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Wuyang Li’s Status Report for 03/07/2026

This week, I finished writing the design proposal with my teammate Xinyu. We each were responsible for some sections, and I particularly worked on the later sections, i.e., testing, project management, summary, related works, etc. I also did some research into how to implement the speed controller, and started to implement it.

Generally, I’m still on schedule, although I haven’t entirely finished the speed controller implementation which should have been done by this week. However, this was mainly due to the spring break, and I still have time tomorrow to complete it.

By next week, I hope to have the entire motor mobility module completed. This includes the basic mobility and the speed controller. I will also try to write some coverage tests to ensure that it works.

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Xinyu Li’s Status Report for 03/07/2026

This week I focused on getting the YOLO object detection model running on our Raspberry Pi so that we can start testing the vision component of SafeFollow. I worked on setting up the runtime environment, installing the necessary dependencies, and running initial inference with the camera input. One challenge I encountered was the frame processing rate. Because the Raspberry Pi does not have a dedicated AI accelerator, running YOLO on the CPU can result in very low frame rates, sometimes only a few frames per second, which makes real-time tracking difficult.

To address this, I experimented with reducing the input image resolution and adjusting the camera capture settings to balance accuracy and speed. I also explored using lighter YOLO model variants and optimizing the inference pipeline. These adjustments helped improve the processing speed somewhat, although performance tuning is still ongoing. Next week, I plan to continue optimizing the pipeline and integrate the detection output with our person tracking module.