Immerse yourself in the world of autonomous driving with the ROS Deep Learning Autonomous Driving Sandbox Kit. This comprehensive educational platform, built around the NVIDIA Jetson Nano, provides a hands-on experience for developing, testing, and understanding AI-driven vehicle systems. Featuring both Mecanum omnidirectional and Ackermann steering chassis options, it offers unparalleled flexibility for diverse autonomous navigation challenges.
The kit is equipped with an integrated simulation environment and supports advanced sensor modules like LiDAR and depth cameras for real-time mapping (SLAM) and obstacle detection. Coupled with AI recognition capabilities based on OpenCV and TensorFlow, it's an ideal tool for students, researchers, and developers to explore deep learning, path planning, and autonomous decision-making in a practical, controlled sandbox environment.
Its modular and expandable design ensures that the platform can grow with your learning and research needs, allowing for easy integration of additional sensors, mechanical arms, or custom AI modules. Perfect for STEM education, university robotics labs, and autonomous driving competitions.
Key Features
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Comprehensive AI Autonomous Driving Platform
Provides a complete workflow covering object recognition, path planning, and self-driving algorithm experimentation.
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Dual Chassis Options
Available with either Mecanum omnidirectional drive or Ackermann steering for versatile applications in navigation and control.
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Built-in AI and Deep Learning Support
Fully compatible with TensorFlow, PyTorch, and OpenCV AI frameworks for real-time inference and vision tasks.
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Interactive Driving Sandbox System
Features a physical sandbox with lanes, traffic signals, and obstacles for hands-on training in autonomous navigation.
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Supports SLAM and Visual Navigation
Equipped with LiDAR and IMU for real-time mapping (SLAM) and precise localization.
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Open ROS Architecture
Based on an open-source ROS framework with multi-node communication and simulation capabilities.
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Modular and Expandable Design
Detachable and upgradeable components enable easy integration of new sensors, mechanical arms, or AI modules.
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Designed for Education and Research
Tailored for universities, training centers, and research institutions specializing in robot autonomy and AI applications.
Technical Specifications
| Feature | Detail |
|---|---|
| Product Name | ROS Deep Learning Autonomous Driving Sandbox Kit (Mecanum / Ackermann Chassis) |
| Model | Jetson Nano Autonomous Driving AI Educational Kit |
| Main Controller | NVIDIA Jetson Nano Developer Kit (optional Xavier NX / Orin Nano) |
| Operating System & Software | Pre-installed Ubuntu with ROS (Noetic / Melodic), integrated Gazebo simulation environment |
| Drive Type | Mecanum Omnidirectional Drive / Ackermann Steering Chassis (optional) |
| Motor Configuration | DC encoder motors with closed-loop PID control and precise odometry feedback |
| Sensor Modules (Optional) | LiDAR, Depth Camera, IMU Sensor, Ultrasonic Obstacle Detection |
| AI Recognition | Object and Path Detection based on OpenCV and TensorFlow models |
| Power Supply | 12–24 V DC rechargeable battery pack with BMS protection (over-charge / discharge safety) |
| Connectivity Interfaces | USB, UART, CAN, Wi-Fi, Bluetooth, Ethernet |
| Communication & Control | ROS Node control, APP remote operation, and LAN communication |
| Simulation & Teaching Sandbox | Includes AI driving sandbox with lanes, traffic lights, obstacles, and visual marker zones |
| Expansion Ports | Supports GPIO / I2C / UART modules (e.g. mechanical arm or sensor expansion) |
| Structure & Materials | CNC aluminum alloy chassis, acrylic sandbox terrain, high-strength sensor mounts |
| Sandbox Size | Approx. 60 × 60 cm (customizable) |
| Programming Languages | Python, C++, C, ROS Scripting |
| Typical Applications | AI Education, Autonomous Driving Research, Robotics Labs, Competition Training |
| Certification | CE, RoHS, and Educational Export Compliant |