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Bhaviks Status Report for 10/19/2024

The majority of the week went into working on the design review report. For this report, I worked on various sections of the paper including introductio, use case, architecture and many more. I spent quite some time with my team to understand the feedback received from the design presentation and discussed if any changes were required to the original plan. We incoorporated any needed feedback and wrote up a comprehensive design report.

I also put some time looking into interfacing with the xbee radios to begin communication with the drone. In order to test any of the code I wrote, I need the hardware first. Therefore, I placed orders for two radio devices (one for the drone and one for the base station – my laptop). I am on schedule but will need to put in some extra effort next week to make sure my code is working as expected. Additoinally, our previous order has come in and I was able to get a small start on looking into buildling the actual drone frame. This work will continue next week with the team to finish building the frame and attatching the motors for testing any initial code.

For the next week, Gaurav and I will work closely to get the vision system working on the Kria. We will uplaod the model weights to the kria and hopefully get it running. We will also build out the frame as a team to get started on testing if possible.

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Ankit’s Status Report for 10/05/2024

This past week, I continued work on tuning the process and measurement noise for the Kalman Filter and working to interface with the motors. Specifically, this week I looked into the PPM interface and realized that this is quite similar to a PWM implementation. I found some open source code online that generates the PPM signal and used an oscilloscope to verify that the pulses generated make sense. I wasn’t able to test with our actual drone ESCs and motors because the parts have been delayed in arriving but once those come this week I should be able to test.

I will admit I am a bit behind right now. I’m having some trouble understanding how to tune the process and measurement noise for our Kalman Filter. Measurement noise I understand can be empirically calculated through data analysis of our IMU measurements but process noise is something I’m struggling to figure out how to tune. Additionally, our parts not arriving this week has really set us behind. We are hoping to get parts next week.

This week I will focus on hardware testing. We got confirmation from the ECE receiving desk that the parts should arrive this week so I am hoping to actually do the testing this week. If we can get the Kalman Filtering working with drone motor control by end of Fall Break, I’m confident we will be back on schedule

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Team Status Report for 10/05/2024

Currently, the most significant risks that could jeopardize the success of the project is integration. Currently we each have our own parts of the project that we need to work on individually so getting that working together will be our biggest struggle. Also making sure we can keep on each other schedule, for example that Ankit can complete the drone by the time that Bhavik has completed training the model and I have finished setting up the vision model on the KRIA. To manage these risks, we are keeping each other accountable and up to date so that we can all finish on time.

No changes were made to the design of the system. Our progress is currently on schedule. We are currently trying to finish our individual components for full integration sometime soon.

By next week, we hope to have the drone partially assembled assuming the parts get here in time and have the balloon detection algorithm working on the KRIA in some capacity. We may have to alter the model to account for the distance from the target but for now we want the KRIA to simply detect the target object.

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Gaurav’s Status Report for 10/05/2024

This week, I created an example Vivado project and got the complete workflow working. I also started on getting the Vision AI working, and I plan to finish that by the end of this weekend. I also worked on the Design presentation with Bhavik and Ankit and helped finalize the design and order all the parts.

I am slightly behind the schedule because I was hoping to have more of the toolchain working on the KRIA itself. However, because I do not have a good way to connect to the KRIA until the parts come in which they have not yet. However, once the adapter comes I will be able to flash the SD card with the linux image and test the vision AI on the board itself.

Next week, I hope to have the camera connected to the KRIA and a basic vision model working. I also want to have tried using Bhavik’s parameters to see if that can identify the target (which is a balloon).

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Bhavik’s Status Report for 10/05/2024

In the beginning of the week, I spent time with my team to finalize our design presentation and make any changes required for the presentation. Once we completed the work required for the design presentation, I began working on our path planning algorithim. I wrote up sudo code for our lawn mowing algorithim and the various states we will have in our drone. The next step would be to write the code out in Arduino. In order to do this, I need to first get a good understanding of the hardware parts we have and understand how to interface with them. I began by looking into how to interface with our radio and wrote up basic tests benches in Arduio that I can use to verify my knowledge and set up the radio correctly once it arrives.

I also began looking into how the altimeter and the GPS will send signals to the arduiono board. For path planning, we need to make use of these signals to determine the drones current position and determine its next step. I wrote up some basic test benches to verify these components once they arrive in our order.

On the computer vision front of the project, we ordered the various parts required for testing. Once I get a hold of a testing carmera and the testing balloons, I plan to set up a testing structure to test the accuracy of the trained model and verify that it can detect the balloon up to 20ft. I will do this by setting up a camera to a stand, and using a tape measure to walk back 20ft away from the camera. Then, we can position the balloon in various places of the frame to make sure it is able to detect it. We can also place other random objects in view to make sure the model would accurately ignore them.