Creative Programming Project Cases Based on Open-Source Ai toy

　　Creative programming projects are the core of campus maker education, integrating AI technology, programming logic, and real-world problem-solving. Below are 10 practical cases tailored to the 5 recommended open-source AI toys, covering primary to university levels. Each case leverages the toy’s open-source hardware expansion (from sensors to AI accelerators) and supports graphical/professional programming tools, enabling students to cultivate engineering thinking, algorithm application, and interdisciplinary innovation.

　　Project Design Principles

　　Hardware Compatibility: Strictly based on the previously listed upgrade accessories (sensors, communication modules, AI processing units) to ensure practicality.

　　Progressive Difficulty: From Scratch graphical programming (primary) to Python/ROS development (university), matching cognitive levels.

　　Campus-Oriented Scenarios: Focus on campus life, teaching assistance, and environmental protection to enhance practical application.

　　Interdisciplinary Integration: Combine computer science, mathematics, biology, art, and language education (STEAM integration).

　　Open-Source Resources: Utilize official SDKs, open-source libraries (TensorFlow Lite, OpenCV), and community tutorials for easy replication.

　　Creative Programming Project Cases

　　1. Entry-Level (Primary & Junior High: Ages 8-14)

　　▶ Case 1: "Smart Campus Plant Butler" (FoloToy Open-Source AI Companion)

　　Core Creativity: An AI-assisted plant care system that monitors growth environment and interacts with students via voice.

　　Required Hardware: FoloToy host + LANDZO Micro:bit Sensor Kit (temperature/humidity/light sensors) + ESP8266 Wi-Fi Module + 0.96-inch OLED Display.

　　Programming Tools: Scratch 3.0 (graphical programming) / MicroPython.

　　Implementation Logic:

　　Sensor Data Collection: Real-time capture of soil humidity, ambient temperature, and light intensity.

　　AI Voice Interaction: Program FoloToy to broadcast "The sunflower needs watering!" when humidity 0%, or "Light is sufficient today!" via voice synthesis.

　　Cloud Data Synchronization: Use ESP8266 to upload data to a campus maker platform (e.g., Thingspeak) for long-term growth tracking.

　　Visual Feedback: Display sensor values and plant status on the OLED screen.

　　Interdisciplinary Value: Integrates biology (plant growth needs), programming (data logic judgment), and environmental science (ecological monitoring).

　　▶ Case 2: "Obstacle-Avoiding Interactive Companion" (Shifeng AI Magic Star)

　　Core Creativity: A toy that avoids obstacles autonomously and responds to gestures, combining motion control and human-computer interaction.

　　Required Hardware: Shifeng AI Magic Star + HC-SR04 Ultrasonic Sensor + SG90 Servo Motor + Bluetooth 5.0 Module.

　　Programming Tools: Mixly (Arduino graphical programming) / Arduino IDE (C++).

　　Implementation Logic:

　　Obstacle Avoidance Algorithm: Program the ultrasonic sensor to detect distances; when the servo motor controls the toy to turn left/right automatically.

　　Bluetooth Remote Interaction: Develop a simple smartphone APP (via MIT App Inventor) to send gesture commands (e.g., "wave" to make the toy dance).

　　Emotional Feedback: Link temperature data (DHT11 sensor) to voice tone—warmer temperatures trigger more lively responses.

　　Interdisciplinary Value: Combines mechanical engineering (motion control), programming (algorithm logic), and art (gesture interaction design).

　　2. Intermediate (Junior & Senior High: Ages 12-18)

　　▶ Case 3: "Campus Intelligent Navigation Robot" (UBTECH UGOT AI Education Robot)

　　Core Creativity: A robot that navigates campus paths autonomously, recognizes landmarks, and provides guidance for visitors.

　　Required Hardware: UBTECH UGOT + OV7670 Camera Module + MPU6050 Gyroscope + 4G LTE Module + DC Gear Motor with Encoder.

　　Programming Tools: Python (OpenCV + ROS Lite).

　　Implementation Logic:

　　Visual Landmark Recognition: Use OpenCV to train the camera to identify campus landmarks (e.g., classroom numbers, statues) via image classification.

　　Path Planning: Combine gyroscope data and encoder feedback to implement differential drive navigation (avoiding stairs/obstacles).

　　Remote Monitoring: Use 4G module to stream real-time video to teachers’ phones for safety supervision.

　　Voice Guidance: Integrate DeepSeek API to answer visitor questions (e.g., "Where is the library?").

　　Interdisciplinary Value: Integrates computer vision (image recognition), robotics (motion control), and geography (campus map modeling).

　　▶ Case 4: "Maker Lab Material Management System" (TensorFlow Lite AI Kit for BeagleBone)

　　Core Creativity: An intelligent system that identifies and tracks maker lab tools via RFID and AI vision, simplifying inventory management.

　　Required Hardware: BeagleBone AI Kit + RFID RC522 Module + BME280 Sensor + 128GB MicroSD Card + 2.8-inch Touch Screen.

　　Programming Tools: Python (TensorFlow Lite + SQLite).

　　Implementation Logic:

　　Tool Identification: Attach RFID tags to tools; the RC522 module reads tags when tools are taken/returned, recording data to a local database.

　　Environmental Monitoring: BME280 sensor tracks lab temperature/humidity to prevent tool damage (e.g., corrosion in high humidity).

　　Visual Interface: Design a touch-screen GUI to display tool status (available/borrowed), inventory alerts, and environmental data.

　　Data Analysis: Use TensorFlow Lite to analyze borrowing patterns (e.g., which tools are most popular) for lab resource optimization.

　　Interdisciplinary Value: Combines IoT (RFID communication), data science (database management), and industrial design (user interface).

　　3. Advanced (Senior High & University: Ages 16+)

　　▶ Case 5: "Social Assistive Robot for Classroom" (USC "Build Your Own Robot Friend" Module)

　　Core Creativity: A humanoid robot that assists teachers in class (e.g., emotion detection, group collaboration guidance) with custom AI models.

　　Required Hardware: USC Open-Source Module + NVIDIA Jetson Nano + Servo Motor Kit (MG996R) + LoRa Module + 2.8-inch Touch Screen.

　　Programming Tools: Python (TensorFlow + ROS) / C++ (custom model training).

　　Implementation Logic:

　　Emotion Detection: Train a TensorFlow Lite model on Jetson Nano to recognize students’ facial expressions (happy/frustrated) via the camera.

　　Gesture Interaction: Program servo motors to simulate facial expressions (smiling/nodding) and arm gestures (pointing to blackboard).

　　Multi-Robot Collaboration: Use LoRa modules to connect multiple robots for group activities (e.g., dividing students into teams for projects).

　　Customizable Functions: Allow students to modify open-source code to add features (e.g., sign language translation for hearing-impaired classmates).

　　Interdisciplinary Value: Integrates AI ethics (emotion-aware design), mechanical engineering (bionic structure), and education (classroom interaction design).

　　▶ Case 6: "Multi-Robot Collaborative Campus Patrol Network" (USC + UBTECH UGOT)

　　Core Creativity: A team of robots that patrol campus together, monitoring safety (e.g., unauthorized access, fire hazards) and sharing data in real time.

　　Required Hardware: 3x USC Modules + 2x UBTECH UGOT + LoRa Long-Range Modules + Coral USB Accelerator + FY-G3 Gimbal.

　　Programming Tools: ROS (Robot Operating System) + Python (multi-agent communication).

　　Implementation Logic:

　　Task Division: USC robots (humanoid) interact with people; UBTECH robots (mobile) patrol outdoor areas, sharing data via LoRa.

　　Hazard Detection: Coral USB Accelerator speeds up fire/smoke recognition (via pre-trained YOLO model) from gimbal camera feeds.

　　Collaborative Navigation: Use ROS to coordinate robot paths, avoiding collisions and ensuring full campus coverage.

　　Emergency Response: When a hazard is detected, robots send alerts to the school’s security system and guide students to safety.

　　Interdisciplinary Value: Combines multi-agent systems (robot collaboration), computer vision (hazard detection), and public safety (campus security).

　　Teaching Implementation Suggestions

　　Leveled Guidance:

　　Primary students focus on graphical programming (Scratch/Mixly) and sensor data collection;

　　Senior high students deepen into Python/OpenCV and algorithm optimization;

　　University teams tackle custom model training and ROS-based multi-robot development.

　　Open-Source Resource Utilization:

　　Use official SDKs (e.g., FoloToy MicroPython Library, USC ROS Package) for faster development;

　　Reference community projects (e.g., TensorFlow Education Forum, Arduino Project Hub) for code templates.

　　Safety & Scalability:

　　For outdoor projects (e.g., navigation robots), set safety boundaries and use low-voltage power supplies;

　　Design projects with modular upgrades (e.g., add AI accelerators to entry-level toys for advanced practice).

　　Assessment Focus:

　　Evaluate not only functional realization but also creativity (e.g., custom features), code readability, and interdisciplinary integration.