Overview
Raspbot V2 is a robot car designed for Raspberry Pi 5 learning and AI development. It uses a metal body bracket and Mecanum wheels for 360° omnidirectional movement, and combines a 1MP USB camera with a 2DOF PTZ to support AI visual recognition and interaction. Development is based on Raspberry Pi 5 with Python, and it supports Raspberry Pi OS and ROS2 Humble for robotics and perception projects.
Note: Only the Superior Version supports AI large model features and AI voice interaction.
Key Features
AI vision recognition (OpenCV + MediaPipe)
- Color recognition
- License plate recognition (recognition results can be displayed on the OLED in real time)
- Human posture estimation (33 key point coordinates)
- YOLOv5-lite object recognition
- Garbage recognition (recognition results can be displayed on the OLED in real time)
- Gesture recognition / interaction features including brush and finger control
AI visual interaction (2DOF camera PTZ)
- Color recognition / tracking / following
- Face detection / tracking / following
- Gesture control (supports recognition of 14 types of gestures; supports controlling movement forward, backward, left, and right through gestures)
- QR code control (performs movements based on recognized QR information)
- AprilTag tag code recognition / tracking / following
- Visual line tracking
Omnidirectional mobility
- Mecanum Wheel 360° omnidirectional movement (forward, sideways, diagonal, rotation)
- 4-channel independent drive motors
Sensor and accessory expansion
- Multifunctional robot drive expansion board
- Supports connecting: 4-channel tracking sensor, ultrasonic sensor, OLED display, RGB light bar
- Sensor Expansion functions listed: OLED data display, ultrasonic avoiding, ultrasonic tracking, infrared tracking
AI autonomous driving (listed functions)
- Speed limit sign recognition
- Traffic light recognition
- Horn sign recognition
- Track map
Superior Version: large model + voice interaction (only for Superior version)
- Multimodal Large Model Embodied Intelligence: voice interaction, scenario understanding, intent prediction, customized RAG knowledge base
- New AI Large Model Features: integrates visual, audio, and text information for environmental perception, task planning, and dynamic execution
- Upgraded AI large model voice module: supports far-field voice pickup, echo cancellation, and ambient noise reduction; recognizes voice commands and responds naturally
- Built-in speech recognition and natural language processing; combined with speakers for voice commands and question-and-answer interaction
- Upgraded offline voice interaction control: control movement, light strip changes, color recognition, autonomous driving, and tracking tasks without a large model
- Built-in three AI models: Large language model, Voice large model, Vision large model
Specifications
| Product | RASPBOT V2 / Raspbot V2 |
| Main control | Raspberry Pi 5 (4GB/8GB/16GB) |
| Programming language | Python |
| Supported system | Raspberry Pi OS |
| ROS system | ROS2 Humble |
| Chassis / drive | Mecanum wheels; 4-channel independent drive motors |
| Camera | 1MP USB camera |
| PTZ | 2DOF camera PTZ; 180° horizontal rotation; 110° vertical rotation |
| Power to Raspberry Pi 5 (supported) | 5.1V/5A (Raspberry Pi 5 power supply protocol) |
| Raspberry Pi 5 performance statement | 2–3 times the performance of Raspberry Pi 4B (as stated) |
Version Comparison (as listed)
| Item | Standard Version | Superior Version |
|---|---|---|
| Master Control | Raspberry Pi 5 (4GB/8GB/16GB) | Raspberry Pi 5 (4GB/8GB/16GB) |
| AI large model voice module | / | Supported |
| AI large model function | / | Supported |
| AI voice interaction | / | Supported |
| AI visual interaction | Supported | Supported |
| Master control system | Raspberry Pi OS | Raspberry Pi OS |
| ROS system | ROS2 Humble | ROS2 Humble |
| Recommended users | Suitable for learning AI vision functions | Suitable for learning AI large model, AI voice interaction, and AI vision function applications |
Applications
- Raspberry Pi 5 robotics learning and sensor practice
- OpenCV / MediaPipe computer vision experiments (tracking, recognition, posture estimation)
- ROS2 Humble beginner projects and course/competition projects
- Embodied intelligence demos (Superior Version)
Manuals / Tutorials
Videos
For pre-sales compatibility questions or after-sales help, contact support at https://rcdrone.top/ or email support@rcdrone.top.
Details

Raspbot V2 is built for Raspberry Pi 5 learning, combining omnidirectional mecanum drive with a PTZ camera for AI vision projects.

Designed for Python development on Raspberry Pi OS and ROS2 Humble, with room to expand into sensors, displays, and interaction modules.

A wide set of demos and course content supports everything from visual recognition to autonomous-driving exercises.

Superior Version adds large-model features and voice interaction for multimodal perception and natural command control.

Choose Standard or Superior based on whether large-model AI and voice interaction are needed for your projects.

Course modules progress from setup and basic motion to vision tracking, sensor behaviors, and robotics workflows.

Multimodal workflows connect vision and audio to planning tasks, customizing a knowledge base, and executing commands.

Hands-on video tutorials with English subtitles help teams move from first run to advanced demos faster.

Large language, voice, and vision capabilities work together for interactive demos and scene understanding.

Try autonomous cruise and target following using visual recognition plus semantic instructions (Superior Version).

Scene understanding and task collaboration demos turn recognized objects into actionable navigation and responses.

Vision demos include pose estimation, object detection, and interactive controls like brush and finger tracking.

Interactive modes cover color/face tracking, gesture commands, QR control, and AprilTag-based navigation.

Voice commands can drive movement, trigger lighting changes, and start tracking behaviors (Superior Version).

Mecanum wheels enable sideways, diagonal, and in-place rotation, while the 2DOF PTZ helps keep targets centered.

Switch between infrared line following and camera-based visual tracking depending on the course scenario.

Autonomous-driving exercises include turn decisions plus recognition of traffic lights and road signs.

Add the optional driving map to practice route planning, line tracking, and sign/traffic-light challenges.

Yahboom Raspbot V2 supports ultrasonic obstacle avoidance and follow modes, infrared tracking, and a buzzer whistle function.

Yahboom Raspbot V2 supports app control (iOS/Android), PC control via JupyterLab, and infrared remote control for flexible operation.

Yahboom Raspbot V2 supports ROS2 Humble and RViz simulation for visualizing robot movement and camera images during development.

Yahboom Raspbot V2 resources are organized into downloadable tutorial and code folders for different modules and courses.

Yahboom Raspbot V2 comes with organized course materials, video tutorials, and after-sales support resources to help with setup and learning.

Yahboom Raspbot V2 uses a dual-model reasoning architecture with dynamic feedback actions to complete tasks step by step.

Yahboom Raspbot V2 adds RAG-based knowledge Q&A and supports free conversation interruption for smoother voice interactions.

The Yahboom Raspbot V2 uses a stacked, modular layout with a metal body, Mecanum wheels, and plug-in camera and sensor modules for Raspberry Pi builds.

The Raspbot V2 Raspberry Pi 5 robot driver board provides clearly labeled connections for Type‑C power, motor outputs, I2C/OLED, ultrasonic, and servo interfaces.

Yahboom Raspbot V2 includes mm dimension drawings and a quick spec list for the Raspberry Pi 5 master controller and ROS2 Humble support.

The Yahboom Raspbot V2 kit includes the robot car chassis plus key parts like a Raspberry Pi, camera module, battery, charger, and small assembly accessories.
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