This week I worked on preparing our decided cloud platform: Azure IoT Hub and starting the software integration of Aditti’s wavefront segmentation computer vision program with the USB camera. The camera integration proved to be a difficult task, as with closer distance and smaller arenas (I used Letter sized paper as the “arena”), the camera shadow would be interpreted as an additional object. I hence had to use an artificial light source from my phone camera to make sure objects were illuminated correctly. I suspect we will need further testing on making sure the camera is stable and can click frames without a shadow appearing from its overhead nature. I have attached a few pictures below illustrating my testing of the camera feed. We have created a Github repository for our code and CAD files so far.
For the Azure application, there are currently two steps I must perform. One is to link an Arduino with the Wifi module, and the next step would be to link this Wifi Module to the cloud. I have found the following resources to help me investigate the same. (https://blog.avotrix.com/azure-iot-hub-with-esp8266/ for connecting the ESP8266 to the cloud and https://www.instructables.com/Get-Started-With-ESP8266-Using-AT-Commands-Via-Ard/ for controlling the ESP8266 from the Arduino). Next week, after we have all the robot CAD files printed out, Caroline and I will start the next step on sensor array assembling and cloud data collection.
The courses that helped me understand this course are 10-301 Introduction to Machine Learning for computer vision segmentation, 18-220: Electronic Devices and Analog Circuits for understanding basic commands and time delays on the Arduino. We spent some additional time researching multitasking on the Arduino for parallel processes. I have no formal experience in Cloud Computing, but I am certified as a Machine Learning Engineer and Cloud Architect for Google Cloud Platform, which has helped me immensely in investigating implementation on the cloud this week.