Engineering Appllications

Skill Development

We are going to learn the above four things to be able to do our semester 2 project!

Independence

This year Ms. White is helping us develop skills so that we can choose a project to solve later.

Robotic Arm

Our robotic arm is to help us build skills to do our semester 2 project!

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Robotic Arm Base

In class today we got out robotic arm base to do a 0 to 180 degree turn.

Robotic Arm Claw Build

Today I fully built my robotic arm claw out of wood that was laser cutted.

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Left Servo Calibrated

I built my arm claws to function reliably and also calibrated my left arm.

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Right Servo Calibrated

Today we calibrated our servos and here is my right servo.

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Arm claw

I successfully built my claw to pick up things and installed it on my build.

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Full Mobility

I got my robot to be fully functional and grab and move an object left to right using full mobility.

Here is my code for my Robot Coding Quiz!!!

#include <Servo.h>

Servo baseservo; // create servo object to control a servo

Servo rightservo; // create servo object to control a servo

Servo leftservo;

Servo clawservo;

// twelve servo objects can be created on most boards

void setup() {

baseservo.attach(9); // attaches the servo on pin 9 to the servo object

leftservo.attach(6);

rightservo.attach(11)

; clawservo.attach(10)

;}

void loop() {

baseservo.write(90); // tell servo to go to position in variable 'pos'

delay(1000);

rightservo.write(0); // tell servo to go to position in variable 'pos'

delay(2000);

leftservo.write(80); // tell servo to go to position in variable 'pos'

delay(2000);

rightservo.write(90); // tell servo to go to position in variable 'pos'

delay(4000);

clawservo.write(0); // tell servo to go to position in variable 'pos'

delay(1000);

clawservo.write(0); // tell servo to go to position in variable 'pos'

delay(1000);

leftservo.write(110); // tell servo to go to position in variable 'pos'

delay(3000);

rightservo.write(0); // tell servo to go to position in variable 'pos'

delay(3000);

baseservo.write(0); // tell servo to go to position in variable 'pos'

delay(3000);

rightservo.write(0); // tell servo to go to position in variable 'pos'

delay(2000);

leftservo.write(110); // tell servo to go to position in variable 'pos'

delay(2000);

rightservo.write(90); // tell servo to go to position in variable 'pos'

delay(4000);

clawservo.write(180); // tell servo to go to position in variable 'pos'

delay(2000);

rightservo.write(0); // tell servo to go to position in variable 'pos'

delay(1000);

baseservo.write(90); // tell servo to go to position in variable 'pos'

delay(1000);

leftservo.write(90); // tell servo to go to position in variable 'pos'

delay(10000);

// leftservo.write(); // tell servo to go to position in variable 'pos'

// delay(1000);

// leftservo.write(0); // tell servo to go to position in variable 'pos'

// delay(3000);

; // waits 15ms for the servo to reach the position

}

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RGB Sensor Install

Today in class we in installed our RGB sensor into our Arduino's because we are going to be working on color sorting in the near future.

Research

Here is some research I did on our 2 week project on an prototype skittle sorting machine. I found the middle robot the most interesting because it was built out of cardboard like ours and also it is the easy and a good build to get inspiration from.

Idea Development

Today me and my partner Ben drew out how we wanted are prototype to be. On the top there is a tube that will drop the skittles and the put on the servo where there is a claw that then goes to the sensor and the sensor reads and the bottom servo moves depending on the color.

DAY 1

Today me and my partner Ben started cutting out the pieces for are build and testing tube ideas and see how they would function.

DAY 2

Today I worked on assembling the pieces and also testing the servo and how it would function on the first layer. Also we cut out the hole for our tube up top and installed it.

DAY 3

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Today we got most of are colors able to sensed by our color sensor and to react based off the color.

DAY 4

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Today we built the ramp for our next layer and also got the servo to be able to drop several skittles down the hole onto the ramp.

DAY 5

Today we cut out and made a place where the sensor can rest and read the skittles.

DAY 6

Today we glued down our ramp so it would be sort of diagonal so the skittles could drop in the boxes easily. Also we glued our breadboard on the back and made little holes so the wires could go in and out.

DAY 7

Today I built boxes and walls for our build. Also my partner taped the disk with tape so we could get better readings with our colors.

DAY 8

Today we built a new funnel since our previous funnel wasn't acting reliably. Also we covered the disc in white paper to get better readings with our skittles and I finished adding the rest of the walls.

DAY 9

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Today we ran our first run with multiple skittles in it. We had few errors with sensing but majority of the time it senses right..

DAY 10

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Today we tested again we had some problems with the funnel and purple again.

Final Skittle Sorter Prototype

Today was the day of our demo we didn't have consistent reads for our colors coming towards the end of are project. Green and yellow were are most reliable colors with reading but Purple, red, and green gave us problems. All in all we put forth a good effort to get our project done since it was a very challenging task.

Recycling Research

In this sorter it uses magnets to separate the aluminum from the steel.

This one I found interesting because it had a presort function where humans picked it out first.

In this robot the robotic arms quickly pick out materials that can be used for recycling.

This one is very similiar to the 3rd but the guy explains it to make it make sense. This robot also uses machine learning to do certain task and wrap up production.

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Stepper Motor

Today we got our stepper motors to work. I had to troubleshoot mines a lot but it ended up working.

Final Machine Learning Model

What is Machine Learning?

Machine Learning is a form of AI that allows software to learn and better predict outcomes without being specifically programmed to do so.

Ben

Kaden

Olivia

Byron

The goal of my model?

The goal of my model was to be able predict between the 4 faces I was using at a 90% or greater accuracy.

Ben

Kaden

Olivia

Byron

My Teacher produced 50 low resolution images for each of the 4 people I used for my data. In my model each of the images have a pixel size of 48.

Google Colaboratory

<--------Final Model Link

To the left is my final model. My model is fairly simple. My model is reading and trying to determine between 4 different faces. My model's accuracy was both 100 on training and testing dataset.

Building the model

In this section I imported the keras model for the training database. Then I created a sigmoid alignment function as an input called layer. Then created a second layer "layer2" that works depending on "layer". Then the model outputs a value based on "layer2". At the end of this section it prints out the summary of my model including parameter and shape of each layer.

Accurately labeled images

Here are my Accurately labeled images as you can see it has a prediction of what the image will be then it basically tells the computer if it is true or false by saying what the image actually is.

Mislabeled images

Unfortunately I didn't have any mislabeled images because my accuracy was 100%.

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