Month: April 2024

This week I worked on integrating the Bluetooth connection from the phone to the RPi using bluez, but we ran into some issues with compatibility with the ios applications, so we had to pivot to using an MQTT server instead which we worked on setting up. Additionally, I conducted further testing for the ultrasonic sensors and raspberry pi camera quality against the model to test if it works in different environments.

I believe I am on schedule.

For the next week, we will continue fine-tuning our MQTT server to establish a connection between the iOS application and the RPi as well as continue further testing in preparation for the demo.

• What are the most significant risks that could jeopardize the success of the
project? How are these risks being managed? What contingency plans are ready?

The biggest risks of the project are on the latency of our product and trying to keep computation time lower so that our latency is low enough for the user to effectively use. One change we’ve made is removing the Jetson as the data transfer from the RPi to the Jetson heavily affected our latency, so we decided to remove the extra device and do all the computation on the RPi. We have contingency plans and that’s to lower our data set that we will training our model on to potentially reduce latency even further if necessary for the demo.

• Were any changes made to the existing design of the system (requirements,
block diagram, system spec, etc)? Why was this change necessary, what costs
does the change incur, and how will these costs be mitigated going forward?

We have changed our existing design by removing the Jetson, so it will just be the RPi that will run our ML model as well as our distance code and we will send all that information from the RPi to the App. This change was necessary as we struggled to effectively use the Jetson, and we found that latency would be an even bigger issue if we were to use the Jetson as well as the RPi due to the time it would take to transfer data from the RPi to the Jetson

• Provide an updated schedule if changes have occurred.

We have no updates for our current schedule.

For this week, I focused mainly on developing the final presentation that I’ll be giving, so which means preparing the slides as well as what I’ll be saying for the presentation. Additionally, I focused on gathering all the information from the testing and validation that we conduct for all our individual components and for our system as a whole.

I am currently on schedule

Next week, we will continue to test our entire product by putting it through some more latency tests and testing it against different objects we will be assessing during our demo.

As you’ve designed, implemented, and debugged your project, what new tools or new knowledge did you find it necessary to learn to be able to accomplish these tasks? What learning strategies did you use to acquire this new knowledge?

We recognize that there are quite a few different methods (i.e. learning strategies) for gaining new knowledge — one doesn’t always need to take a class, or read a textbook to learn something new. Informal methods, such as watching an online video or reading a forum post are quite appropriate learning strategies for the acquisition of new knowledge.

For almost all tasks I completed, I had to learn various new skills, particularly pertaining to the Raspberry Pi, Ultrasonic sensors, and Pi Camera modules. Setting up the Raspberry Pi was extremely tough, especially connecting it wirelessly using the headless setup. I picked up several new strategies to acquire new knowledge among them was looking at YouTube tutorials or more visual tutorials to complete my tasks as seeing another person deal with the Raspberry Pi was far easier to follow than any documentation. Additionally, I found forums such as the Raspberry Pi forums very helpful to see for bugs and any potential issues as almost always there was another person who had a similar issue, and they were fixed on those pages. Finally, documentation was also very helpful especially for the Camera Module as I relied on it heavily to follow to set up the module, download the necessary dependencies, and so on.

This week I mainly focused on continuing to develop without the Jetson while we wait for it to be up and running. Firstly, I set up the RPi with CMU Wifi via a wired connection, but now that I’ve set up the initial connection, it’s easier to set up the wireless connection. Furthermore, I’ve worked on tweaking my detection model to account for objects that are entering the sight of one sensor to another to ensure that only one of the sensors is picked for the direction that will be sent to the Jetson. For this, I simply added to my filtering algorithm to ensure that there was an object detected on two adjacent sensors within a certain distance range and time frame then the measurement on the sensor furthest away from the user’s north will be discounted. Furthermore, I established Bluetooth connectivity from RPi to the IOS app, but this connection will be used secondary to the Jetson connection as this is a backup option in case of failure of the Jetson.

I’m currently on schedule.

Next week, we hope for the Jetson to be up and working so we will look to connecting the Jetson to the IOS app as well as ensuring the serial connection between the RPi and Jetson works as expected. Furthermore, we are going to build the actual device that the user wears so we can run tests based on how the product will look on the actual user.

Verification and validation:

So far I have completed thorough testing on the range and direction coverage of the ultrasonic sensors. I’ve completed individual tests of the ultrasonic sensors to see their degree of coverage and their distance measurement capabilities, and I have completed this same test with multiple sensors. In addition, I aim to complete further testing with multiple sensors but also analyzing how the sensors react if an object overlaps between two sensors or if an object goes from one sensor to another. These results will be analyzed to deduce the placement of the sensors on the headband and how much distance there should be between each sensor. In addition, I have used this data to adjust how my code is set up in regards to double detection on the sensors and to ensure there are no overlaps in how the objects are detected to ensure the user has the right bearings of the direction of the incoming object.

As far as testing the capabilities of the camera, I have completed testing based on how the camera works in different environments and if the photo quality is good enough for our ML model to see and run on. Furthermore, I will also complete latency testing for how quickly the data is transferred from the RPi to the Jetson and if it meets our latency requirements. Finally, I have also run tests on the portable battery to ensure that the battery life meets our requirements, and with my program running, there’s a battery life of 4 hours however I anticipate that will go down by the time we complete testing with running the entire model.

For the rest of the project, most of my testing will be completed with the actual device on the headband as results could differ when it’s placed on the user’s head. The testing I will complete for the physical headband will be similar to the testing I have completed on the individual sensors but account for the user’s head movement and body movement in general.