[BlocksExtension]

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Set PID Line Following Parameter

Description

Proportional, Integral, and Derivative (PID) functions are used to improve the feedback (analog inputs from the IR Sensors) to improve the robot’s movement.

1. Proportional (P) :
This component enables the robot to make immediate, precise adjustments based on its distance from the line. If the robot drifts too far to the right, it will steer left, and if it veers too far to the left, it will steer right to correct its course.

2. Integral (I) :
The integral component monitors how long the robot has been off the line. If the deviation persists for an extended time, this part applies a larger correction to bring the robot back on track more effectively.

3. Derivative (D) :
The derivative component anticipates future errors by analyzing the rate at which the robot drifts from the line. This ensures smooth, predictive adjustments, helping to prevent overshooting and maintain stability.

The control output is given by:
Control Output = Proportional + Integral + Derivative

So, the PID controller combines these three components to continuously adjust the robot’s movements, keeping it as close to the line as possible while moving smoothly and fast. A robot is always learning and fine-tuning its path to stay on track.
Think of it as a smart system that balances and corrects itself as it moves, ensuring it follows the line accurately. PID line followers are commonly used in robotics competitions and educational settings to teach about control systems and automation.

Note: If you initialize a Three IR line following, use Kp (propositional constant) >= 7 for a better result.

How does PID work in the Quarky Line Following?
In Quarky’s “Do Line Following” Block/Python function, the system employs its two Infrared (IR) sensors, one on the left and another on the right, to navigate along a line.
Let’s say the analog values registered by these sensors
on a white surface are
Left = 150
Right = 170

while on the black line, they read
Left = 820
Right = 750.

When the robot is shifted to the right, the left sensor is on the black line and the right one is on the white. At this point the reading of IR Sensors is
Left = 820
Right = 170,
The error is calculated as follows:

Error = (Left Sensor Value – Right Sensor Value)/10
= (820 – 170)/10
= 65

Proportional Only
For the PID (Proportional-Integral-Derivative) controller, the constants are set as follows:
Kp = 0.5, Ki = 0, and Kd = 0.

The proportional term is then calculated.
Proportional = K_p *Error
= 0.5* 65
= 32 (approximately)

The control output is given by:
Control Output = Proportional + Integral + Derivative

In this case, both the integral and derivative terms are set to 0, so the control output simplifies to:
Control Output = 32 + 0 + 0
= 32

Subsequently, the motor speeds are adjusted based on the control output and the motor speed parameters.

Assuming base speed = 40, minimum speed = 0, and maximum speed = 80
The Left and Right motor speeds are computed as follows:

Left Motor Speed = Base Speed – Control Output
= 40 – 32
= 8

Right Motor Speed = Base Speed + Control Output
= 40 + 32
= 72

Proportional and Integral only
The integral term in the PID (Proportional-Integral-Derivative) controller addresses the accumulated past errors over time. If there has been a persistent error over time, the integral term gradually increases, helping to eliminate the accumulated error.

In a scenario where the robot is stuck and the robot’s tires are slipping due to the excessive weight of the robot and low battery, both sensors are placed on contrasting surfaces – the Left sensor on black and the Right sensor on white. Despite the challenging conditions, the proportional constant (Kp) comes into play, initially adjusting the motor speeds. As a result, the Left motor speed becomes 8, and the Right motor speed reaches 72.

When an integral term with a constant value (Ki = 0.01) is introduced into the control equation, the integral value is updated in each iteration:

I = I + error
I = 0 + 65 = 65

The control output, comprising proportional, integral, and derivative terms, is then computed as follows:
Control Output = Proportional +Integral + Derivative
= (0.5 * 65) + (0.01 * 65) + 0
= 32.65
Left Motor Speed = 7.35
Right Motor Speed = 72.65

In the subsequent loop, the integral term is updated again:
I = I + error
I = 65 + 65 = 130

And the control output in the next iteration becomes:
Control Output = (0.5 * 65) + (0.01 * 130) + 0
= 33.3
Left Motor Speed = 6.7
Right Motor Speed = 73.3

Proportional, Integral, and Derivative
Derivative (D): The derivative term in the PID (Proportional-Integral-Derivative) controller anticipates future errors by assessing how fast the error is changing. It plays a crucial role in preventing overshooting or oscillations by slowing down the control action as the system approaches the setpoint.

In a small arena, when a robot needs to execute a significant turn or a U-turn, increasing the proportional constant (Kp) may result in excessive oscillations. To address this, the derivative term is introduced to control oscillations and overshooting.

Consider a scenario where the error is increased:
Error = (Left Sensor Value – Right Sensor Value)/10
=(930 – 170)/10
= 76

The derivative term is computed as:
D = Previous Error – Error
= 65 – 76
= -11

The control output, including proportional, integral, and derivative terms, is then calculated as follows:
Kp = 0.5, Ki = 0.01, and Kd = 0.2

Control Output = Proportional + Integral + Derivative
= (0.5 * 76) + (0.01 * 76) + (0.2 * -11)
= 38 + 0.76 – 2.2 = 36.56

Subsequently, the left and right motor speeds are adjusted:
Left Motor Speed = 3.44
Right Motor Speed = 76.56

In the subsequent loop, the integral term is updated, and the process repeats with a new error:
Error = (600 – 170)/10
= 43

The integral term is updated:
I = I + Error
= 76 + 43
= 109

The derivative term for the new error is computed:
D = Previous Error – Error
= 76 – 43
= 33

The control output remains the same in this loop:
Control Output = (0.5 * 43) + (0.01 * 109) + (0.2 * 33)
= 21.5 + 1.09 + 6.6
=29.19

Left Motor Speed = 10.81
Right Motor Speed = 69.19

This iterative process continues, with the derivative term helping to manage the robot’s response to changing errors, ultimately enhancing its stability.

Example

Mouse Tracking and Stamping – Python

The example demonstrates how to make the sprite track and stamp its image on the mouse when the space key is pressed in Python.

Code

sprite = Sprite('Tobi')
pen = Pen()

pen.clear()

while True:
  if (sprite.iskeypressed("space")):
    sprite.setx(sprite.mousex())
    sprite.sety(sprite.mousey())
    pen.stamp()

Output

Beetle Movement – Python

The example demonstrates using key sensing to control the sprite's movement in Python.

Code

sprite = Sprite('Beetle')

sprite.setdirection(90)
sprite.gotoxy(0, 0)

while True:
  if sprite.iskeypressed('up arrow'):
    sprite.move(3)
  if sprite.iskeypressed('down arrow'):
    sprite.move(-3)
  if sprite.iskeypressed('left arrow'):
    sprite.left(3)
  if sprite.iskeypressed('right arrow'):
    sprite.right(3)

Output

Ball Movement – Python

The example demonstrates how to add movement to a sprite.

Code

sprite = Sprite('Ball')

while True:
  if sprite.iskeypressed('up arrow'):
    sprite.changey(3)
  if sprite.iskeypressed('down arrow'):
    sprite.changey(-3)
  if sprite.iskeypressed('left arrow'):
    sprite.changex(-3)
  if sprite.iskeypressed('right arrow'):
    sprite.changex(3)

Sprite Direction – Python

The example demonstrates the sprite direction in Python.

Code

sprite = Sprite('Arrow1')

sprite.gotoxy(0, 0)
sprite.setsize(300)

while True:
  if sprite.iskeypressed("left arrow"):
    sprite.left(3)
  if sprite.iskeypressed("right arrow"):
    sprite.right(3)
  sprite.say("Direction is " + str(sprite.direction()))

Output

Mouse Tracker – Python

The example demonstrates how to implement mouse tracking in Python.

Code

sprite = Sprite('Tobi')

while True:
  if (sprite.iskeypressed("space")):
    sprite.goto("_mouse_")

Output

Walking Tobi – Python

The example demonstrates the wall bouncing of the sprite, rotation style, and costume change in Python.

Code

sprite = Sprite('Tobi')
import time

sprite.setrotationstyle('left-right')

while True:
  sprite.move(5)
  sprite.bounceonedge()
  sprite.switchcostume("Tobi walking 1")
  time.sleep(0.1)
  
  sprite.move(5)
  sprite.bounceonedge()
  sprite.switchcostume("Tobi walking 2")
  time.sleep(0.1)

Output

Follow the Mouse – Python

The example demonstrates how to make the sprite follow the mouse in Python.

Code

sprite = Sprite('Tobi')

while True:
  sprite.pointto()
  sprite.move(3)

Output

Bouncing Ball with Gravity in Python

The example demonstrates how to add gravity to the project on a bouncing ball.

Code

sprite = Sprite('Ball')
import time
import random

sprite.setx(random.randrange(-200, 200))
sprite.sety(random.randrange(-100, 140))
gravity = -2
xpos = sprite.x()
ypos = sprite.y()
dx = random.randrange(-50, 50)
dy = random.randrange(-25, 25)

while True:
  dy = dy + gravity
  dx = dx * 0.98
  dy = dy * 0.98
  xpos = xpos + dx
  ypos = ypos + dy

  if ypos < -160:
    dy = -dy
    ypos = -160
  if ypos > 130:
    dy = -dy
    ypos = 130

  if xpos < -220:
    dx = -dx
    xpos = -220
  if xpos > 220:
    dx = -dx
    xpos = 220
  
  sprite.gotoxy(xpos, ypos)
  time.sleep(0.01)

Output

Bouncing an Object

The example demonstrates the bouncing of an object from a bird's eye view in PictoBlox.

Script

Output

Change costume when Clicked

The example demonstrates the costume change and gliding to the random location when clicked.

Script

Output

Costumes in PictoBlox

The example demonstrates the costume change in PictoBlox.

Script

Output

Reciting Table

The example demonstrates how to use a repeat block to recite a table in PictoBlox.

Sprite

Output

Show and Hide

The example demonstrates the show and hides of the sprite using the buttons.

Script

Output

Sprite Looks Effects

The example demonstrates the various graphical effects in the PicotBlox - Brightness, color, fisheye, ghost, mosaic, pixelated, and whirl.

Color

Fisheye

Ghost

Mosaic

Brightness

Pixelate

Whirl

Thinking in PictoBlox

The example demonstrates how to use thinking sprite in a project.

Script

Output

Sprite Looks Effects – Python

The example demonstrates the various graphical effects in the Python - Brightness, color, fisheye, ghost, mosaic, pixelated, and whirl.

Code

sprite = Sprite('Tobi')
import time

effect = "COLOR"
# effect = "FISHEYE"
# effect = "WHIRL"
# effect = "PIXELATE"
# effect = "MOSAIC"
# effect = "BRIGHTNESS"
# effect = "GHOST"

sprite.gotoxy(0, 0)
sprite.setsize(200)
sprite.cleareffects()
sprite.seteffect(effect, 0)

while True:
  for i in range(10):
    sprite.changeeffect(effect, 5)
    time.sleep(0.1)
  for i in range(10):
    sprite.changeeffect(effect, -5)
    time.sleep(0.1)

Color

Fisheye

Ghost

Mosaic

Brightness

Pixelate

Whirl

Show and Hide – Python

The example demonstrates the show and hides of the sprite in Python.

Code

sprite = Sprite('Tobi')
import time

sprite.gotoxy(0, 0)
sprite.setsize(200)

while True:
  sprite.show()
  time.sleep(1)
  sprite.hide()
  time.sleep(1)

Say and Think with Sprite – Python

The example demonstrates how to use say and think of sprite in a project.

Code

sprite = Sprite('Tobi')
import time

sprite.say("Saying for 2 seconds", 2)
sprite.say("Saying forever")
time.sleep(2)

sprite.think("Thinking for 2 seconds", 2)
sprite.think("Thinking forever")

Output

Costumes in PictoBlox – Python

The example demonstrates the costume change in PictoBlox.

Code

sprite = Sprite('Tobi')

sprite.gotoxy(0, 0)
sprite.setsize(200)
sprite.switchcostume("Tobi")

while True:
  sprite.say(sprite.costume("name"), 1)
  sprite.nextcostume()

Output

Reciting Table – Python

The example demonstrates how to use a repeat block to recite a table in PictoBlox.

Code

sprite = Sprite('Tobi')

sprite.input("Which table you want to recite")
number = int(sprite.answer())

for i in range(1, 10):
  sprite.say(str(number) + " x " + str(i) + " = " + str(number*i), 1)

Output

Bouncing an Object – Python

The example demonstrates the bouncing of an object from a bird's eye view in PictoBlox.

Code

sprite = Sprite('Tobi')
import time

sprite.gotoxy(0, 0)
sprite.setsize(100)

while True:
  for i in range(10):
    sprite.changesize(sprite.size()/10)
    time.sleep(0.01)
  for i in range(10):
    sprite.changesize(- sprite.size()/10)
    time.sleep(0.01)

Output

Quarky LED Emotion

The example demonstrates the LED emotions of the Quarky.

Script

Output

Quarky LED Animation

The example demonstrates the various animation of the Quarky LED display.

Script

Output

Name Badge – Quarky

The example demonstrates how to display scrolling text to make a name badge.

Script

Output

Display Number 1 to 9 on Quarky

The example demonstrates how to implement a while loop running from 1 to 9 and show it on the Quarky display.

Script

Output

LED Looping

The example demonstrates how to control the individual LEDs of the Quarky and run patterns using the loops.

Script

Output

IR Sensor Calibration

The example demonstrates how to calibrate the IR sensors to detect black lines on the white surface.

Logic

An IR sensor consists of 2 LEDs: one which transmits the IR light and one which receives the IR light. When the IR rays are transmitted, they bounce from the nearest surface and get back to the receiver LED. That’s how an IR sensor detects an object.

But to detect colors, we depend on the number of rays the surface reflects:

The dark surface will absorb more IR rays and as a result, the receiver will get fewer IR rays.
White or shiny objects will absorb fewer IR rays and as a result, the receiver will get more IR rays.

We can get the sensor values in PictoBlox and based on that value we can estimate whether the surface is black or white.

If the sensor detects the black line, its output value is increased. This means that the sensor is active.
If it detects the white area, its output value decreases. This means that the sensor is inactive.

We will call the threshold value above which the sensor detects the black line. If the sensor value is less than the threshold, it means that the sensor hasn’t detected the line yet.

Alert: IR sensors DON’T work in sunlight. IR rays from the sun increase the overall threshold of the sensors therefore they stay active all time. A closed environment or nighttime is the place/time to work with your line following robot.

Script

Calibrating the IR Sensors

Now, run the script by clicking the green flag and bringing the black line of the track close to the IR sensor. One of the following three conditions will happen:

Your calibration value is HIGH: The black region will not be detected in this case. Reduce the calibration value.
Your calibration value is LOW: The white region will not be detected in this case. Therefore, increase the calibration value.
Your calibration value is OK. The white and black regions are detected accurately.

Now modify the script to add the detection value for the right IR sensor.

Output

Random LED Glow on Quarky

The example demonstrates how to create a random colored LED pattern on Quarky.

Script

Output

LED Control with Keys

The example demonstrates how to control the glowing LED using the keyboard keys.

Script

Output

Traffic Light on Quarky

The example displays how we can display a custom pattern on the matrix by making a script to display a Traffic Light.

Script

Output

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[PictoBloxExtension]

Set PID Line Following Parameter

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