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Bonnie’s Status Report 3/28

This week, I completed the integration between the iPhone application and the Raspberry Pi using Bluetooth Low Energy (BLE) as well as polishing the APP interface. The system is now structured with the iPhone acting as a BLE central that scans for and connects to the Raspberry Pi, which operates as a BLE peripheral. Once connected, the app sends JSON-based commands such as movement, stop, mode, and location, which are received and processed by the Pi.

On the Raspberry Pi side, I implemented a BLE server that advertises services, handles incoming commands, and translates them into motor control signals. The system successfully maps directional commands from the app to two stepper motors controlling the umbrella’s vertical and horizontal movement through GPIO pins. Testing confirmed that commands from the app correctly trigger motor actions, establishing a functional end-to-end control pipeline.

At this stage, both only manual is integrated at the software level and reflected in the app, although motor behavior is currently driven directly by movement commands. Overall, the system now supports reliable wireless control of the umbrella, and future work will focus on the automation mode and refining mode-based behavior and specific calculations of the motor movement degree.

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Ore’s Status Report for 3/28/26

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.

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Team Status Report for 3/21/26

The primary risks to the success of the projects remains mechanical hardware and assembly delays often regarding the 3D printing or just general assembly time. Delays in CAD refinement and hardware selection have pushed back the physical testing phase a bit which is more than we would have liked. As mentioned last week we continue to adopt an all-hands approach to the hardware integration.To mitigate any backlog in assembly and 3D printing, team members primarily focused on software will shift their attention to assist with the physical build as needed so that it can be up and running for our testing processes. This will ensure that the mechanical components don’t become a permanent bottleneck for our progress. As of right now there are no significant design changes.

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Bonnie’s Status Report 3/21/26

This week, I focused on refining the mobile application and completing its integration with the Raspberry Pi–based control system for the smart umbrella. I continued developing the app in Xcode and worked on improving the overall user interface and responsiveness. The control features were polished to make manual adjustments of the umbrella’s position more intuitive, allowing users to easily set the desired pan and tilt angles.

In addition to the app improvements, I successfully integrated the mobile application with the Raspberry Pi. I established communication between the app and the controller, enabling commands sent from the app to be received and executed by the hardware system. Through testing, I verified that user inputs from the app correctly trigger the stepper motors, allowing the umbrella to move as intended.

Overall, this week’s progress resulted in a functional end-to-end pipeline for manual control, where the mobile app can reliably communicate with the Raspberry Pi and control the physical system. Moving forward, I plan to enhance real-time feedback from the controller, improve communication reliability, and begin adding more advanced features such as automated tracking and preset positions.

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Ore’s Status Report for 3/21/26

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.

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Ore’s Status Report for 3/14/26

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.

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Team Status Report for 3/14/26

One of the most significant risks to the success of the project is delays in the physical assembly of the system, particularly the integration of the motors, gears, and structural components. Although much of the initial software development has been completed, meaningful system testing depends on the mechanical components being installed and functioning properly. Without the motors and gearing mechanisms in place, we cannot fully validate the motion control, actuation, and overall system responsiveness of the shade system. Mechanical integration also presents challenges such as alignment issues, mounting stability, and ensuring that the motors provide sufficient torque to move the canopy reliably.

To manage this risk, we’re prioritizing the completion of the remaining mechanical tasks so that the hardware and software subsystems can be integrated as soon as possible. The focus for the current phase of the project is completing the mounting brackets, canopy fabrication, and the full physical assembly of the arm, joints, canopy, and motors. These tasks are distributed across the team to ensure steady progress and reduce the likelihood of delays caused by a single point of failure. By accelerating the physical assembly process by having more people helping, we will be able to begin integrated system testing earlier and identify potential issues before the final stages of the project timeline.

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Bonnie’s Status Report for 3/14/26

This week I focused on the development of the iOS application that will control the smart umbrella system. I created the initial Xcode project and implemented the basic app structure using SwiftUI. The app includes separate components for models, services, view models, and views to keep the code organized.

I implemented the networking layer that will allow the app to communicate with the Raspberry Pi using HTTP requests. The app is set up to send commands such as move, stop, and mode changes, and to retrieve status information from the umbrella system.

The user interface currently includes the initial screens for connecting to the device and controlling the umbrella. These screens will eventually allow users to adjust pan and tilt angles, change modes, and monitor system status.

I also committed the initial version of the app to our GitHub repository and pushed it to a new branch (iOS-APP) so the team can review and collaborate on the implementation.

Next steps include connecting the app to the Raspberry Pi API, testing communication between the phone and the umbrella system, and improving the control interface for smoother interaction.

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Ore’s Status Report for 3/7/26

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.

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Team Status Report for 3/7/26

At the moment, the most significant risks that could jeopardize the success of the project are falling behind on our individual work, which may cause other team members to not be able to progress within their areas of work in the project. We’re planning to be more strict with following our schedule and staying on top of things to have a working demo in time, and shifting priorities within the project as needed, keeping our TA and designated professor in the loop. We’re currently working on the perception, app development, and the physical assembly of our project

 

A was written by Bonnie, B was written by Ore, and C was written by Nicholas.

Part A: Global Factors

Our smart beach umbrella considers several global factors beyond a local setting. Rising global temperatures and increased UV exposure make sun protection more important worldwide. An automatically adjusting umbrella can help reduce health risks such as heat stroke and sunburn in many regions.

Accessibility is also a global factor. Not all users are physically able or technologically experienced enough to adjust a traditional umbrella frequently. Automating the process makes the product more inclusive.

Cost and scalability were considered by using affordable components such as a Raspberry Pi and standard motors, making the system more practical across different economic environments. Privacy is also important, since the umbrella uses a camera and mobile app, so data processing should remain local to protect users.

 

Part B: Cultural Factors

The design of Shade.ai also considers cultural factors associated with beach recreation and outdoor leisure. In many coastal communities, beach outings are social and family-centered activities where convenience, relaxation, and shared spaces are valued. A system that autonomously maintains shade allows users to focus on spending time with friends and family rather than repeatedly adjusting equipment.

 

Part C: Environmental Factors

Shade.ai prioritizes the protection of living organisms and the responsible use of natural resources through its core functionality and material selection. By utilizing autonomous solar modeling and CV-based tracking, the system provides a reliable barrier against harmful UV radiation, addressing a health risk for beach-goers that is often underestimated due to unreliable cooling sensations.