Honor’s Robot phone is real, and coming later this year

Introduction

In this tutorial, you'll learn how to create a basic robotic phone interface using Python and a Raspberry Pi. This hands-on project will teach you the fundamentals of controlling motors, sensors, and displays to create simple robotic behaviors like nodding, dancing, and face tracking. While the Honor robot phone features advanced AI and robotics, this tutorial focuses on the core concepts that make such devices possible.

Prerequisites

• A Raspberry Pi (any model with GPIO pins)
• Basic Python knowledge
• Access to a breadboard and jumper wires
• Small servo motors (2-3 for head movement)
• LED lights or display module
• Camera module or USB webcam
• Power supply for the Raspberry Pi

Step-by-step instructions

Step 1: Set up Your Raspberry Pi

First, ensure your Raspberry Pi is running the latest Raspberry Pi OS. Update your system with these commands:

sudo apt update
sudo apt upgrade

This ensures you have the latest packages for hardware control. We'll use Python 3, which is pre-installed on Raspberry Pi OS.

Step 2: Install Required Libraries

Install the necessary libraries for controlling motors and working with the camera:

sudo apt install python3-rpi.gpio
pip3 install opencv-python
pip3 install imutils

The RPi.GPIO library allows us to control the physical pins, OpenCV handles computer vision tasks, and imutils provides useful image processing functions.

Step 3: Connect Your Servo Motors

Connect your servo motors to the Raspberry Pi GPIO pins:

• Red wire (power) to 5V pin
• Brown wire (ground) to ground pin
• Orange wire (signal) to GPIO pin 18

For multiple servos, use different GPIO pins (12, 13, etc.). This setup allows you to control head movement, similar to how the Honor phone might nod.

Step 4: Create Basic Motor Control

Create a Python script to control the servo motors:

import RPi.GPIO as GPIO
import time

# Set up GPIO
GPIO.setmode(GPIO.BCM)
GPIO.setup(18, GPIO.OUT)

# Create PWM signal
pwm = GPIO.PWM(18, 50)  # 50Hz frequency
pwm.start(0)

def set_angle(angle):
    duty = angle / 18 + 2
    GPIO.output(18, True)
    pwm.ChangeDutyCycle(duty)
    time.sleep(1)
    GPIO.output(18, False)
    pwm.ChangeDutyCycle(0)

# Test the motor
set_angle(90)  # Turn to center
set_angle(0)   # Turn left
set_angle(180) # Turn right

pwm.stop()
GPIO.cleanup()

This code creates a basic servo controller. The PWM (Pulse Width Modulation) signal controls the motor position. The duty cycle determines the angle, with 2% for 0° and 12% for 180°.

Step 5: Add Camera Functionality

Now add face detection using OpenCV:

import cv2
import numpy as np

# Load the pre-trained face detector
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')

# Initialize camera
cap = cv2.VideoCapture(0)

while True:
    ret, frame = cap.read()
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray, 1.3, 5)
    
    for (x, y, w, h) in faces:
        cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2)
        
    cv2.imshow('frame', frame)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

This code captures video from the camera and detects faces. It's the foundation of how the Honor phone tracks faces - identifying human presence in its field of view.

Step 6: Combine Motor and Camera Control

Integrate the face tracking with motor control:

import cv2
import numpy as np
import RPi.GPIO as GPIO
import time

# Set up GPIO
GPIO.setmode(GPIO.BCM)
GPIO.setup(18, GPIO.OUT)
pwm = GPIO.PWM(18, 50)
pwm.start(0)

face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
cap = cv2.VideoCapture(0)

# Track face position
face_x = 0

while True:
    ret, frame = cap.read()
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray, 1.3, 5)
    
    if len(faces) > 0:
        x, y, w, h = faces[0]
        face_x = x + w/2
        
        # Move motor based on face position
        if face_x < 200:
            set_angle(0)  # Turn left
        elif face_x > 400:
            set_angle(180)  # Turn right
        else:
            set_angle(90)  # Center
        
        cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2)
        
    cv2.imshow('frame', frame)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

pwm.stop()
GPIO.cleanup()
cap.release()
cv2.destroyAllWindows()

This combined code creates the face-tracking behavior similar to the Honor phone's robot features. The motor adjusts based on where the face is located in the camera frame.

Step 7: Add LED Display

Add visual feedback using LED lights:

import RPi.GPIO as GPIO
import time

GPIO.setmode(GPIO.BCM)
GPIO.setup(24, GPIO.OUT)  # LED pin

# Blink LED when face is detected
while True:
    # Your face detection code here
    if face_detected:
        GPIO.output(24, GPIO.HIGH)
        time.sleep(0.5)
        GPIO.output(24, GPIO.LOW)
        time.sleep(0.5)
    else:
        GPIO.output(24, GPIO.LOW)

The LED provides visual feedback, indicating when the robot phone is active and tracking faces.

Step 8: Test and Refine

Run your complete program and observe the robot's behavior. Adjust the motor positions and sensitivity to get smooth, natural movements. The goal is to create a responsive system that mimics the Honor phone's robotic gestures.

Summary

This tutorial taught you how to build a basic robotic phone interface using a Raspberry Pi. You learned to control servo motors for head movement, implement face detection with OpenCV, and add visual feedback with LEDs. While this is a simplified version of the Honor phone's technology, it demonstrates the core principles behind robotic devices that can nod, dance, and track faces. The skills you've learned form the foundation for more advanced robotics projects and AI applications.