Professional supplier of AI and robot teaching equipment

  • Industrial-grade design
  • 1-year warranty
  • Free technical support
  • Customization available
  • Educational resources included
  • Compatible with major platforms
  • Supports secondary development

AI Experiment Box (Standard)

The AI Experiment Box(Pro) is an all-in-one artificial intelligence education platform designed for higher education, vocational training, and AI laboratory applications. Integrating edge computing, computer vision, speech processing, robotics, and embedded sensing technologies, it provides a unified environment for AI teaching, experimentation, and project-based learning.

Notice:

The listed price is for standard product reference only.
It does not apply to institutional projects, tenders, system integration, training, installation, commissioning, or customized solutions.
For project-based requirements or bulk orders, please contact us for an official quotation.

Specifications

AI Experiment Box Specifications

Dimensions (in)

18.9×15×7.9

Weight (kg)

11

Communication Interface

USB, Wi-Fi, Bluetooth

Structure

Aluminum alloy, integrated design, includes keyboard, mouse, power adapter, and educational tools; plug-and-play

Display

17-inch IPS screen, ≥ 1920×1080 resolution

Integrated Components

Robotic arms, 2D vision, depth vision, two-axis gimbal, voice module, embedded sensors, etc.

Computing Unit

6-core NVIDIA Carmel ARM CPU, 8GB RAM, 128GB storage, NVIDIA Volta GPU with 8GB memory

Vision Systems

2D vision: ≥ 640×480 resolution, Depth camera: ≥ 640×400 resolution

Robot Arm

5-axis, 15cm gripping range, two-finger gripper, kinematic solver, one-button start/reset

Sensors

Ultrasonic, temperature, heart rate, pressure, Bluetooth, gyro, OLED display

Open-Source Software

Full software and source code for secondary development

Video

1.Voice-Based Robot Arm Control

2. Voice based intelligent sensor control

3. Flame sensor

4.Human Radar Detection

5.Face Detection and Distance Measurement

6.Object Edge Length and Area Measurement

Description

AI Experiment Box for AI and Robotics Education with Jupyter Notebook Support
  • Supports the teaching of courses or knowledge points in Python programming, machine learning, deep learning, digital image processing, computer vision, speech recognition, embedded systems and applications, intelligent robotics, etc.
  • Features an integrated design with a metallic aluminum alloy frame for enhanced durability.
  • Equipped with a 17-inch HD display, full keyboard, mouse, and experimental tools, supporting plug-and-play with no additional configuration needed by the user.
  • Utilizes an edge computing terminal for computational power, supporting the deployment of mainstream AI frameworks such as PyTorch and TensorFlow.
  • Combines Linux operating system, deep learning, machine vision, speech recognition, robot arm control, and embedded sensors, among other components and technologies.
  • Supports a variety of experimental combinations, including 2D vision + robotic arm, depth vision + PTZ, speech + sensors, speech + 2D/depth vision, and speech + robotic arm.

Open Experimental Environment

The AI Experiment Box allows the experiment code to be executed in the Jupyter Notebook environment with the following features:

  • Both teachers and students can directly conduct interactive programming experiments through their browsers.
  • Markdown editing is supported, with cells used for coding and writing text, including formatting titles, mathematical formulas, etc., making it easier to explain code and suitable for teaching scenarios. Code can be separated into different cells for step-by-step debugging, with interactive monitoring of variable values and types during testing.
  • The provided experimental environment allows for experiment verification through command execution in the terminal.
  • The environment supports multiple students working with different models for sample recognition, meeting the requirements of various experimental projects.
  • The experimental environment supports multiple deep learning frameworks, including but not limited to TensorFlow, PyTorch, etc.

Open Source Code

  • All software frameworks are fully open-source.
  • Algorithm-level source code is provided for transparency and customization.
  • The product supports secondary development for further adaptation.
  • Comprehensive experiment guides are included to assist users.
  • Technical documentation is available to facilitate understanding and usage.
  • Architectural and design documentation for both hardware and software is provided.
  • AI + Vision Sorting

The robotic arm combines with the vision system for target sorting and smart stacking. It uses deep learning models for complex object recognition and real-world industry training.

  • AI + Depth Vision

Depth vision enables height, distance, and contour detection, ideal for obstacle detection, live object recognition, and target tracking experiments.

  • AI + Speech Processing

The microphone supports sound detection and recognition. Interaction with the AI processor guides the robotic arm to perform tasks based on voice commands.

  • AI + Embedded Sensors

Offers 12 types of sensors for experiments like facial recognition, voice control, and temperature control systems.

Components

Freely add or remove devices as needed. Contact us for assistance.

Courses

Education-focused, enhances learning​

(1) Data Types, Conversion, and Operations

(2) Basic Usage of Python Operators, Built-in Functions, and Sequences

(3) Program Selection Structures

(4) Experiment on Loop Structures

(5) List Experiment

(6) Set Experiment

(7) Function Applications

(8) String Experiment

(9) Data Processing in Python

(10) 0 File Operations in Python

(11) Extracting telephone numbers using regular expressions

(12) Data Visualisation

(13) Python Multi-Processing

(14) Multithreading in Python

(15) The Difference Between Python Processes and Threads

(16) Python-based Serial Communication

(17) Socket TCP Communication in Python

(18) UDP Socket Communication in Python

(19) Modbus Communication in Python

(20) Understanding Object-Oriented Programming in Python

(21) Using Python Classes and Class Instantiation

(22) Using Instantiated Objects in Python

(23) Using Inheritance in Python Classes

(24) Setting up the PyQt5 environment

(25) Using PyQt5

(1) Data Classification on the AdaBoost Movie Dataset

(2) Verification of a Two-Coin Toss Model using the EM algorithm

(3) Clustering of Unlabeled Data Using the K-Means Algorithm

(4) Film Genre Classification Based on the K-Nearest Neighbors Algorithm

(5) Dynamic Pedestrian Detection Based on HOG and Support Vector Machine (SVM)

(6) Decision Tree-Based Diagnosis of Breast Cancer

(7) Naive Bayes-Based Spam Filtering

(8) Face Recognition Based on Random Forests

(9) House Price Forecasting Based on Linear Regression

(1) Linear Regression Modeling and Training

(2) Clothing Classification Using a Neural Network

(3) Neural Network Regularization-Based Clothing Classification

(4) Optimization of Neural Network Parameters

(5) Building and Testing Neural Network Models

(6) Design of an Optimization Model Based on Residual Networks

(7) Neural Network Optimizer

(8) Handwritten Digit Recognition Based on LeNet

(9) Image Object Detection Based on YOLOv5

(1) Algebraic Operations on Images

(2) Image Processing, Encryption, and Decryption

(3) Spatial Domain Filtering

(4) Affine Transformations of Images

(5) Frequency-Domain Filtering of Images

(6) Morphological Rice Grain Detection

(7) Image Extraction Based on the Canny Algorithm

(8) Watershed-Based Contour Segmentation

(9) Hu Moment-Based Shape Matching

(10) Smoothing and Morphological Processing

(1) Introduction to the Vision System

(2) Pixel Dimension Measurement

(3) Bearing Dimension Measurement

(4) Object Localization and Angle Measurement

(5) Measurement of Object Edge Lengths and Area

(6) Object Color and Shape Recognition

(7) Offline Barcode Recognition

(8) Offline QR Code Recognition

(9) Character Segmentation

(10) Character Training and Recognition

(11) Product Surface Defect Detection Based on Morphological Processing

(12) LED Defect Detection

(13) Screw Appearance Defect Detection

(14) Camera Checkerboard Calibration

(15) Electronic Product Recognition

(16) License Plate Recognition Using OpenCV

(17) Vision-Based Barcode Recognition

(18) Vision-Based QR Code Recognition

(19) Vision-Based Object Shape and Color Recognition

(20) Vision-Based Fruit Classification

(1) 3D Camera Face Detection and Distance Measurement

(2) 3D Camera Face Detection and Pan-Tilt Tracking

(3) Face Enrolment and Recognition

(4) Face Mask Detection

(5) Pedestrian Detection

(1) Introduction to Intelligent Sensing Systems

(2) OLED Display

(3) Temperature and Humidity Monitoring

(4) Human Body Radar Detection

(5) Light Detection

(6) Heart Rate Monitor

(7) Ultrasonic Rangefinder

(8) Smart traffic lights

(9) Fan Speed Control

(1) Audio Recording

(2) Audio Recognition

(3) Real-time speech recognition

(4) Voice-based Intelligent Sensor Control

(5) Voice-controlled robotic arm

(6) Text-to-speech

(1) Robotic Arm Fundamentals and Basic Operations

(2) Robotic Arm Teaching and Motion Control

(3) Calibration of the Robotic Arm and the Vision System

(4) Vision-Based Robotic Arm Object Classification

(5) Vision-Based Robotic Arm Object Palletising

(6) Vision-Based Robotic Arm Digital Sorting

(7) Vision-Based Robotic Arm Fruit Sorting

Contact us if you need a custom course.

Cases

FAQ

The AI Experiment Box is an educational tool that integrates various artificial intelligence technologies, designed to help students understand and practice core AI and robotics concepts. It includes components like sensors, drivers, control systems, and supports experiments in machine learning, image recognition, robotics control, and more.
This experiment box is designed for use in higher education institutions and related educational organizations. It offers a range of experiments that are suitable for both foundational AI knowledge as well as more advanced research and development needs.
The experiment box contains a variety of hardware components, including a 5-axis robotic arm, sensors, actuators, AI modules, control boards, and more. It also comes with accompanying textbooks and experiment manuals to help users get started easily.
Yes, we offer comprehensive teaching resources, including textbooks, experiment plans, and user manuals, to help both teachers and students effectively utilize the equipment for teaching and experiments.
Yes, we can customize experiment projects and course content based on the needs of schools or educational institutions. We offer various levels of difficulty and different areas of focus to meet specific teaching objectives.
Yes, certain experiment projects support cloud connectivity, allowing for data uploading, remote control, and sharing of experiment results. Additionally, the box can connect to large models, enabling more intelligent experiments.
The experiment box is designed to be easy to install and set up. It comes with a detailed installation manual and video tutorials, making it accessible even for users without prior experience.
We offer a one-year warranty for the equipment, during which defective parts will be replaced free of charge. For maintenance, you can contact our customer service for technical support. We also provide equipment upgrades and repair services.
Yes, the equipment is designed to be compatible with other educational tools. It supports connection with various external sensors, devices, and platforms, making it easy to integrate with existing teaching setups.
We provide round-the-clock online technical support and after-sales service. You can reach our customer service team via phone, email, or our official website for any inquiries or assistance.

Download

Documentation

Course - 3D Camera-1

Course - 3D Camera-2

Model

Please submit your download request, and we will send the relevant information to your email within 2 business days.