This week, I primarily worked on finishing the design report with my teammates and finalizing several of the software-side design choices related to the UWB functionality. I decided that the best way to send information from the iPhone receiver to the Raspberry Pi would be through a local Wi-Fi HTTP bridge. In this setup, the Pi runs a lightweight HTTP server on the local network, and the mobile app on the receiver’s iPhone sends JSON packets containing UWB position data using HTTP requests.
I also conducted additional research into LiDAR drivers for the Raspberry Pi to understand better how sensor data will be processed in our system. Our RPLIDAR will connect to the Raspberry Pi via USB, and its driver allows the Pi to read and process distance and angle data from the sensor. This data is then converted into a 2D map showing nearby obstacles and open spaces. I explored various software options for running the RPLIDAR on the Raspberry Pi, including the official SDK, a lightweight Python library, and a ROS-compatible driver for real-time mapping. This research helped me better understand how the LiDAR subsystem will integrate with the rest of our navigation and obstacle-avoidance software.
According to our Gantt chart, my progress is behind schedule, as the goal for this stage was to finalize our mobile app and have the beginnings of shopper tracking over UWB. I was unable to dedicate as much time to these goals since we were working extensively on the design report.
To get back on track, I plan to finalize the mobile app next week and ensure the UWB session is fully functional between two iPhones. I will also focus on establishing the connection between the receiver iPhone and the Raspberry Pi, which involves setting up the Wi-Fi HTTP server on the Pi and linking the iPhone to it. Once this is in place, we’ll have a clearer understanding of how the data transmission and processing will work within the overall system.