How to Build a Maze-Solving Algorithm for Any Basic STEM Robot

• Sensors: To detect walls without touching, use infrared or ultrasonic. Touch sensors are easier to use but not as accurate.
• Algorithms: Start with wall following to learn the basics. Move on to mapping later if you want your robot to be faster and smarter.
• Programming: Use conditional logic in Python or Scratch for easy implementation.
• Challenges: Dead ends and loops can trap basic setups, so incorporate memory variables.
• Applications: Skills apply to real-world autonomous navigation, like in Micromouse competitions.

Common Problems and Tips

Why Maze Solving is the "Hello World" of Autonomous Robotics

The Hardware Foundation: Essential Sensors for Navigation

Choosing Between Ultrasonic, Infrared, and Touch Sensors

Threshold Calibration: Teaching Your Robot to Recognize a Wall

1. Place the robot facing a wall at varying distances.
2. Read sensor values in code (e.g., Python with libraries like RPi.GPIO for Raspberry Pi bots).
3. Average multiple readings to reduce noise—take 5-10 samples.
4. Set threshold: if distance < 15cm, treat as wall; adjust based on maze size.

import time
import RPi.GPIO as GPIO

# Setup pins
TRIG = 23
ECHO = 24
GPIO.setmode(GPIO.BCM)
GPIO.setup(TRIG, GPIO.OUT)
GPIO.setup(ECHO, GPIO.IN)

def get_distance():
    GPIO.output(TRIG, True)
    time.sleep(0.00001)
    GPIO.output(TRIG, False)
    
    while GPIO.input(ECHO) == 0:
        start = time.time()
    while GPIO.input(ECHO) == 1:
        end = time.time()
        
    duration = end - start
    # Distance = (time * speed of sound) / 2
    distance = duration * 17150  
    return distance

threshold = 15  # cm
if get_distance() < threshold:
    # Wall detected, turn
    print("Wall detected! Turning...")

The "Wall Follower" Algorithm: Your First Success

Understanding the Left-Hand or Right-Hand Rule

• Pros: No memory needed; simple code.
• Cons: Inefficient in loopy mazes.

Coding the Conditional Logic: The "If-Then-Else" Structure

• Use blocks: "If <front sensor detects wall> then turn left"
• Add: "Else if <right sensor no wall> then turn right"
• Else: move forward

• Forever loop
• If front wall: turn left 90 degrees
• Else if right open: turn right 90
• Else: move 10 steps

while True:
    # Read distance from sensors
    front = get_front_distance()
    right = get_right_distance()
    
    if front < threshold:
        # Obstacle ahead, must turn
        turn_left()
    elif right > threshold:  
        # Right side is open, follow the wall
        turn_right()
    else:
        # Path is clear
        move_forward()

Beyond Simple Mazes: Handling Dead Ends and Loops

The Problem with Island Mazes and Infinite Loops

Implementing a Simple Path-Memory Variable

path_memory = []  # List of visited coordinates
current_pos = (0, 0)

if current_pos in path_memory and is_dead_end():
    # Robot recognizes this path and returns
    backtrack()
else:
    # Save the new position to map the maze
    path_memory.append(current_pos)

Advanced Maze Solving: Introduction to the Flood Fill Algorithm

Moving from Reactive Logic to Mapping

• Odometry: This uses motor sensors to track (x, y) coordinates and current location.
• Grid Mapping: The robot records every path so it avoids going down the same dead end twice.

Assigning Values: How the Robot Calculates the Shortest Path

• Mark the Goal: The finish line is always 0.
• Propagate Values: Every neighbor square to the goal becomes a 1. The neighbors of those squares become a 2, and so on.
• Choose the Path: When the robot stands in any square, it looks at the numbers around it and moves toward the lowest value.

# Initialize maze with "infinity" distances
maze = [[float('inf')] * width for _ in range(height)]
maze[exit_y][exit_x] = 0
queue = [(exit_x, exit_y)]

while queue:
    x, y = queue.pop(0)
    for dx, dy in directions:
        nx, ny = x + dx, y + dy
        # Update distance if a shorter path is found
        if valid(nx, ny) and maze[ny][nx] > maze[y][x] + 1:
            maze[ny][nx] = maze[y][x] + 1
            queue.append((nx, ny))

Tuning and Tweaking: Boosting Your Robot’s Speed

• Smooth Out Turns: Stop using jerky "stop and spin" moves. Program "arc turns" instead. This helps the robot keep its speed and prevents sudden shaking.
• Check Your Data: Look at your sensor logs. If the numbers jump around wildly, add a simple filter to average the readings. This stops the robot from twitching.
• Fix Loose Parts: Shaking makes your robot less accurate. Check that every screw is tight and the wheels are steady. A solid robot is a precise robot.

Handling "Sensor Noise" and Erratic Movements

1. Collect a Sample: Take five quick readings in a row.
2. Calculate the Mean: Add them together and divide by five.
3. Smooth Action: Only move the robot based on that average number.

Speed vs. Accuracy: Finding the Sweet Spot for Motor Control

• 50% Power for Turns: Slower turns are more accurate and keep the robot from spinning out of control.
• 80% Power for Straights: You can speed up on long, flat paths where the robot doesn't need to make quick decisions.

Conclusion: Applying Maze Logic to Real-World Engineering