The ROS Ackermann Smart Robot Platform is a compact research and education robot that uses true Ackermann steering with front wheel steering and rear wheel drive. It runs Ubuntu with ROS Noetic or Melodic, supports LiDAR based SLAM for real time mapping and autonomous navigation, and integrates seamlessly with Raspberry Pi 5, Jetson Nano, or Xavier NX for AI and computer vision projects.
Main Specifications
- Model: ROS Ackermann Smart Robot Platform SLAM Ready
- Processor support: Raspberry Pi 5, Jetson Nano, Xavier NX optional controllers
- Operating system: Ubuntu with ROS Noetic or Melodic pre installed or image ready
- Control system: Ackermann steering architecture with front wheel steering and rear wheel power
- Motion control: DC encoder motors with PID closed loop speed control and IMU feedback
- Navigation and mapping: Built in LiDAR for real time SLAM mapping and autonomous path planning
- Sensors optional: 2D LiDAR, 9 axis IMU, RGB camera, ultrasonic sensor array
- Motor drivers: Independent dual channel PWM driver modules with current feedback
- Wheel configuration: Front steering and rear drive Ackermann system simulating real vehicle mechanics
- Chassis material: CNC aluminum alloy frame with integrated mounts for LiDAR and camera
- Image processing: Compatible with OpenCV and TensorFlow lightweight AI modules for object tracking or lane detection
- SLAM algorithms: Supports GMapping, Hector SLAM, Cartographer, and ORB SLAM under ROS framework
- Connectivity: Wi Fi, Bluetooth, USB, UART, and Ethernet interfaces
- Software simulator: Gazebo simulation environment and Rviz visualization for real time mapping display
- Power supply: 12 V Li ion battery pack with on board power management and voltage display
- Programming languages: Python, C plus plus, C for ROS node development and control algorithms
- Chassis dimensions: Approx 32 x 24 x 12 cm customizable layout
- Applications: Autonomous navigation research, SLAM teaching, robot control training, AI education, and R and D projects
- Certifications: CE and RoHS compliant for education and research use
Key Features and Benefits
- Ackermann steering geometry: Realistic automotive style dynamics deliver precise turning, stable cornering, and reproducible experiments.
- Integrated SLAM navigation: On board LiDAR and IMU enable real time mapping, localization, and autonomous path planning with ROS navigation stack.
- AI and computer vision support: Works with OpenCV and TensorFlow for object recognition, lane tracking, and scene understanding on edge controllers.
- Open source ROS workflow: Provides drivers, topics, and packages for motion control, sensor fusion, and visualization in Rviz to speed up development.
- Precision hardware: CNC machined aluminum chassis provides rigidity and smooth drive under load, with dedicated mounts for sensors and cameras.
- Seamless simulation to real: Develop in Gazebo and deploy the same ROS code to the robot for consistent testing and faster iteration.
- Flexible controller options: Ready for Raspberry Pi 5, Jetson Nano, or Xavier NX to match performance and budget needs.
What is Included
- Ackermann steering chassis with DC encoder motors and dual channel motor drivers
- 12 V Li ion battery system with on board power management and voltage display
- Pre configured Ubuntu plus ROS image Noetic or Melodic or pre installation option
- Integrated mounting interfaces for LiDAR and camera
- ROS packages for navigation, SLAM, and sensor integration
Recommended Add ons
- 2D LiDAR for SLAM
- 9 axis IMU
- RGB camera
- Ultrasonic sensor array
- Controllers: Raspberry Pi 5, Jetson Nano, or Xavier NX
Use Cases
- Autonomous driving research and Ackermann kinematics labs
- SLAM and navigation coursework and demonstrations
- AI vision experiments including object tracking and lane detection
- Robot control algorithm design, testing, and benchmarking
- Prototyping for intelligent mobile robotics in education and R and D
Notes
- Dimensions and sensor configurations are customizable on request.
- Controllers and some sensors are optional. Choose configurations to fit your project.