Author: ppetraki

This week I was very busy in creating a functional system. I finalized the code this week which takes into account the two PIR sensors and the camera, in turn opening or closing the door. In addition, this required much wiring for all the power and GPIO pins. I also helped Jing out with getting the solenoid to work (the transistor we were using needed 5V and the GPIO pins output 3.3V).

My major accomplishment this week had to due with the motor and door. Previously, Irene was not capable of creating a functioning motor, which prevented us from having an automated door. Earlier this week, Jing and I decided to fix this as we believed this was a necessary aspect of our project, especially for the demo on Monday. We found a new DC motor that we believed rotated at a fast enough speed and with enough torque. We were able to get this motor to work within a couple of hours. On Saturday, I successfully mounted the motor onto the door, then found a way to use fishing wire to pull the door up! It takes approximately 8 seconds for the door to completely open and 3 seconds for the door to close. Although this was not my aspect of the project I was assigned to do, I was willing to work on it to create a more complete project that better showcased my efforts this semester.

(If you look closely there is clear fishing wire attached between the motor and door)

This past week I have been doing a lot of integration. First, I greatly modified my Python script to include the inputs from both PIR sensors, call the open and close door functions, and fully communicate with the App. I had all the individual components ready which made this part easier. The logic for this script is well thought out and a main goal is to not close the door on the cat!

I have also been trying to use Jing’s newest algorithm on the Jetson but it has encountered difficulties. It is not smoothly replacing the older one. This is because Jing added a new layer to his algorithm which the Tensorflow on the Jetson does not like for some reason. This will be my priority this upcoming week because the older algorithm is not good enough.

In addition, I also worked on the presentation slides which are due Sunday night.

This upcoming week is crunch time! I will be getting Jing’s newest algorithm working on the Jetson and finish fully integrating the system.

In the first part of the week I focused on integrating the Tensorflow algorithm, the MQTT library, and the GPIO library into one Python script. Integrating the GPIO was fairly straightforward as when a cat is detected by the camera or the PIR sensor detects movement, I simply set an LED to turn on for 5 seconds. The MQTT library was a little more tricky because I had to define what information will need to be sent between the Jetson and the iPhone app. The iPhone app tells the Jetson when the user wishes the door to be enabled or disabled. The Jetson then interprets this command and applies it. In addition, when movement is detected, the Jetson informs the app of the time of the movement in addition to the direction (inbound or outbound). When the app receives this information it then interprets it and updates the screen. Finally, when the app first opens it requests an update automatically from the Jetson for the most recent movement which then sends the corresponding info back to the app.

In addition to changing a lot on the backend of the iPhone app, I also spent a lot of time with the design of the app. Here is the final version:

My progress is on track.

This week I will need to test the entire integrated system. This will require working extensively with Jing and Irene.

I was originally hoping that Jing’s ML algorithm and Irene’s CV algorithm would smoothly work on the Jetson. I had prepared for this by installing all the necessary libraries for Tensforflow and OpenCV, in addition to the TensorRT libraries. However, after attempting to run the Python script on the Jetson there was a library mismatch. The drivers for the GPUs were too old of a version to work with the Python script. Unfortunately, there is no easy way to update the drivers. Originally, I was using the OS that boot up out of the box. Now, I had to download the most recent OS and re-flash the whole board.

After the re-flashing of the board, I had to reinstall all of the libraries. This process was a lot quicker than previously because I already knew what I had to do, but was rather tedious. Finally, Jing and I started to try and run the Python script. The Python script ran perfectly well on his PC laptop, now we just had to get it to run on the Jetson. After updating to the newest version of Tensorflow, and modifying bits and pieces of the CV and ML portions of the code, we were finally able to get it to work! It ran very fast, although this conclusion is just from my eye’s view, we have yet to run tests.

My progress is behind due to the complications that arose in running the Python script on the Jetson. To catch up, I will not be focusing my time on optimizing the algorithm for the Jetson, as this is not a necessary aspect of the project, simply a desired aspect.

In the next week I will be working on fully integrating the entire system. After this is accomplished I hope to be running a variety of tests on the system.

Second, I was able to download the MQTT library onto the Jetson board. I created a Python script that subscribed to messages from the simulated iPhone app on my laptop. These messages were successfully received! I now have an established protocol to communicate between both devices.