Category: Ore Abolade’s Weekly Status Reports

Focused mainly on preparing and finalizing the final presentation, since I was the one presenting. I also started working on the final poster early to make sure we stay on track and don’t fall behind. In addition, I helped finalize testing across all subsystems and the overall system to ensure everything is validated.

Physical assembly is slightly behind schedule. To address this, we will prioritize assembly tasks this week and ensure more time is allocated specifically to completing the hardware integration.

Plan to continue working on and finalize the poster, and help push physical assembly to completion for the demo day. Also, aim to support any remaining system-level testing once the full assembly is complete.

Worked on full sensor integration, debugging, and testing with the motors. This included verifying that sensor outputs (e.g., IMU, sun sensor data, user detection) are correctly read by the Raspberry Pi, processed into control signals, and then used to drive the stepper motors. Specifically, I tested the full pipeline from sensor readings → control logic → GPIO signals → motor actuation, ensuring that the motors respond correctly to changes in sensor input (e.g., adjusting position based on orientation or sun alignment). Ran into an issue where the motors stopped working, which required debugging and rewiring parts of the system. After we fixed the issue, I continued motor–sensor integration and testing, which is now almost complete.

Slightly behind schedule due to the time spent debugging and rewiring the motors. To catch up, I will focus on completing real system testing (moving the canopy) and prioritizing integration validation.

I plan to complete real system testing with canopy movement and finalize motor–sensor integration testing in the next week.

I learned more about motor control and interfacing with the Raspberry Pi 5, building on my previous basic understanding. Used online videos, Raspberry Pi tutorials, and posts from past ECE capstone groups working on similar projects. I also relied on prior knowledge from past courses and projects for some of the user detection methodology.

This week, I focused on progressing toward full system integration. I worked on motor debugging and connecting the user detection pipeline with the rest of the system, specifically preparing it to interface with the Bluetooth control pipeline and motor subsystem.  I also began structuring the integration logic that combines inputs from the vision system, Bluetooth app commands, and solar positioning into a unified control flow.

My progress is on schedule with the revised Gantt chart. With the major debugging issues resolved and subsystems I’m dependent on now functional, I am able to move forward into integration.

For verification of my subsystem, I am testing the user detection pipeline by measuring detection latency and stability. I also verify that the computed position error responds correctly to user movement in the frame.

Next week, I plan to complete the full integration script that combines the Bluetooth app, user detection system, and solar positioning logic. I will begin end-to-end testing by feeding detection outputs into the motor control system and measuring how accurately and quickly the system responds. To analyze results, I will compare measured latency, responsiveness, and tracking accuracy against our design requirements to ensure the system meets both engineering design and use-case requirements.

This week, I successfully got the user detection system fully running on the Raspberry Pi 5 with the Raspberry Pi Camera Module. I resolved the remaining camera and runtime issues and verified that the full vision pipeline works reliably on the device. The system now runs the MoveNet pose estimation model in real time, detects body keypoints, computes the user’s torso center, and outputs the position error relative to the center of the camera frame. I also began working on communication between the user detection script and the motor control subsystem, preparing to use the error signals to drive the pan and tilt motors.

My progress is now on schedule. With the major debugging issues on the Raspberry Pi resolved, the vision subsystem is stable and ready for integration. This puts me in a good position to move forward without impacting the overall project timeline, while the rest of the team continues advancing the mechanical assembly in parallel.

Next week, I plan to complete the integration between the vision system and the motor control subsystem so that the umbrella can automatically adjust based on the detected user position. I will also continue testing the system in real-time conditions to ensure stability and responsiveness, and support full system integration as the mechanical components are finalized.

This week, I focused on continuing to debug the user detection deployment to the Raspberry Pi 5. I worked on identifying setup and runtime issues on the device. I also installed the fan case for the Raspberry Pi because it was overheating even when idle, which will help ensure stable operation during longer testing sessions. Progress this week was somewhat limited because I was at a conference starting midweek and had to leave the Raspberry Pi on campus, but I still spent time diagnosing issues and planning next steps for debugging and setup.

My progress is behind schedule due to the remaining setup and debugging work required on the Raspberry Pi. There are still a few external components we need, such as a USB keyboard and mouse, and an Ethernet cable, to fully configure and streamline code development on the Pi 5. Once those are available, I expect the setup issues to be resolved quickly. Importantly, this delay does not block my teammates’ progress, as they are continuing work on the mechanical assembly and other subsystems in parallel.

Next week, I plan to complete the Raspberry Pi setup and get it running reliably on the hardware. I also aim to begin integrating the user detection system with the motor control subsystem, allowing the position error output to drive the pan and tilt adjustments. As the physical assembly progresses, I will support early integration testing of the full system to ensure the tracking pipeline works smoothly with the mechanical components.

This week I focused on getting the user detection system running on the Raspberry Pi 5 with the Raspberry Pi Camera Module. I ported the vision pipeline to the Raspberry Pi and began testing live camera input while running the pose estimation model. The system uses MoveNet to detect body keypoints and estimate the user’s torso center, which is then used to compute the position error relative to the center of the camera frame. I spent most of my time testing the pipeline on the Raspberry Pi, debugging runtime issues, and verifying that the camera feed and model inference run correctly on the target hardware.

My progress is slightly behind schedule because I am still debugging a few issues related to running the system on the Raspberry Pi.

Next week, I plan to finish debugging the user detection system on the Raspberry Pi and continue testing the tracking pipeline with the camera. As I also help get the physical assembly completed, this will allow me to begin early integration testing of the full system. I will begin connecting the vision output to the motor control system so the umbrella can adjust its position based on the detected user location.

This week, I helped narrow down which parts to buy and started submitting actual purchase orders. I also worked on the user detection implementation, but mainly this week, for me, was drafting, refining, and submitting the design report. I’m currently on track with my scheduled work. Next week and a half, I hope to finish locally testing the user detection and have the full physical assembly of our shade system completed.

I worked this week on deciding on the best motors we planned to use based on the requirements of our project.  I have worked on initial designs of how the motors will interact with the umbrella; I also looked into different umbrellas/canopies we’re going to purchase, along with the best material/fabrication to use so the arms and joints don’t put too much pressure on the motor.s

My work is currently on schedule with the reworked schedule that we put into place after our project changes from Professor and TA feedback. Next week, I hope that once we acquire a camera, our motors, and the umbrella/canopy, I can start making progress towards the implementation of the lighting calibration and user detection.

This week, we had several meetings about our intended product and its feasibility within the scope of the semester.  We decided to pivot away from working with drones, so I then spent the next few days doing some research on our other ideas for the upcoming design review presentation, ultimately helping to pinpoint and flesh out a new idea we can go with for our capstone project, actively keeping our designated professor and TA in the loop. Our collective progress is a bit behind due to us having to pivot from our initial capstone project, which also means I’m individually behind in what I had initially planned for the week. I am working towards getting the design review presentation done on time first, and then start gathering components and resources we’ll need to start building out our product in the coming week.

This week, I focused on doing research for our proposed sensing, computation, and communication approach for our system. I also researched hardware requirements needed to run efficient, lightweight CV models: camera modules, CSI connectors, and mounting considerations. I looked into the hardware options for our drone, comparing different levels of assembly, and seeing which one is the most feasible and optimal for us.  I also looked at obstacle avoidance requirements, weighing the pros and cons of 2D vs 3D implementations. Finally, I began looking into what canopy design and material would be best suited to our use cases, and would have minimal impact on our overall drone flight stability and accuracy. Next week,  we’re looking into actually acquiring a drone, and then I’ll begin component-flight controller communication setup with the team. I’ll also try to start building the Git repository with all the pipelines and models we’ll be using. My progress is on schedule.