This week I focused on the design and implementation of the state machine for path finding and user following behavior. In particular, I worked on defining how the robot should transition between different states when navigating around obstacles while maintaining a stable following distance. This includes handling cases where the robot needs to temporarily deviate from a direct path to the user and then re-align once the path is clear. I refined the logic to make sure the behavior remains predictable and safe, especially when combined with the existing idle, searching, following, and stop states.
In addition, I worked on improving the vision pipeline by exploring ways to better distinguish the target user in multi-person scenarios. I experimented with training a lightweight CNN-based component to capture color features, which can help differentiate between individuals when detection alone is ambiguous. This is intended to complement the existing tracking approach and improve robustness in cluttered environments.
My progress is currently on schedule, as these tasks align with our planned work on perception and system behavior. Next week, I plan to continue integrating the state machine with the rest of the system and begin testing how the color-based features interact with the tracking pipeline in real scenarios.