Pathway 01
Robotics
How the car moves and senses.
- Motors, PWM, and encoders
- Differential drive kinematics
- Ultrasonic and IMU sensing
- PID control and sensor fusion
Final project: straight-line PID with an obstacle stop
Software AI is saturated.
Physical AI is the frontier.
"Bridging the physical AI gap requires more than code. It demands a hands-on environment where algorithms move from simulation to reality—learning through trial, error, and physical interaction."
Get started01. The Hardware
AIoT Smart Car System
A high-performance robotic platform designed to interact with its environment using on-board MCU processing. Featuring a magnetic modular system, it allows for effortless hardware swapping. The system offers a wide selection of interchangeable sensing and output modules, alongside an integrated web-camera and microphone for real-time Edge AI applications.
Sensing Modules
- IR Sensing Module
- Ultrasonic Sensor
- IMU (Motion/Gyro)
- RFID Reader
Output Modules
- LCD Display Screen
- Active Buzzer
- Web-Camera
- Microphone
...and more modules coming soon!
02. The Environment
Physical Interactive Playfield
An interactive playfield is an essential part of the system that provide real-time feedback to the car based on different environmental settings. This ecosystem uses integrated tracking and customizable obstacles to bridge the gap between digital logic and physical results.
Interactive Features
- Integrated RFID Tags
- Ground-Truth Sensors
- Tile-to-Car Feedback
- Smart LED Traffic Lights
Sensing Applications
- Dynamic Object Detection
- Navigation Challenges
- Obstacle Avoidance
- Traffic Management
Place different objects based on the specific needs of your task.
03. The Intelligence
Smart Teacher Dashboard
We offer a solution that extends beyond local computing and reactive sensing. Our system forms a cohesive IoT network across multiple devices, controlled through a centralized dashboard.
Instead of managing a single device, users can perform high-level control of the entire network. The dashboard visualizes live conditions on the playfield, enabling advanced operations such as global traffic flow control and real-time environment synchronization.
For educators, the same dashboard doubles as a Smart Teacher Board: oversee every student kit at a glance, track progress in real time, and adjust difficulty or troubleshoot instantly from one place.
Network Control
- Fleet Management & Remote Reset
- High-Level Network Logic
- Remote Device Overrides
- Real-Time Telemetry Data
Live Monitoring
- Visual Playfield Map
- Traffic Flow Regulation
- Environmental Sync Status
- Student Progress & Analytics
Experience full-scale system coordination through a single web interface.
Key Feature: Learning
User-Friendly Block Coding
Skip the syntax errors and focus on the logic. We utilize intuitive block-based programming instead of traditional line coding, significantly reducing the learning curve. This allows students to learn computational thinking in a visual, interactive way that provides instant physical feedback.
- Drag-and-drop logic blocks
- No Python/C++ installation required
- Real-time code execution
Key Feature: Environment
Reactive Smart Game Field
Our Smart Game Field isn't just a track; it's a real-time ecosystem. Using a dedicated tile server, the field provides constant data to the car, allowing it to "sense" the environment and interact with external elements like traffic lights and neighboring tiles.
- Automated traffic reconciliation
- Live environment data streaming
- Dynamic path-planning challenges
Key Feature: AI Training
No-Code AI Model Training
Inspired by Google's Teachable Machine, our training platform makes image recognition accessible to every student — collect samples, label them, and train a working model without writing a single line of code. It is deeply integrated with the Blockly app suite, so trained models plug straight into block-coding projects and deploy to the smart car in one click.
- Sample, train, and deploy — no code
- One-click model deployment to the car
- Fully integrated with the Blockly suite
Learning Pathways
Curriculum
Course materials cover three focuses: how the car moves and senses, how it finds its route and talks to the cloud, and machine learning that runs on the car itself. The content each course covers is freely selectable, and every pathway ends with a hands-on final project.
Pathway 02
IoT
How the car finds its route and talks to the cloud.
- Graphs and Dijkstra routing
- FreeRTOS multitasking
- Firebase realtime telemetry
- RFID playfield localization
Final project: the fleet dashboard
Pathway 03
Physical AI
Machine learning that runs on the car itself.
- Collecting and labeling images
- Training a CNN without code
- Live inference on the ESP32-P4
- Road-sign recognition
Final project: a vision-driven mission
Further units are in preparation for every pathway.
Sustainability and Scale
Hardware Acquisition
Schools purchase the physical ecosystem (Car Kits, Game Fields) once.
Platform Subscription
Ongoing access to the AI Dashboard and telemetry tools.
Content Ecosystem
On-demand purchase of specialized courses and teaching packages.
Development Trajectory
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Phase 01: 2024 Q2
Academic Validation & Pilot
The first version of our AIoT prototype was designed and successfully deployed in an undergraduate course in Hong Kong for rigorous hardware testing and pedagogical verification.
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Phase 02: 2024 - 2025
Extended Pilot Program
Conducting second and third rounds of pilot programs across various educational settings to refine the user interface and hardware durability based on student "trial and error."
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Phase 03: 2025 Q3 – Ongoing
Commercial R&D & Integration
Developing an improved, integrated version of the entire system. This phase focuses on hardware refinement, seamless IoT connectivity, and finalizing the Smart Teacher Dashboard.
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Launch: 2026 Q3
Commercial Deployment
Full-scale market launch featuring commercial-grade packaging, ready-to-use teaching materials, and a complete interactive wiki for global STEAM classrooms.
About Us
CHAN Ming Chun, Jason
Founder
Hardware Engineering Lead
An electronic and robotic engineer specializing in the structural and circuit design of high-performance systems. As the former Captain of the RoboMaster Team ENTERPRIZE, he brings elite competitive robotics experience to the R&D process, overseeing the mechanical and electronic integration of the AIoT ecosystem.
ZHANG Yunxin, Vicky
Co-Founder
Software Engineering Lead
An HKUST Integrative Systems and Design (ISD) student specializing in full-stack architecture and systems engineering. As the Lead Software Developer, she engineered the frontend and backend framework for the AIoT ecosystem. She is driven by a mission to make high-end technology accessible to the next generation of engineers through intuitive, high-performance educational kits.
Prof. SONG Sheunghui
Technical Consultant
Dr. S.H. Song is now an Associate Professor jointly appointed by the Division of Integrative Systems and Design (ISD) and the Department of Electronic and Computer Engineering (ECE) at the Hong Kong University of Science and Technology (HKUST). He interested in the research on Engineering Education and served as an Associate Editor for the IEEE Transactions on Education.
Academic Profile