What happens when a Raspberry Pi can do more than process data? With the Physical AI with Raspberry Pi bundle, you can connect cameras, sensors, motors, a microphone and a speaker, then put them to work in projects ranging from hardware control to object tracking and voice interaction.

A Raspberry Pi, a camera and a few sensors already make a capable project platform. Add motors, audio, computer vision and object recognition, and things become considerably more interesting.

This is the idea behind Elektor's Physical AI with Raspberry Pi bundle, which combines Dogan Ibrahim's Physical AI with Raspberry Pi with the SunFounder AI Fusion Lab Kit. The book provides the projects and explanations, while the kit supplies the hardware needed to build them.
 
Physical AI with Raspberry Pi - Components
The kit components.

Start with the Electronics

The book begins with familiar electronics before moving into more advanced Raspberry Pi AI projects. Readers work with LEDs, buttons, relays, buzzers, sensors, displays and motors, learning GPIO control, PWM, analog-to-digital conversion and sensor interfacing along the way.

Projects include distance and temperature measurements, motor control, alarms and environmental sensing. These provide the hardware foundations for the voice and vision projects that follow.

Add a Camera, Voice, and Movement

The Fusion HAT+ adds motor control, ADC, and PWM channels, along with an onboard microphone and speaker. The kit also includes a camera and a two-axis pan-tilt mechanism, making it possible to combine software with physical movement.
 

Physical AI with Raspberry Pi Assembled Camera
Assembled camera pan-tilt.

The book covers taking photographs and video, camera previews and remote camera control before moving on to computer vision. On the audio side, projects include text-to-speech, speech recognition, a speaking clock and voice-controlled hardware.

There are also projects using online services as well as Ollama models running locally on suitable Raspberry Pi hardware.

From Computer Vision to Object Detection

The later chapters move into OpenCV and MediaPipe. Readers experiment with edge detection, face detection, hand tracking, and object detection.

One particularly visual project turns the user's fingertip into a virtual drawing pen. MediaPipe tracks the index fingertip, and OpenCV converts its movement into lines drawn on screen.
 

The figure shows the result of the virtual drawing project, with the fingertip drawing lines on a virtual canvas.

The final chapter introduces YOLO object detection. The Raspberry Pi camera can identify objects in real time, display bounding boxes and confidence values, and even use the pan-tilt mechanism to follow a selected target.

Readers can go a step further by training a custom YOLO model for objects relevant to their own application. The example in the book trains a model to recognize a cup, ending with the Raspberry Pi identifying it with a confidence level of 0.9.
 

Cup identified.

From a Blinking LED to Object Tracking

That progression is what makes Physical AI with Raspberry Pi interesting. You can begin with basic electronics and gradually move through sensors, motors, speech, and cameras before reaching real-time object detection and tracking.

The projects are also intended as starting points rather than fixed exercises. Readers are encouraged to modify parameters, combine ideas, and develop their own applications from the techniques covered in the book.

For anyone who prefers learning by wiring something up, running the code and seeing what happens on the workbench, the Physical AI with Raspberry Pi bundle provides plenty to experiment with.


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