Today in class we wired a servo and used Arduino to program the servo.

The video to the left is a video of the servo motor working.

In class we used an application called Tinkercad to show the wiring of our servo motor we wired last class. The picture to the left is a diagram of the wiring in Tinkercad

In class, I learned how to code two separate servos to move independently using Arduino. To do this I used the same code from my first moving servo and copied some of the code to then change some of it. The video to the left is the two servos moving independently.

In class, I updated my last servo wiring on Tinkercad.  I added four battery packs with wiring to my Tinkercad circuit. The picture to the left is the updated circuit.

In class, I wired four battery packs to my Arduino Uno and breadboard. Then I put two batteries in each of the battery packs to give the breadboard six volts and the Arduino Uno six volts. I also did this so it could work without the laptop and so it would be easier to move my robot around. To the left is a video of it working.

In class, we put our servo and battery on cardboard to make the base for our prototype robot. I used hot glue and tape to do this. After I finished I made a code for my robot to move forward and backward. A video to the left is the code working.

In class, I made by robot move forward, right, left, and backward. To do this I updated my last code and uploaded it into my robot using Arduino.  The video to the left is my robot moving forward, back, left, and right.

In class I had my robot follow a track, and to do this I used Arduino to code my robot.  While I was using Arduino to code my robot I faced many challenges and had to keep changing my code. The video to left is my robot completed the mini track.

In class, I learned how to power my hobby motors using a diagram for the wiring. To the left is a video of the hobby motors working.

In class, I made my second robot base build. This robot uses my hobby motors instead of the servo motors. To the left is a picture of my second robot base build.

Today, I wired a ultrasonic sensor to my servo motor robot.  I used a guide from my teacher to code it and to wire it. A video to the left is the ultrasonic sensor working.

In class, I used an app called dabble to connect to the robot. Before I could use dabble, I had to combine 

Some advantages of my robot are the front support and how hard it can be to move it.  A disadvantage was the back of my robot struggling to push other robots meaning I will make the ramp less steep. To be advanced I will add a neopixel and a mini servo with an axe. 

To the right is a google slide of my collage of photos of Dog houses, Kitty condos, and modern homes. There are also the collages of my group members.

In class, After we finished the research we had to make four sketches choosing from the 30 image collage.  To the left are the sketches of the four images I chose.

Today in class my teacher taught me how to scale a perspective drawing. First, I drew the dog house and made it 3d using the steps of perspective drawing. Then we put the real life measurement for the height, length, and width. After that we put the measurements for each piece and lastly we converted the inches into 1:8 scale so its scaled to the drawing.

To the left is my detailed sketch that represent the dog house Im going to build. It has the measurements of the real life dog house and the requirements to reach this was to have an inside space of at leas\

\t 24in x  30inx 24in.   

In class I used onshape to create pieces for my prototype. I used the real life measurements on my sketch and then after I finished all of the pieces I added them onto one drawing and sized it to 1:8 so it matched the scale I used. To the left is a picture of all of the pieces I made that were used for my box.

After creating all of my onshape pieces on different sketches I added them to glowforge so I could cut them out using a laser printer. I used a cereal box to represent the 1:8" of the 1/2" thick plywood. To the left are all of the pieces glued together using Elmer's glue.

After finishing my first prototype I went over it went my teacher and we came up with ways to make my dog house better. The ways that we came up with are to engrave the wood on the front, make the bottom piece less lengthy, and too add structural support in each corner. The first image to the left is what we came up with on paper.  Once we finished I had to make my second prototype on Onshape but it had to be 3d and have the ideas implemented on the prototype. The second image to the left is my 3d Onshape prototype.

After making the 3d model on Onshape, I took the pieces from the assembly and put them onto a drawing with a 1:8 scale. I downloaded the drawing as an SVG so I could add it to Glowforge. Once I added it to Glowforge I cut out the pieces using their laser cutter. Lastly, I glued the pieces together to get the prototype that is shown to the left. 

In class, I got my Neopixel matrix to light up. I first wired this neopixel matrix to my adafruit feather.  I used mu editor to code the neopixel matrix with code provided from my teacher.

In class, I coded the neopixel matrix to show a smiley face using mu editor.  I used indexing in my code so it would match up with the picture above thatmy teacher provided.

The goal of my model is to correctly determine whose student image is shown. The data used to train the model are the images of the students from my google drive. The model predicts who the person in the image shown is. My model predicts the person in the image correctly 100% of the time.

Loops repeat code a certain amount of times and `functions An image from the perspective of a computer is numbers and pixels. Mancine learning is when a machine develops the behavior of human intellegence. Training a model is teaching the model with data and building a model is creating the outline for the model so it can predict.

Data structures are used to organize and store data. Loops repeat code a certain amount of times and functions are a set of code that runs when called. An image from the perspective of a computer is numbers and pixels. Mancine learning is when a machine develops the behavior of human intelligence. Training a model is teaching the model with data and building a model is creating the outline for the model so it can predict. In the picture to the left my MLM is wrongly predicting who is the image based on the code provided.