Author: eanand

Potential Risks and Risk Management
Upon testing the servo control logic, we noticed erratic behavior in the angle the servo would turn to. However, we narrowed this down to a mechanical issue, which has since been fixed through acquiring a new servo mount. 

Overall Design Changes
No significant design changes were made during this phase of the project. The design of the mount for the servo turning the ramp was changed in light of the mechanical failure we encountered, but the functionality remains the same.  

Schedule
Schedule – Gantt Chart
We are mostly on track. We plan to finish the remainder of integration Monday and work on documentation and testing for the remainder of the week before the demo. 

Progress Update 
Over the past weekend, we tested specific subsystems of our project. We were satisfied with many of our results; however, we also noticed that some hardware limitations constrained us. For example, we assumed when creating these specifications, we would have been able to achieve a control center latency of <400ms. However, upon implementation, we found that sending a command from the Jetson to the Arduino has a minimum latency of 2s. This does not really impact our project because we only require this communication when we have a lot more time to work with. For other parameters like the detection of different items, we are confident that with further tuning, we will be able to increase accuracy. We will repeat the tests we were unable to conduct and include the results in our final report once the system is fully integrated. 

Requirement	Target	Results
Control center Latency	400ms	(max) 2s
Maximum Load Weight	20lb	~20lb
Detection of different items	Metal, Plastic, & Paper with 90%	~77%
System sorting accuracy	90%	–
Item inference speed	< 2 seconds	1.15s
Overall system speed	12 items per minute	–
Final Cost	< $500	$514

Potential Risks and Risk Management
We ran into some issues while using higher conveyor belt speeds; specifically, the power supply we were using had a current limit of 0.25A, which was too low for the motor. However, we were able to acquire one with a greater current limit when supplying higher voltages, and this allowed us to raise our maximum speed threshold. Over the next couple of days, the only risks we anticipate are with respect to timing and synchronization. We believe that with our unit testing, which we will conduct this weekend, we will have a better understanding of the areas we need to further improve in time for the demo. 

Overall Design Changes
We did switch to a model trained from scratch to better meet our design and use-case requirements, but the model architecture is still the same (YOLOv5). Additionally depending on the unit tests and integration process, we may switch from stopping the belt temporarily for sorting the objects to going at a slower motor speed and not halting. Beyond that, we have decided to stick to the generic webcam used in the interim demo rather than use the Oak-D Short Range camera mentioned in our design report due to performance issues (we may revisit using the camera  given enough free time). Regardless, all changes are relatively minor functionally speaking. 

Schedule
Schedule – Gantt Chart

Progress Update 
This week, we made significant progress toward integrating the major hardware and software components of the project. On the mechanical side of things, the ramp was installed into the main system, and we were able to further tension the belt to avoid any rattling as the conveyor belt approached greater speeds. We were also able to transmit input (conveyor belt speed) from the user interface to the Arduino, and have a working live video stream of the inference and classification happening in real-time. We are currently working on testing the integration of the ML model with the servo actuation. With respect to the ML model, we were able to test inference on CUDA on the new Jetson firmware from last week, and despite having multiple compatibility issues, it was eventually resolved. We were also able to get the OakD SR camera working with color on the Jetson; however, since the model’s performance was subpar, we decided to stick to the regular camera while adjusting color settings for more optimal imaging. In addition, we looked at the dataset from the MRS-Yolo waste detection paper and trained a new model from scratch. At the moment, we are observing better object recognition.

Over the weekend, we also plan to conduct unit testing on the different subsystems of the project and fine-tune a lot of parameters for synchronization to make sure the ramp moves in response to the classification in a timely manner.  

Potential Risks and Risk Management

During the interim demo, we noticed that our timing belt was not measured correctly and did not have enough tension to turn the pulleys. This led the belt to slip from the pulleys under loads that were well below our maximum weight requirement. To fix this, John designed a tensioner (shown on the right) made from spare PLA and attached it earlier this week. The tension in the timing belt is sufficient, and the belt runs smoothly now.

We also noticed during our testing that the Jetson was running inference on CPU by default rather than CUDA (GPU acceleration), which likely stalled significantly in comparison. To remain on track with our inference and classification speed metric, we have looked into ways of enabling CUDA and managed to get it working by reflashing our Jetson with a pre-built custom firmware. 

During our interim demo, we noticed that at the moment, the model is not fully capable of recognizing the objects moving on the belt. This is partially due to a noticeable glare in the camera feed. Beyond further fine-tuning the model and adding filters to reduce the glare, we will have our servo default to sorting into the “trash” bin to prevent contaminating the properly recycled batches. 

Overall Design Changes
We had a minor design change when it came to the camera mount for the interim demo. Our initial design mounted the camera on the side of the conveyor belt, while the change has the camera mounted right above the belt. The change itself is subtle, but we believe it will help with integration as we were able to easily restrict the locations of detected objects to the approximate pixels of the belt. Additionally, the new positioning allows us to capture a larger section of the belt in each frame. 

Beyond that, we are removing the detection and sorting of glass from the scope of the project, as far as the MVP is concerned, at least. This was done due to multiple factors and issues we ran into, including safety precautions and the fact that glass data samples were extremely limited in the datasets used.

Schedule
We updated our schedule in time for the interim demo last week. We are now (mostly) in sync with the following schedule. 

Schedule – Gantt Chart

Progress Update    
Since carnival, we’ve made significant progress towards our project. We have a bulk of the mechanical structure built (as depicted in the image below). 

In addition to this, we integrated the motor and servo into the mechanical build, and for our interim demo, we were able to showcase all our subsystems (object detection, servo control logic, and user interface) pre-integration. 

Over the last week, we added finishing touches to the mechanical belt by installing the tensioner to fix the motor belt slipping. Additionally, we reflashed the Jetson and are now able to run CUDA successfully, which should make inference significantly faster. We were also able to establish communication between the Arduino and Jetson using Pyserial. This will be useful for when we configure the servo control logic using classification signals from the ML model. In addition, we were able to install all the dependencies for the web interface (see image below) on the Jetson and can successfully use simple buttons to toggle an LED onboard the Jetson. Over the next week, the goal is to be able to use user input to control the speed of the motor, build the ramp (we have obtained the necessary materials), and successfully use the servo to get it moving, and lastly, integrate the ML classification into the whole mechanism. 

Testing Plan

The use case requirements we defined in our design report are highlighted below. To verify that we meet these requirements, we have a series of tests that we will conduct once the system is fully integrated and functional. The tests are designed such that their results directly verify whether the product’s performance meets the use case and design requirements imposed or not. 

Requirements

System needs to be able to detect, classify, and sort objects in < 5 seconds

• Perform 10 trials, consistently feeding objects on the belt for a minute at a time. We will be able to verify that we met this benchmark if we are able to place at least 12 objects on the belt per minute. 

The accuracy of sortation mechanism should be > 90%

• Perform 30 trials with sample materials and record how many of these classifications are accurate.

System runs inference to classify item < 2 seconds 

• Perform 5 trials for each class (metal, plastic, paper, and waste) and record how long it takes for the system to successfully detect and categorize the items moving on the belt

Control center latency < 400ms

• We will perform 5 trials for each actuator (servo and DC motor), where a timer will start once an instruction is sent using the Jetson. The measurement will end once the actuator completes the instruction, that being a change in speed for the motor and rotation for the servo. 

Potential Risks and Risk Management
The risks remain the same as they were last week. We were hoping to get started with the conveyor belt build as soon as Monday’s lab, but some of the necessary components did not arrive by then. We will be working over the weekends to compensate. Additionally, we were unable to access some of our project’s remaining components as they were stored in ANSYS Hall prior to break due to ECE Inventory running out of red bins. We received word the building will be re-opened on Monday. 

Overall Design Changes
No major design changes have been made since the last Team Status Report.

Schedule
We are slightly behind schedule when it comes to the mechanical portion of the project, especially due to shipment delays mentioned in Progress Update. We hope to continue building over the weekend (previously labeled as slack time) in addition to beyond the scheduled class time to compensate. 

Schedule – Gantt Chart

Progress Update     
We have obtained the remainder of the project’s mechanical components as of Friday. Due to unfortunate shipping complications that were beyond our control, the belt’s physical infrastructure did not arrive by Monday. We did, however, begin familiarizing ourselves with the project’s actuators (the servo and DC motor) as they arrived on time. Furthermore, we have started 3D printing the necessary belt components using the 3D filament that arrived earlier this week.