Author: asalu

WORK ACCOMPLISHED:

This week, I tried to work on setting up the Raspberry Pi 4, but I realized I would require a micro SD card reader; hence, I was unable to move forward as I was missing the device. I also worked more on the Google Maps API.

Additionally, I decided to modify the design of the system by removing the web server and localizing the navigation and audio system to the Raspberry Pi instead. This drastically reduces the latency required for our system.

PROGRESS:

Due to some issues I faced, I’m currently behind schedule as I had expected to finish up with how to record audio files from the Raspberry Pi and also begin to work on integrating the Google Speech-to-Text AI.

NEXT WEEK’S DELIVERABLES:

I am mostly will try and catch up on last week’s deliverables. So, I will working on how to record audio files from the Raspberry Pi and sending it to the Navigation endpoint. I will also begin to work on integrating the Google Speech-to-Text AI.

WORK ACCOMPLISHED:

This week, I primarily worked on designing the workflow for using the user’s voice commands to extract the destination for the trip and also began thinking about the relevant data that will be required for the Google Maps API call.

Google Maps API url

(Cleaned) API response  with locations and navigation instructions

Additionally, I decided to change the type of microphone being used for the system from a MEMS Omnidirectional Microphones to a standard USB microphone. The main reasoning behind this was that the USB microphone is easier to configure and has better sound quality compared to the initial microphone.

PROGRESS:

I am in progress for the moment.

NEXT WEEK DELIVERABLES:

For the upcoming week, I will be working on how to record audio files from the Raspberry Pi and sending it to the Navigation endpoint. I will also begin to work on integrating the Google Speech-to-Text AI.

For this week, I was working on research for the speech and navigation aspects of our system. Specifically, I was identifying the different software components that would be required in translating the user’s speech, containing their desired destination, into inputs that can be used by the Google Maps API platform for retrieving the intended route. This is the tentative workflow for this process:

1. Microphone receives location from user’s speech
2. Google Speech to Text AI is used to extract destination from user’s speech
3. Google Maps Geocoding API to translate location to Longitude and Latitude (Numerical Representation)
4. Use the location information from JSON response with user’s GPS location to get route from Google Maps Direction API
5. Each leg of journey is stored in a database so, each “leg” in the journey maps to an instruction at that point e.g. turn left on xxx road, turn right at yyy intersection
6. The system will use the real-time GPS location to locate the closest leg of the journey and use that leg’s instruction for audio output to the user. The algorithm for figuring out closest leg to the current location is the R-tree algorithm.

For next week, I will be working on programming working demos that utilize the Google Speech APIs and Google Maps APIs. Additionally, I will be working on how to use the R-tree algorithm for the navigation system.For next week, I will be working on programming working demos that utilize the Google Speech APIs and Google Maps APIs. Additionally, I will be working on how to use the R-tree algorithm for the navigation system.