This week, our team focused on preparing our project for the interim demo. Adithi and Mahati worked together to debug code for the motors to make the robot chassis move forward, backward, and turn (both right and left). Adithi worked on the code to drive the LiDAR camera in such a way so that it can detect whether or not the robot is in the duct and the physical integration between the camera and the robot chassis. Adithi has also started building our final demo HVAC duct to show our project on. Mahati worked on rebooting the Jetson and integrating all the systems including the acoustic data collection system, LiDAR, motors, and ML model with the Jetson. Mahati physically integrated the ultrasonic sensors with the ADC and Teensy MCU on the robot chassis. Rayann collected healthy duct and corroded material data using the data collection circuit. She plans to collect more samples. She also debugged the MATLAB processing code to take in the data she collected, and processed this data so it is ready to be used to train the SVM. She also wrote another processing pipeline in MATLAB specifically meant to be downloaded onto the Jetson and take data from the Teensy MCU, process the data, and hand it over to the SVM. She is currently debugging this code.
We are currently on schedule, with a few things to accomplish in the coming weeks. We will finish collecting data, debug our on board processing pipeline, and train the ML model. We will finish building our demo structural HVAC duct so that we can have a good presentation to show viewers what our project is. We also want to make sure the robot does not have to be connected to a charger by the NVidia and that we can have our LiPo battery pack on the Jetson. We will also begin working on the final presentation, final report, and demo poster.
A potential risk is the ML model not being able to reliably classify defects vs healthy ducts. This is quantified using false/true positives and negatives. In this case, we plan to either change hyperparameters or change the processing pipeline after analyzing the data (adding/removing features).
All of our code can be found in the github: https://github.com/aphadke234/ece_capstone_C7