This past week Anthony and I have been at somewhat of a standstill. We have been trying to decide whether or not to buy a specific part that we found pre-made for our project. We made a power point on this problem and presented it to the class. Today we decided that we would find the parts online and build it in class.

Presentation: https://docs.google.com/presentation/d/1aGyVe19d4ezW4J1zq53dXzdaGQytKflFxPTR7Q4ivKc/edit#slide=id.g4cda35ee10_0_56

While we have been attempting to figure things out from a financial stand point, I have been working on the math for calculating the speed at which our steppers need to spin. We want a rate of approximately 1 cm/s for our ball, so I have been using some physics to figure out what angular speed the ball needs to be moving at in order to get the appropriate speed at each radius. I am still working on this problem, but I should expect some results within the next week.

Me and Anthony have made significant progress in our knowledge of arduino coding. We have hooked up two different stepper motors and programmed them to do different things on the same arduino. This is the core to our project. Now we plan on working on planning out and coding for a “reset” point so our stepper knows where it is.

I hope to soon get working on the hardware for a prototype so we can put our coding to good use. With the recent cancellation of school it has slowed our progress, but we are on our way to success!

On the previous week, Denver and I figured out how to operate a stepper motor and even make two stepper motors spin at the same time. We also figured out how to make the stepper motor “microstep” for precise movement.

For this week, we will be focusing more on the hardware aspects of the project and continue to develop our understanding of the stepper motor as it is a key component that drives the kinetic sand art project.

This past week I’ve spent learning how to control motors with Arduino. I completed the DC motor lab, finally figuring out how to make a motor’s speed increase incrementally with a potentiometer as well as toggle the direction of the motor while being able to turn the circuit on and off. I learned some new functions in the coding such as analogWrite, which unlike digitalWrite has more then 2 states and can apply partial voltage to a dedicated pin instead of being completely active. Another useful function was the serial.println, this allowed me to be able to troubleshoot my circuit by monitoring the state of certain components I was minupulating. The only other task i have left to do before i move on is the servo motor control, then I will be able to start on the wind chime project.

I have started progress on a program to recognize and identify faces in a live video stream that has knowledge of names and training data and can output the name of the face it recognizes in the live stream. There are a few methods available for facial recognition, including CNNs, Fisher Faces, and Eigenfaces. Convolutional Neural Networks (CNNs) have unprecedented accuracy, however they are computationally expensive and I will be unable to run it in real time on a video feed on my laptop. This leaves two other methods: Eigenfaces and Fisher Faces. In today’s standards, both methods are computationally cheap and should be able to run real time on my laptop.

I predict that the Fisher Faces method will be more successful than the Eigenfaces method because it is less sensitive to changes in lighting and focuses more on actual facial features than just shadows and lighting (see this blog post, section “Eigenfaces”, and also this paper). The Fisher Faces detection method is actually very similar to the Eigenfaces method. Both methods require that recognition input and training data consists of a forward facing face that is positioned, resized, and cropped to a uniform size.

I would like to avoid manually cropping, resizing and positioning each photo of training data, and also live video feed recognition will also require automatic input preprocessing (I can’t hire a human to draw boxes around human faces in real time sadly).

Cropping and resizing operations are fairly straight forward. Cropping can be done through a simple HAAR or LBF cascade that selects a bounding box around the detected faces (and these faces can be cropped from the boxes). Resizing operations should be fairly simple too, and require simply resizing an input image into the target output dimensions that should be constant across the data set (all input and training images should be a certain size).

The tricky part comes in with aligning the faces to the image. The training and input data requires the images to be aligned the same, which means that the eyes of a face from Image 1 should be at the same position as the eyes of face from Image 2. They might not be in exactly the same position, however they should be fairly close (within a few pixels). This might be easy to do if you force your subjects to align their face perfectly when collecting the training and input data, however that is not very applicable in the real world. In reality, it would be better for facial recognition to work on faces that are slightly turned or looking off in a direction.

This adds more strain on the facial recognition software as input images have to be automatically aligned, which is a daunting task because not only must a face be detected, but also the eyes and mouth and a general outline of the face. This type of recognition is known as “facial alignment”, “facial features detection” or “facial landmark detection” (source). Once the position and rotation of the eyes is detected, we can use simpler rotation and translation operations to center the face.

Thankfully OpenCV provides tools through their “contrib” modules to do facial landmark detection (the “Facemark” API contains the tools we need). To install the “contrib” modules, you must already have downloaded the OpenCV4 source code and also download the contrib modules source code from here. Please follow the installation instructions that are also listed in the README for the repository.

This concludes the basic theory and decision to utilize Fisher Faces recognition method instead of Eigenfaces as well as the setup needed for the project. I will continue posting updates on this project.

Today, Denver and I will be learning the ins and outs of a stepper motor as it will be a key component in the sand art project. After learning the basics and successfully operating a stepper motor, we will begin research on what GT 2 belt size we would like as our table will be based on the size of the belt. This is only the beginning and we have a lot more to do to see the end result of our project.

Over the past two weeks Ive learned how much you can accomplish using microprocessors to conrtol electronic circuits. Being able to program logic into a circuit and have it control and monitor events enables us to accomplish a lot more then we could otherwise. However there is a bit of a learning curve when it comes to coding, finishing the LED traffic light helped me gain a basic understanding of some program functions aswell as its limitations. Im still yet to complete the DC motor project partially due to the messed up schedule from the snow and as well as it takes me a bit longer to fully understand something then it may most people. Im looking forward to see what is yet to come for this class.

The first few weeks of Robotics were patchy and often shortened due to weather and school scheduling, but we managed to complete the learning projects nonetheless. The goal of these projects was to prepare us for our own personal final project; in our case, the tennis ball cannon. In order, we had to make a flashing traffic stop, attach a crosswalk button to the traffic stop, make a motor that is speed controlled by a potentiometer, then change direction and speed. In the last of the generic class-wide projects, we controlled the position of a servo with a potentiometer. In the near future, we will learn how to use stepper motors and accelerometers.