GX-MAT-09S
Modular Embodied Robotics Innovation Kit
From Design to Control
One system for structure, actuation, and intelligence.
The GX-MAT-09S is an embodied intelligence robotics training platform designed for systematic learning of robot structure, actuation, and intelligent systems.
Embodied intelligent robots integrate perception, decision-making, and manipulation for operation in unstructured environments, typically as mobile composite robots.
This platform decomposes a typical embodied composite robot into modular units, enabling deeper understanding of structure, actuation, and intelligent systems.
Through a modular architecture, 11 types of chassis and 7 types of robotic arms can be freely combined to achieve up to 88 configurations, enabling hands-on experience of the full process from design, construction, to control.
Integrates AI vision, voice, pose detection, obstacle avoidance, line tracking, LiDAR, and other sensors for comprehensive perception.
The control system adopts a three-layer architecture based on Arduino, STM32, and RDK X5, supporting applications from entry-level learning to advanced AI development with Ubuntu and ROS. It is suitable for a wide range of use cases, including education, research, and robotics competitions.
■ Applications & Use Cases
University Robotics Education
Integrated Training
Research & Development
Competitive Training
Key Features
Embodied System Decomposition Learning
Built on a mobile composite robot, it modularizes structure, actuation, and intelligent control for deeper system understanding.
Modular Learning Process
Combines 11 chassis types and 7 robotic arms to achieve up to 88 configurations, enabling end-to-end learning from design to control.
Multimodal Perception
Integrates AI vision, voice, IMU, obstacle avoidance, line tracking, and LiDAR to build environmental perception capabilities.
Hierarchical Control Architecture
Three-layer architecture (Arduino, STM32, RDK X5) supports entry-level learning to advanced AI development with Ubuntu/ROS.
Supports Education and Competition
Supports education in robotics, sensors, ROS, and navigation, and is also suitable for research and competitions.
Specifications
■ Configuration List
Main Control Board ×3
STM32F4 / Arduino MCUs (Keil 5 / Arduino IDE / VS Code supported)
RDK X5 AI board
Expansion Board ×1
DC Motors, Servo Motors, Stepper Motors
Integrated drive circuits for serial communication.
Structural Components ×110+
Beam Structures, Grippers, Linear Motion Modules, etc.
Supports diverse robot configurations
Motors ×14
12V high-power DC motors with feedback ×6
digital bus servos ×8 built-in
Sensors ×7
LiDAR / IMU / Vision Camera / Ultrasonic Sensor / Monocular Camera
Line Tracking Module / Offline Voice Module
Wheel Components ×19
Differential Wheels, Mecanum Wheels, Omni Wheels, Casters
Including Fixed Wheels
Other Components ×200+
Stainless Steel Screws, Nuts, Metal Spacers, Nylon Standoffs, etc.
Assembly Tools ×8
Hex Drivers, Wrenches, etc.
■ Sensor Configuration
■ Control System Architecture
Robot Configuration Examples
Module combinations enable up to 88 robot configurations, supporting differential, omnidirectional, steering, and dual-arm systems.
■ Chassis (11 types)
3-Wheel Differential Drive Chassis
3-Wheel Omni Wheel Chassis
3-Wheel Omni Wheel Chassis
4-Wheel Differential Drive Chassis
4-Wheel Mecanum Wheel Chassis
4-Wheel Differential Drive Chassis
4-Wheel Omni Wheel Chassis
4-Wheel Mecanum Wheel Chassis
4-Wheel Steering Chassis
6-Wheel Differential Drive Chassis
6-Wheel Differential Drive Chassis
■ Robotic Arms (8 types)
SCARA Robotic Arm
4-DOF Robotic Arm
5-DOF Robotic Arm
6-DOF Robotic Arm
Experiment Items
Basic ROS Experiments
■ ROS Basics – Topics, Services, and Turtlesim Lab
■ ROS Basics – Workspace Setup and Package Migration
■ ROS Tools – RViz Visualization Tool
■ ROS Applications – Establishing Serial Communication with Arduino
■ ROS Applications – Mobile Robot Velocity Measurement
■ ROS Applications – Keyboard-Based Mobile Robot Motion Control
■ ROS Applications – MoveIt! Setup
■ ROS Applications – Robot Arm Motion Control
Robotic Vision Experiments
■ Vision Basics – Color Recognition / Shape Recognition / QR Code Recognition
■ Vision Basics – Color Ring Recognition
■ AI Vision – Object Dataset Collection and Annotation
■ AI Vision – Object Model Training and Recognition Implementation
■ Vision Applications –
■ Vision Applications – Object Recognition and Mobile Robot Following
■ Vision Applications – Robot Arm–Camera Calibration and Transformation
■ Vision Applications – Vision-Based Robot Arm Grasping
Robot Navigation and Localization
■ Drive – PID Closed-Loop Control for Mobile Robots
■ Drive – LiDAR Driver Installation
■ Communication
■ Communication – Precise Mobile Robot Motion Control via Keyboard
■ Localization & Navigation – Mapping
■ Localization & Navigation – Map-Based Navigation
Large-Scale AI Model Integration and Application
■ Voice Interaction – ASR (Automatic Speech Recognition) Model
Integration
■ Voice Interaction – LLM (Large Language Model) Integration for Natural Language Understanding
■ Voice Interaction – TTS (Text-to-Speech) Interface Integration
■ Communication – Physical Interface Invocation via Function Calling
■ Application – Voice-Interactive Vision-Based Face Recognition and Autonomous Cart Following
■ Application – Voice-Interactive Vision-Guided Robotic Arm Handling