The most significant risks that could jeopardize the success of the project are related to integration. The first one will be gathering meaningful images to perform the ML algorithms on. As of now, we have acquired the radar and plan to begin image capturing within the next two weeks. With this comes the challenge of figuring out how to best position and use the radar so that the images it captures can be used with the ML algorithms we train. We will be using a dataset of standstill drone image captures to train our model, but until we begin image capture with the radar, the radar image quality is still a large risk that could delay the integration of the software with the hardware if the radar images are significantly different than the dataset images. These risks are being managed by starting the radar image capture as early as possible (i.e. within the next two weeks), since the ML training process will not be significantly far along before we start image capture. Therefore, we have allotted time to examine the radar captures together and ensure that they work with our dataset. In addition, we have looked into other radar image datasets and sources to find these datasets in case we find that our dataset is drastically different in comparison to our radar images.
One change we made to the existing design of the system is that we narrowed our project scope down to fire search and rescue missions. While we were planning on doing search and rescue missions that did not involve metal, since that would interfere with the radar, we did not explicitly narrow down the scope further than that. We received feedback from our TA that our use case scope was not extremely clear in our presentation and that it would be very helpful to do so in order to make clearer goals for ourselves and allow us to come up with a more specific testing plan. This change incurs no extra costs since it allows us to create more specific plans going forward and narrow down our needs. In addition to this change, we also added the creation of 3D chassis to encapsulate our device and have it rest on the drone legs. We had not previously included this in our project spec, but we needed to include something that would safely keep our entire device together and allow it to attach to any drone that could hold its weight. This did not incur many extra costs, since Angie has experience with 3D printing and was confident she would be able to create this chassis with ease. We had to allot one week to design and print this chassis to hold our radar and raspberry pi, which will occur once we acquire the raspberry pi, since we have already acquired the radar. This did not add extra time, since it can be done in parallel with many of the other tasks and does not have many dependencies.
As of now we are on schedule with our project, and plan to stay on track with our plans for the next few weeks.
Our project includes considerations for public health and safety concerns because of our use case. Our project is designed to help first responders stay safe by limiting the amount of time they are exposed in high danger areas. Our project also focuses on improving the efficiency and cost of search and rescue missions by using an mmWave radar. Currently the infrared sensor is more commonly used but can provide unclear results due to the flames. Since our radar would not get blocked by the waves from the fire, our project should allow for better human detection in fire, and thus help save more people.
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