Autobotic STEM Education in collaboration with Makerspace@UM

Project 1: AI-Based Object Detection and Sorting Robot

Get ready to build your very own robot! You'll design a cool machine that can "see" objects with a camera and sort them into different bins using a conveyor belt or robotic arm. The robot’s brain? An AI model that you'll train to recognize things like shapes, colors, or items using awesome pre-trained machine learning models!

Key Learning Concepts:

• Image Processing: Learn how to use a camera to capture images and send them to a machine learning model for object detection.

• AI/ML Basics: Introduction to pre-trained AI models (like MobileNet or YOLO) for object recognition.

• Motor Control: Use GPIO pins to control a conveyor belt or robotic arm, sorting objects based on the AI's decision.

• Automation: Explore how AI can automate tasks like sorting objects in warehouses.

 

Project 2: AI Voice Assistant with Natural Language Processing (NLP)

Get ready to create your own voice-activated assistant using the OiviO Pi kit and a microphone! Your assistant will be able to understand cool commands like “Turn on the lights,” “Play music,” or “What’s the weather?” By using AI and natural language processing, it’ll listen to your voice and control devices through GPIO pins. How awesome is that?

Key Learning Concepts:

• Natural Language Processing (NLP): Learn how to use NLP to recognize and interpret voice commands.

• Speech Recognition: Use speech-to-text libraries like Google Speech API or Vosk to capture and analyze voice input.

• Voice-Activated Control: Control connected devices (like LEDs or motors) based on voice commands.

• AI-Powered Assistants: Understand how virtual assistants like Siri or Alexa process and execute voice commands.

 

Project 3: AI-Powered Smart Home System

Get ready to create your own AI-powered smart home system that gets smarter the more you use it! You'll work with sensors like motion detectors, temperature sensors, and light detectors to automate things like lighting, heating, and even security. The best part? Your system will learn your preferences over time, using AI to save energy and make your home more comfortable and efficient.

Key Learning Concepts:

• AI in Smart Homes: Learn how AI can enhance the efficiency and automation of home systems.

• Data Collection and Analysis: Collect and analyze data from sensors to make predictions about user behavior.

• Machine Learning (ML): Introduction to basic ML algorithms to learn from sensor data and make smart decisions.

• Energy Efficiency: Understand how AI can optimize energy usage in smart homes.

 

Project 4: AI-Powered Gesture Recognition for Device Control

Key Learning Concepts:

• Computer Vision and AI: Learn how AI can be used to process video input for gesture recognition.

• Data Collection for AI: Capture gesture images or video and prepare them for training AI models.

• Classification Models: Use classification models to recognize different hand gestures.

• Human-Computer Interaction: Understand how AI enables new forms of interaction between humans and computers.

 

Project 5: AI-Powered Weather Station

Students will create an AI-powered weather station that collects temperature, humidity, and air quality data using sensors connected to the OiviO Pi kit. The system will use AI to predict future weather patterns based on collected data and display the results on an LCD screen.

Key Learning Concepts:

• Data Collection: Using sensors to gather real-world data.

• AI Predictive Models: Introduction to using machine learning models to predict future events.

• Data Visualization: Displaying data on an LCD screen or graphically.

• Hands-on Sensor Integration: Connecting temperature, humidity, and pressure sensors to the OiviO Pi kit.

 

Project 6: AI-Powered Face Detection Door Lock System

Students will build a face detection-based door lock system using a camera module and an AI model to recognize authorized faces. The system will unlock the door (or activate a motor) when it detects a known face and remain locked otherwise.

 

Key Learning Concepts:

• Computer Vision: Learn about facial recognition and object detection.

• AI for Security: Use AI to increase security and privacy in real-world applications.

• Python and OpenCV: Introduce the OpenCV library for image processing and AI-powered facial detection.

• Hands-on Motor Control: Use the OiviO Pi kit to control a motor for the door lock mechanism.

 

Project 7: AI-Based Emotion Detection System

Students will build an AI system that detects and analyzes emotions from facial expressions using a camera and pre-trained AI models. The system will analyze the face and determine if the person is happy, sad, surprised, or neutral, and display the result on an LCD or LED matrix.

Key Learning Concepts:

• Emotion Detection with AI: Understand how AI can be trained to analyze facial expressions.

• Real-time Processing: Learn how to process video feeds in real-time to detect and analyze emotions.

• Data Visualization: Use the OiviO Pi kit's LCD or LED matrix to visually represent the detected emotion.

• AI for Human-Computer Interaction: Explore how AI can be used to make technology more interactive and user-friendly.

 

Project 8: AI-Powered Smart Farming System

Students will build a smart farming system that uses sensors to monitor soil moisture, temperature, and light levels. An AI model will predict when plants need watering based on sensor data and automatically activate a water pump or irrigation system. 

Key Learning Concepts:

• AI in Agriculture: Learn how AI can be used to optimize farming and irrigation.

• Sensor Data Collection: Collect real-time environmental data using soil moisture and temperature sensors.

• AI for Predictive Maintenance: Use AI models to predict when plants need watering based on environmental conditions.

• Automation: Automatically control devices (e.g., water pumps) based on AI predictions.

 

Project 9: AI-Powered Traffic Light System Using Computer Vision

 

Students will create an AI-powered traffic light system that uses computer vision to detect the number of cars at an intersection. The system will adjust the traffic light timing based on the number of cars, helping reduce congestion.

Key Learning Concepts:

• AI for Traffic Management: Learn how AI can optimize traffic flow in cities.

• Object Detection: Use computer vision to detect and count cars.

• Real-Time Control: Implement real-time control of traffic lights based on the AI model’s output.