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Team Status Report 4/12

What are the most significant risks that could jeopardize the success of the
project? How are these risks being managed? What contingency plans are ready?

The risks that could jeopardize the success of the project is the battery not working for the Jetson Orin Nano and it possibly frying our Jetson. We performed extensive research on this to make sure it won’t fry it but for the small chance that it will, we backed up all of our code on github and have all of the individual components. working seperately

• Were any changes made to the existing design of the system (requirements,
block diagram, system spec, etc)? Why was this change necessary, what costs
does the change incur, and how will these costs be mitigated going forward?
• Provide an updated schedule if changes have occurred.
• This is also the place to put some photos of your progress or to brag about a
component you got working.

There have been no chances to the existing design. There have been no changes to the schedule.

We have finished composing our cane!

 

Now that you have some portions of your project built, and entering into the verification and validation phase of your project, provide a comprehensive update on what tests you have run or are planning to run. In particular, how will you analyze the anticipated measured results to verify your contribution to the project meets the engineering design requirements or the use case requirements?

Validation

We plan on testing all of the various main features individually and then together. This means testing the object detection on 50 various objects, testing the wall detection on 50 various walls, testing the FSR on 25 different surfaces. For the various object and wall tests, the haptic feedback should detect on 48 out of the 50 tests respectively (~95%). For the 25 different surfaces, we want the FSR’s to detect the floor on 24 out of 25 of them (~95%).

Additionally, we want the system to only have false positives <= 5% of the time.

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Maya’s Status Report 4/12

Accomplishments this week:
This week, I wrote the code for the FSR, and soldered all of the connections together which means our entire cane composition is mostly complete. This included everything for the haptics, and both FSRs and their connections with the QT Py. My code currently prints out when each pressure pad is pressed or not pressed, and we are currently working to integrate these responses with the Computer Vision.
The left image is the feedback we get with pressing and releasing the FSRs. The right picture is how all of our wiring is set up with the cane.

Reflection on schedule:
We had a lot of important progress this week, and everything other than our power supply has finally come together. According to our schedule, our entire cane should be complete by Wednesday, and we should begin completing usability testing, so I think we are on track with that as long as the barrel jack converter we ordered is the fix to our power issues.

  • Plans for next week:
    Over the next week, I will be working on the power supply, and we will begin our usability testing. We will also begin working on our final presentation and report!

    Verification:
    FSRs:

  • To verify the FSR system, I applied various pressures to the cane while logging voltage readings and observing whether they responded correctly to pressing and lifting.
    • A threshold voltage of 1V was chosen to distinguish between cane contact and non-contact, based on real-world walking pressure tests.
    • When the voltage exceeds the threshold, the QT Py sends a serial signal “ON” to the Jetson to indicate ground contact and trigger the computer vision script.
    • When the cane is lifted and pressure is removed, the QT Py sends “OFF”, and the Jetson pauses the object detection process to conserve resources.
  • I will also be testing the accuracy and responsiveness of this signal transition by walking with the cane and confirming that the system correctly activates only when the cane is placed on the ground.

    HAPTICS

  • The haptics send proper feedback based on which obstacle type is sent to it. We verified this by manually creating each object type and confirming the correct response was output.

    OVERALL:

  • The subsystem that deals with the QT Py was considered verified because it correctly detects cane contact, communicates with the Jetson, and produces haptic feedback reliably. We determined this was reliable because the feedback matches the print statements that we have on the screen based on object, wall, and stair locations.

 

 

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Maya’s Status Report 3/29

Accomplishments this week:
This week, I integrated the haptics with the Jetson, cut the wood and materials for our cane, and helped Cynthia work out specific cases for when objects should be detected and to trigger the proper haptic signal.

Reflection on schedule:
We had a lot of progress as a group this week and we were able to get many big steps put together. We are in a great spot for the interim demo and are in a great spot in terms of our schedule.

Plans for next week:
Over the next week, I will be working on testing each of the elements for power and current to make sure they are all going to be safe before plugging them in to the portable charger. I will also work to set up the pressure pads if time allows this week, but it is less important than the power consumption.

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Team Status Report 3/29

Risks:
The only risk is that sometimes the depth stream from the LiDAR camera gives us the wrong data/doesn’t work in random holes of the frame, so it tells us objects are 0 meters away which will likely lower our accuracy but hopefully won’t end up interfering with too much.

Changes:
We changed our plan back to the original plan of using pyrealsense2.

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Cynthia’s Status Report 3/29

Accomplishments this week:
Finished debugging and integrated my fine-tuned YOLOv8 model that now classifies stairs along with objects (image included below — note that our Jetson has to be plugged in now, so we couldn’t bring it to actual stairs but I emulated stairs with the cart in the image).  Helped Kaya with fixing pyrealsense a little, then once it worked I used his code that gets distance from grid points to make the code that runs the model and creates bounding boxes now get the distance to the center points of the objects too.  I also worked with Maya to get the haptics working from the Jetson.  Lastly, I wrote the code to integrate/trigger the haptics and decide what action to suggest with the object detection model, based on objects detected, their location, and their distance — this currently works but the suggested actions are not all correct yet.

Reflection on schedule:
I believe I did a lot this week and caught up to what we planned.

Plans for next week:
Work with Kaya and get the wall detection working.  Fix the recommended action decision making code.

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Kaya’s Status Report 3/29

Accomplishments this week:
I worked on reconfiguring the jetson jetpack so that we can get the correct pylibrealsense module. This involved reflashing our SD card, researching the compatible modules with our new Ubuntu v20.04, and reinstalling all of the compatible modules. After that, I wrote code for distance detection and was able to get distance detection working at 9 different points. Lastly, I was able to integrate that distance detection code with Cynthia’s CV code (see photo below for distance detection on 9 points integrated with the CV code).

Fig 1: Distance Detection on 9 points integrated with CV algorithm.

Reflection on schedule:
We did a lot this week with integrating distance, CV, and haptics so we are on track.

Plans for next week:
My plan is to work on wall detection with the distance code. Additionally, we plan on writing code for the force sensitive resistors and plan to start building our cane.

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Cynthia’s Status Report 3/22

Accomplishments this week:
I worked with Kaya after the correct versions of the libraries we needed were installed (after a lot of trouble and many hours spent on this) to get the pre-trained ML model working on the live RGB stream (see photos below).  I additionally had to change the code I previously wrote to work around what we decided does not work (pyrealsense2) on the Jetson, which we were depending on for its depth stream.  Recently, I started working with Kaya to get depth data and figure out how we can use that data in my python scripts instead of just getting it as a terminal command output.

Reflection on schedule:
I think I am slightly behind what our schedule was for my portion, but that is because we switched to using the camera on the Jetson earlier than planned since I am not able to use the library I need on the desktops and cannot use the camera on my laptop, so I was never able to test my code until one day ago and have not included stairs in our model yet.  Additionally, I was sick (and still am) and was able to work less than planned.  Overall, because of the rearranging, we are still on schedule as a group, but I need to continue to make good progress with our model moving forward.

Plans for next week:
Write code to train the model to incorporate a dataset of stairs and work further on getting distance measurements without pyrealsense2.

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Team Status Report 03/22

Risks:

The only risk we have is that the only way we can access the distance data is through a terminal command. We need to come up with a way to run that in parallel with the CV algorithm.

 

Changes:

We are no longer using pylibrealsense2 for our ML model and instead using open cv with a yolo model.

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Kaya’s Status Report 03/22

Accomplishments this week:
This week, I configured the proper CV libraries to analyze our Lidar camera data. I did this by setting up a virtual environment with the proper libraries (Python3.7, OpenCV version 4.11.0).  Additionally, I configured our Jetson to analyze the distance data and wrote a script for this analysis.

Reflection on schedule:

We are slightly ahead of schedule now. We did a lot this week (object detection, distance detection, haptics).

We dedicated a lot to capstone this week, especially with the configuring of the libraries. We had to research and reconfigure the python and pip libraries to match the needs of the Lidar camera. After numerous tries of source installing and pip installing pylibrealsense, we came to the conclusion that we can’t use this library pylibrealsense. After trying other things out, we came to the conclusion that cv2 is the best python library for analyzing the camera data. Additionally, we came up with a separate command for getting distance data.

Plans for next week:

Work with Cynthia on writing a script to connect the distance detection with the computer vision code. Additionally, I plan on working with Maya on configuring the force sensitive resistors.

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Maya’s Status Report 3/22

Accomplishments this week:
This week, I setup the haptics and created a few sample patterns that we will be using with our Jetson.  The haptic patterns for each obstacle type is demonstrated here.

Reflection on schedule:
We were a bit behind schedule at the beginning of the week because we had some problems with the Jetson and L515 compatibility, but we put in a lot of hours this week to recover from this. Personally, that included me helping Kaya with the Jetson and L515 connections, and I also setup the haptics and created case statements for when each haptic pattern is set off.

Plans for next week:
Over the next week, I will be working to run the haptics over the Jetson and hopefully begin to connect the haptic responses through the CV code, and I will begin testing the overall power consumption to make sure it is under 30W and 5V.