2 and Arduino Integration for Sensor Coordination The system features ROS 2 communication between the main AI controller and Arduino-based sensors, coordinating light, LiDAR, and IMU data in real time. Learners develop ROS topics, nodes, and publishers to create dynamic feedback loops. Through this integration, students understand modular sensor fusion and the workflow of AI-enabled robotic perception systems.
- Deep Learning for Color and Brightness Classification Using CNN deep learning models, the Jetson Nano analyzes and classifies color temperature and brightness zones. Students train their own AI to distinguish warm, cool, and neutral light conditions, applying data-driven logic in experiments. This module strengthens understanding of AI classification, dataset labeling, and real-world sensor calibration in robotics.
- Wi-Fi and Bluetooth AIoT Connectivity The robot connects via Wi-Fi to cloud dashboards for light-mapping visualization and experiment data logging, while Bluetooth enables direct control and calibration. With AIoT communication, students can build distributed learning networks and access remote data monitoring, fostering skills in IoT integration, wireless communication, and AI data synchronization.
- Open-Source STEM Education Platform As an open-source STEM learning kit, this platform encourages exploration in AI, optics, IoT, and robotics. It supports both Python and C++ programming for AI vision and illumination projects, empowering students to engage in hands-on experimentation, algorithm tuning, and real-world sensor control. Perfect for classrooms, maker labs, and research environments focused on AI-powered
تحویل بین 29 آبان الی 08 آذر
گارانتی اصالت و سلامت فیزیکی کالا
ارسال رایگان به سراسر کشور
