This week’s progress:
This week, I focused on refining the machine learning model, with the goal of improving both its accuracy and overall robustness. I explored ways to better structure the data and tune the model so that it can generalize more effectively to real-world scenarios. This refinement process is an important step toward ensuring that the model performs reliably when integrated into the full system.
In addition to the machine learning work, I made progress on the Jetson-side implementation. I restructured the codebase by separating the robot control logic from the ADC signal processing pipeline. This separation makes the system more modular and significantly improves the ability to visualize and debug sensor data independently from control behavior, which will be valuable as the system becomes more complicated.
Things to do for next week:
Looking ahead to next week, I plan to collect more training data to further strengthen the model. With a larger and more diverse dataset, I aim to improve the model’s performance and reduce potential edge-case errors. I will also continue testing the system end-to-end to ensure stable and consistent communication between all components, including the Jetson, Teensy, and sensors.
Additionally, I will be collaborating with Aditi to integrate the LiDAR system and begin working toward autonomous navigation of the robot through the HVAC environment. This will involve combining perception and control components into a more cohesive system.
My work can be found here: https://github.com/aphadke234/ece_capstone_C7