Advanced vision and control platform for learning and development, offering target tracking, faceactivation, smart carrier modes, and group control for multidevice coordination. Key benefits Ready for practical projects: Built for realtime vision tasks such as target tracking, visual picking, and faceactivated control that solve common prototyping challenges. Fast development with Python: Python support and opensource code reduce setup time and let you behavior quickly for research, classroom exercises, or product prototypes. Low learning barrier: Two control methods accommodate beginners and advanced users, while abundant tutorials guide stepbystep development and troubleshooting. Scalable for secondary development: Designed for sensor integration, algorithm testing, and visual picking workflows so you can expand capabilities as project needs grow. Core features and attributes Function: Target tracking, faceactivation, smart carrier, group control, and visual picking support for robotics and automation tasks. Compatibility: performance singleboard computer compatible with Raspberry Pi 4B and full Python development support for easy software integration. Performance: Sufficient processing power for responsive vision and control tasks, enabling smooth tracking and coordinated group behavior. Software and learning resources: Includes opensource code and extensive tutorials to accelerate learning, , and deployment. Practical use scenarios Educational labs and STEM courses: Teach computer vision, control systems, and Python programming with handson exercises using face activation and target tracking modules. Prototype automation solutions: Implement visual picking and smart carrier functions for pickandplace experiments or light warehouse automation prototypes. Multirobot coordination trials: Test group control strategies and swarm behaviors for research or advanced project demonstrations. Why this platform Reduces development friction with readytouse vision functions and extensive documentation. Offers flexible expansion paths for sensor applications and custom algorithms. Balances accessibility and capability so both learners and experienced developers can use the same hardware and codebase. Supported for custom development with opensource resources and Python, this platform is suited for classroom instruction, prototype development, and experimental automation projects.