Team Status Report for 2/7

Work Completed This Week As a Team:

• Contacted local trade schools regarding access to used or damaged HVAC ducts (no response yet)
• Researched robot simulators for early testing before physical hardware integration
• Contacted HVAC technicians about sourcing scrap duct materials
• Researched purchasing options for sheet metal ducts and fiberglass ductboard and documented sizes/prices
• Reviewed research papers to identify acoustic features used for corrosion and leak detection
• Researched publicly available ML datasets for acoustic-based defect classification
• Reviewed guided-wave and structural health monitoring literature to understand how tap-induced vibrations propagate through ducts
• Investigated NVIDIA Jetson Orin Nano as an onboard compute option for real-time inference
• Explored multiple mechanical tapping mechanisms for generating consistent acoustic excitation

Summary

This week our team prepared for the project proposal presentation. We finetuned our use case requirements, settling on a couple large goals: defect detection using acoustic data, localization using image data, and a generalization to different materials. After Rayann presented the proposal on Wednesday, the team focused on our supervising professor’s largest questions: where will the HVAC duct system that we plan to test the robot on come from and where will the data to train the ML model come from. Adithi and Rayann called HVAC technicians inquiring about where damaged HVAC ducts go. These technicians were unresponsive so as a precautionary measure, Rayann looked into buying affordable sheet metal HVAC ducts and fiberglass ductboards on Amazon. She saved the links and documented their sizes and prices and shared them with the rest of the team. In the next week, our team will continue to search for old HVAC duct systems. Our new direction is to call trade schools.

Mahati has already begun by contacting the Pittsburgh Trade School. Rayann also looked into different acoustic features that are used to detect corrosion and air leaks in research papers. She was able to get a comprehensive list of features we can extract from acoustic signatures. Adithi looked into finding a dataset to train the ML model with. She has found a large amount that Rayann will have to search through and find one that matches the minimum requirements: a dataset that includes acoustic data labeled with the defects we are focusing on (corrosion and air leaks) from the duct material we are focusing on (sheet metal and fiberglass ductboard). Rayann worries that if we are not able to find a dataset that fits these requirements, we will have to collect the data ourselves.

For now, Rayann is working on choosing a dataset or concluding that none will fit. No changes have been finalized to our design as of now. If there must be a change regarding where we obtain the dataset, this decision will be made by next week. This would affect our entire schedule, moving building the robot and duct system to the front and training the ML model farther to the back as we would need time to collect data.

Meanwhile, Adithi is working on the hardware design. Specifically, Adithi has already gone through the process of requesting the robotic base from the ECE department and ordering the sensors that will be mounted on the robot. Mahati has been looking into finding a simulator that we can use for testing the robot before we get to the physical phase of testing. She has found a couple of online simulators and shared with the rest of the team. In terms of our current schedule, we have taken care of most of the parts that need purchasing or requesting. However, due to the delays with finding HVAC ducts, we are slightly behind schedule. Although, as a team, we agree to do the research into finding and ordering HVAC ducts along with our tasks for next week.

•  Risks and Contingency Plans
  
• We have found that running our ML model in real time on our Jetson might need setup and debugging time which we have not accounted for. We need to make sure we have a Jetson and a contingency onboard computing option so that we can pivot and need to account for this in our schedule.
• We also found that there may be limited HVAC duct materials available to us but we have started to overcome this by calling suppliers and checking within CMU so that we can correctly get our own data as well as test our final product. We have looked through cheap options and have talked extensively on how testing will work.
• We also started the week thinking we may not have enough available public datasets but have since then made sure to review multiple datasets across different HVAC materials and are learning more about guided waves through academic and industrial literature available to us. The worst case is we can collect our own data once the tapping and sensing system is operational. Since this is one of our contingency plans, we will have to build our tapping mechanism quickly.

Design Changes

We do not have any major architectural changes as of yet however the first part of next week will be spent finalizing and finetuning decisions so this may change. We are however considering simplifying our hardware by using a single onboard compute (SBC) rather than multiple controllers. We will check in with our TA and professor about this change as well.

Schedule Update

We are slightly behind schedule as we spent a lot of time thinking about testing, material supplies, datasets, and literature. However, we have completed purchasing and technical planning and plan to catch up on our schedule by Wednesday this following week.

Next week, we aim to finalize hardware architecture, secure HVAC duct materials, begin prototyping the tapping system, and set up the software environment.