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Raymond Ngo’s Status Report for 2/19

This prior week I was getting myself acquainted with opencv and its libraries. I successfully made a function that captures webcam data both in a continuous stream and when a function is invoked. I successfully applied the cammy edge filter (for thickness detection) on a captured image and increased its threshold. (proof below) This is necessary for the computer vision part of the project because this will be the primary way to detect meat thickness for the cooking time estimation.

I am actually currently on schedule. Figuring out features of opencv and trying out some of the tools is important before starting on the real work of creating tools for the project. Furthermore, finding limitations of some computer vision methods is important before the design review.

Next week’s deliverables: some rudimentary form of blob detection. Uses opencv to capture and process an image. This is necessary because the action that kick starts the cooking process is a user placing meat in front of a camera, and this requires blob detection to see if an object exists or not.

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Jasper Lessiohadi’s Status Report for 2/12

This week I have primarily been brushing up on Python so that when we really get going with the project, I will be ready as well. I have coded mostly in C and C++ for the last few semesters, so this preparation was necessary for me to be able to properly contribute. I am not as well-versed in robotics or computer vision as my partners, so I most of my work will probably end up being related to UI and the software controller, rather than the two previously stated aspects. I have also been familiarizing myself with front-end development to better accommodate how a user would most likely interact with our product. I hope that this research will allow for a smooth and satisfying experience for anyone who tries it.

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Joseph Jang’s Status Report for 2/12

For this week, I looked into the design of robotic arms.  I looked into the different types of robotic joints that can be used.  I will use a stepper motor for the base joint of the robot, and then use 3 servo motors, which I am still deciding between the torque of two different motors – the MG996R servo (10 kg-cm) versus the Zoskay servo (35 kg-cm).  While the Zoskay is most definitely overkill and expensive, I am curious if the weight it can handle and I also have never used it.  However, there are probably MG996R servo motors that we can borrow as a team, so it would help us save our budget.  I also ordered parts from the ECE 500 Inventory and picked them up, which are the NEMA 17 Stepper Motor,  DM542T Stepper Driver, and 2 cameras for the Jetson AGX Xavier (ARDUCAM UC-698 REV. B and  e-CAM50_CUNX/NANO) I also requested a blue crate from Techspark.  I have read over the next Design assignment guidance to help assign tasks for the upcoming design presentation.  We hope to review the system architecture with a faculty member ASAP.  But by Friday of next week, I hope to have the basic design of each major subsystem down (the robotic arm, CV algorithm, UI, and software controller), and the necessary block diagrams completed and ready to be reviewed as a team.  We will have to solidify our use case quantitative requirements, implementation plan, risk factors, metrics and validation, and system architecture.  We will also have to configure all of our electronics (Jetson AGX Xavier and the 2 cameras) in the upcoming week.

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Team status report for 2/12

One risk we have not considered to the extent we should have until feedback from the instructors for other proposals was slack and integration time. For one, our integration between the software controller and the computer vision algorithm and the integration between the software controller and the robotic arm happen almost simultaneously, which creates the issue of integrating 3 items at the same time, greatly complicating the process of integration. Developing the Computer Vision and software interface might take less time compared to the robotic arm, so one idea proposed is to reduce the development time of computer vision and software by a few days or a week and use that extra time to integrate. Furthermore, testing the robotic arm on an actual grill should take place before the integration step, in the case of a serious misunderstanding of the heat tolerances of the arm.  We also made a basic concept drawing of our robotic system.  Here is our updated schedule, mostly seen at the end of March and the month of April (I’m sorry for the image quality it cannot be improved for some reason, but we have made some dates for integration longer and software and CV implementation shorter). 

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Raymond Ngo’s status report for 2/12

This past week I took a further look at the types of computer vision algorithms needed to complete the thickness estimation project. While initially we decided on using a neural network to determine the type of meat to help find the cooking time, we decided this would not be a good idea, owing to the different colors (from marinating) and the similarities of various types of meat. We would also have issues finding a proper data set to train on.

 

Instead, I looked through the different methods of finding thickness, and the best way seemed to be using the Cammy edge detector function in opencv. The challenge facing the upcoming week would be finding a way of making sure the thickness measurement (most likely in pixels) is accurate. The second issue would be making sure the meat measurement is correctly measured at a similar environment each time. This would most likely be done by having the robotic arm lift the meat to the same location each and every time, with the only variable being the position  the robotic arm grabs the meat at. However, this ignores the really thin cuts of meat. In the coming week, I will discuss the possibility of removing that type of meat from our testing metric completely, given how different it will be from every other type of meat we plan on testing. Included is the image of the outlier meat cut.

 

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