In this project we had to raise a flag 90 degree autonomously. To accomplish this, I designed all three gears and make sure that their teeth match well with each other as well as making sure that the last gear had 4 times as many teeth as the driving gear to ensure that a full rotation of the driver gear will result in the last gear to rotate 90 degrees. I also wrote the code that ensured that the Stepper motor made a full rotation. We were able to successfully raise the flag to 90 degrees for one full rotation of the driver gear.

In this project we had to create a robot that can pie someone in the face autonomously while they are sitting at a table.  The robot had to be able to pie the person in the face at least 4 times in the span of 30 seconds. This was accomplished by Cading and Laser cutting a four-linkage mechanism that would be run continuously until we tell it to stop.  We used two stepper motors, two bridge and a raspberry pie. Our robot was able to successfully pie our TA right in the face with utmost precision in terms of where her face was. 

In this project we had to create a vehicle that would be able to follow a path line of any color of our choosing (Black, Red, Yellow, Green). This was accomplished through an intricate and very cool design for the vehicle inspired by racing cars powered by two stepper motors, two H-bridges, one sensor and a Raspberry Pi. The sensor was attached at the bottom of the vehicle to pick up the color and using PID controller we were able to make the vehicle follow the line of our choosing(red). Although not everything went exactly how we had hoped for the demonstration we are going to try to refine our line following vehicle since we only had one week to complete this project.

In this project we had to create a vehicle that would be able to follow a path line of any color of our choosing (Black, Red, Yellow, Green) but using a pie camera contrary to part 1 where we used a color sensor. This was accomplished through an intricate and very cool design for the vehicle inspired by racing cars powered by two stepper motors, two H-bridges, one pie camera and a raspberry pie. The camera was attached to the front of the vehicle facing down so that it can capture the path of the colored line. In our case it was blue.  In order to allow better lightning for the camera we 3d printed larger wheels for the back of the vehicle and designed and added a ball bearing to the front for balance and support. We were able to make the motors step using a threading library and by calculating the percentage of the color that the camera was capturing but we ran into so many issues. For example, the delay between each step was quite long and we were getting a lot more vibrations than steps and also when it the motors did run, they moved in opposite direction. I personally learned a ton though. 

Just as the name suggest, in this project we had to successfully navigate a maze controlling our create3 robot remotely.  We had to use ROSS to get access their set of software frameworks and used Air table to send in commands to move the create3 robot and Twist message type from ROSS to communicate from Air table to our script.   In Air table, we just had to worry about X-direction for the linear vector and the z-direction for the angular vector because we just wanted our Create3 to have the capability of moving forward backward and rotate around the z-axis to pivot. In order for us to control the Create3 Robot remotely, we designed and 3Dprinted a cellphone mount that would be our eyes through facetime, so that we can see the path of the maze and navigate accordingly. This project was a great success as we were able successfully navigate the maze remotely and independently.

In this project we had to navigate a given path based on the object that the robot recognized. Basically, we had to make an exact 90 degrees left, or right turn based on the object that the pie camera picked up 6 inches away.  Part of the challenge was that they revealed the path 10 min before we had to go so, we had to make sure that our code was well structured for unknow inputs and be able turn in the right direction and complete the path successfully. We took about 100 pictures for each object with our pie camera and trained our code using Teachable Machine with google. Due to the variety of the object size, we designed and 3d printed the mount that holds the pie camera with the feature of being adjusted based on the side of the of the object.  Our robot successfully completed the path that was given and played a song when it identified the last object found as part of doing something fun when the last object is found.