Note: Just superior version supports AI large models and AI voice interaction.
RASPBOT-V2 is an AI large-model robot car with a metal body bracket. It is equipped with Mecanum wheels to achieve 360° omnidirectional movement. Raspberry Pi 5 serves as the main control, with Python as the programming language. A 1MP USB camera and 2DOF PTZ, combined with the OpenCV image processing library and MediaPipe machine learning framework, enable features such as color recognition, target tracking, license plate recognition, visual tracking, face recognition, and gesture recognition. Yahboom also provides it with accessories such as a 4-channel tracking sensor, ultrasonic sensor, OLED display, RGB light bar, and comprehensive software and hardware support, which can be used in various application scenarios such as intelligent robot learning, artificial intelligence development, and competition projects. Raspbot-V2 is not only suitable for individual developers and students, but also widely used in education, scientific research, and innovation projects.
Features
Surprising AI vision capabilities
With 2DOF camera PTZ, cooperate with OpenCV and MediaPipe to realize color recognition, target tracking, license plate recognition, gesture and face recognition and other functions.
360 ° omnidirectional movement
Mecanum wheels and 4-channel independent drive motors, it can easily achieve all-round flexible movement and adapt to complex terrain.
Support multiple accessories
Multifunctional robot drive expansion board, supports connecting 4-channel tracking sensor, ultrasonic sensor, OLED display, RGB light bar, users can use the car in various scenarios.
Raspberry Pi 5 as main control
Combined with ROS2-Humble operating system and Python programming environment. The simple programming interface and rich sensor modules make Raspbot-V2 suitable for ROS beginners to quickly get started.
For Superior Version
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Built-in speech recognition and natural language processing, combined with speakers, enable easy voice commands and question-and-answer interactions.
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Integrating multimodal interaction capabilities, including text, image, and voice, it can adjust actions in real time based on environmental changes, supporting free conversation interruptions and dynamic feedback reasoning.
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Integrating a large model and an extensible RAG knowledge system enhances task awareness and complex problem-solving capabilities.