A research-grade omnidirectional mobile robot platform designed for indoor autonomy, SLAM learning, and AI navigation experiments. The chassis integrates dual front and rear LiDAR sensors with ROS/ROS2 SLAM and navigation stacks, delivering 360-degree perception, accurate localization, and precise motion control on smooth or tight indoor spaces.
Model Overview
- Product Name: ROS Mecanum Wheel Robot Base with Dual LiDAR SLAM and Navigation System
- Model: Dual-LiDAR ROS Mobile Chassis with Raspberry Pi 5 Controller
- Main Controller: Raspberry Pi 5 (64-bit CPU / 8 GB RAM / AI co-processor support)
- Operating System: Ubuntu with ROS Noetic or ROS2 Humble pre-installed, including SLAM and Navigation packages
- Drive System: Four-wheel Mecanum drive enabling omnidirectional translation and in-place rotation
- Motor Control: Encoder motors with PID closed-loop for precise motion and odometry
- Chassis Material: CNC-machined aluminum with shock resistance and anti-vibration mounts
- Dimensions and Payload: ≈ 45 × 35 × 20 cm base, load capacity up to 15 kg
- Certifications: CE / RoHS / Education and Research Compliant
Key Features
- Omnidirectional Mecanum wheel chassis for 360-degree mobility and fine trajectory tracking.
- Dual LiDAR system (front + rear) providing continuous 360-degree coverage for robust SLAM and collision avoidance.
- Advanced SLAM and navigation stack supporting Cartographer, GMapping, and AMCL for mapping and path planning.
- Expandable AI computing platform leveraging Raspberry Pi 5 for on-board vision with OpenCV or TensorFlow Lite.
- Research-level sensor fusion combining LiDAR, IMU, ultrasonic, and visual inputs for precise localization.
- Modular open design fully compatible with ROS packages and extensible with arms, cameras, and AI modules.
- Ideal for universities, research labs, and training programs focused on autonomous navigation and mapping.
Sensors and Perception
- Sensors: Dual 2D LiDAR (front + rear) for 360-degree SLAM coverage and obstacle avoidance
- Additional Sensors: 9-axis IMU, ultrasonic distance sensors, depth camera (optional RGBD or stereo module)
- Mapping: 2D and 3D SLAM compatible using LiDAR fusion for simultaneous localization and mapping
Navigation and Control
- Navigation System: ROS navigation stack with AMCL and Cartographer for real-time mapping and path planning
- Motor Control: High-precision encoder motors with PID closed-loop feedback
- Software Support: Gazebo simulation and Rviz visualization; ready for ROS navigation and SLAM toolsets
Computing, Connectivity, and Power
- Programming Languages: Python, C++, C for ROS nodes and OpenCV AI projects
- Connectivity: USB, UART, I2C, Wi-Fi, Bluetooth, Ethernet (optional 4G/5G expansion module)
- Power System: 12–24 V Li-ion battery pack with charging protection and power distribution board
- Display (Optional): 7-inch touch screen for live monitoring and manual control
Applications
- AI navigation research and indoor autonomous robotics
- SLAM learning, dataset collection, and benchmarking
- Multi-sensor fusion and perception algorithm development
- Autonomous robotics training and AI curriculum development
Technical Specifications
| Main Controller | Raspberry Pi 5 (64-bit CPU, 8 GB RAM, AI co-processor support) |
| Operating System | Ubuntu with ROS Noetic or ROS2 Humble, SLAM and Navigation packages pre-installed |
| Drive System | Four-wheel Mecanum drive for omnidirectional movement (translation and rotation) |
| Motor Control | High-precision encoder motors, PID closed-loop feedback, accurate odometry |
| Sensors | Dual 2D LiDAR (front + rear), 9-axis IMU, ultrasonic sensors, optional RGBD/stereo depth camera |
| Navigation and Mapping | ROS navigation stack, AMCL, Cartographer, 2D/3D SLAM compatibility |
| Power System | 12–24 V Li-ion battery, charging protection, power distribution board |
| Connectivity | USB, UART, I2C, Wi-Fi, Bluetooth, Ethernet (optional 4G/5G module) |
| Chassis Material | CNC aluminum frame with shock resistance and anti-vibration mounts |
| Dimensions and Payload | ≈ 45 × 35 × 20 cm base, payload up to 15 kg |
| Software Support | Gazebo simulation, Rviz visualization, ROS navigation and SLAM toolsets |
| Display | Optional 7-inch touch screen for monitoring and manual control |
| Applications | AI navigation research, autonomous robotics training, multi-sensor fusion, SLAM learning, AI curriculum |
| Certifications | CE, RoHS, Education and Research Compliant |