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Team Status Report for 10/3/2026

For the week of September 27th – October 3rd, our team made significant progress toward finalizing the Iteration 1 design of our 6-DOF robotic arm. Our main focus was completing the mechanical design, validating the robot through simulation and torque analysis, finalizing the hardware parts list, and preparing for physical implementation when our ordered parts begin arriving.

A major area of progress was remodeling our robot around the dimensions and geometry of the UR3e model from last week. We used the UR3e’s dimensions as a reference for our own kinematic model, allowing us to use a proven robot configuration while adapting the design to our 3D-printed links and selected smart bus servos. The updated SolidWorks assembly now contains modeled representations of the purchased smart bus serial servos, power supply, shafts, and other components. The end effector was also modified to support a pen for our intended testing.

We also performed several simulations to validate the updated design. FEA stress testing was used to identify and strengthen weak areas of the printed components, while MuJoCo was used to visualize the robot and verify its motion and geometry with the SolidWorks assembly. Simultaneously, Python kinematic and torque analysis tools were used to evaluate loads at every robot joint and confirm that the purchased servos can support the weight of the robot arm. The results of these showed that the shoulder and elbow joints are near the practical limits of our selected servos at the full UR3e dimensions. To address this, the current design uses two servos at each of these joints, along with a reduction mechanism and counterbalance spring to reduce the load and side forces on the servos.

On the software side, we continued developing our ROS2 environment on the Jetson and established the project’s GitHub repository and initial software structure. We also determined that OpenCV AprilTag detection is a better fit for our vision system than using a YOLO26 model, since AprilTags can be detected directly (using the apriltags OpenCV library) without training an object detection model. This should provide a simpler and more efficient solution for our Jetson Orin Nano and Intel RealSense D455 camera setup.

Finally, we worked together to finalize the Iteration 1 master parts list and identify remaining hardware such as bearings, screws, shafts, tubing, and additional filament. With the mechanical design and simulations largely complete, our primary risk is now transitioning successfully into fabrication and assembly. The first print runs are expected to require approximately 35 hours and 900 g of filament, so we will need to carefully prioritize the initial parts and quickly iterate on any components that require redesign.

Our project is slightly behind the original schedule since the CAD design and subsequent validation took more time than anticipated. Therefore, we have had to push back most of our schedule by about a week, so printing and assembly will commence this following week.

For next week, our team plans to:

  • Begin printing the finalized Iteration 1 components, starting with the base.
  • Measure and test initial prints before committing to the full build.
  • Order any remaining hardware and additional filament required for assembly.
  • Begin assembling the printed components and servos as parts become available.
  • Continue developing the ROS2 framework, including the URDF and tf2 structure.
  • Begin prototyping AprilTag tracking using the Jetson and RealSense camera.
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Bryan’s Status Report for 10/3/2026

For the week of September 27th – October 3rd, I focused on helping finalize the Iteration 1 system design and narrowing down the CV approach for our 6-DOF robot arm. In addition, I helped finalize the master parts list, including the required components, quantities, and pricing. With our overall Iteration 1 design done, we are now in a better position to move forward with more implementation-related work.

One of my main areas of research was determining the best approach for AprilTag detection. After looking over documentation and comparing OpenCV-based AprilTag detection with using a YOLO26 model, I determined that OpenCV is the better choice for our application. Since OpenCV already has a library dedicated to detecting AprilTags, a YOLOv26 model trained on these tags would be less appropriate. OpenCV should also provide a simpler and less computation-heavy on the NVIDIA Jetson Orin Nano while integrating well with the Intel RealSense D455. This approach will allow us to focus the vision system on reliable AprilTag detection and tracking without adding unnecessary model-training time.

I also continued working with the team on the overall Iteration 1 design and component selection. With the parts list now largely finalized, we have a clearer picture of the electrical and mechanical components required for the system. This will help us move toward integrating the camera, computing hardware, servo motors, and other components into the final design.

For next week, I aim to:

  • Prototype OpenCV AprilTag detection and tracking on the NVIDIA Jetson and Intel RealSense D455.
  • Start configuring motors and power supply once they come in.
  • Continue refining the Iteration 1 design and preparing components for system integration.
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Bryan’s Status Report for 9/26/2026

For the week of September 20th – 26th,  I focused on continuing my research and development into Iteration 1 of our 6-DOF robot arm. After initially choosing a YOLO model for AprilTag (used for calibration) detection, my team and I discussed using the NVIDIA Isaac ROS or OpenCV instead, since it is more optimized for AprilTag detection. However, since our design has not yet been fully finalized, much of our final electrical components have not been finalized yet as we do not have accurate enough data for torque calculations. In addition, we are still investigating whether a dedicated microcontroller will be necessary for our final system.

One of my main areas of research this week was determining how AprilTag tracking could be incorporated into our system. I investigated using OpenCV-based and Isaac ROS AprilTag detection approaches as alternatives to using a YOLO-based object-detection pipeline. OpenCV would potentially provide a simpler solution for identifying AprilTags, since the architecture is quite easy to use, which cannot be as easily said about Isaac ROS. This could simplify the computer-vision pipeline compared to training a YOLO model specifically for AprilTag detection. However, I am still evaluating the tradeoffs between the approaches, specifically in terms of detection accuracy, implementation ability, and how well each method would integrate with the NVIDIA Jetson Orin Nano and Intel RealSense Depth Camera D455.

I also continued investigating the smart bus serial servo motors that we discussed last week. While Hiwonder, Feetech, and Waveshare are still candidates, I was unable to definitively select the motors because our team has not yet finalized the Iteration 1 design, and as a result, we do not have an accurate estimation for the required torque at each joint. Therefore, we decided to hold off for the time being to confirm our final Iteration 1 design, since prematurely ordering motors that are too weak to support the joints is a real concern. In the meantime, I will continue searching more approved vendors such as DigiKey to source smart bus serial motors from.

Overall, this week was primarily focused on narrowing down the computer-vision and embedded-system architecture while continuing to work with my team on the overall design. Since our Iteration 1 design is still a work in progress, my work has consisted more heavily of research and comparison instead of final component selection. Once the mechanical design is finalized, I will be able to use the resulting simulation data to make appropriate decisions about the servo motors, power requirements, and camera placement.

For next week, I aim to complete the following:

  • Continue comparing OpenCV AprilTag detection against YOLO-based detection and determine which approach is better suited for our system
  • Test or prototype AprilTag tracking using the NVIDIA Jetson and camera setup.
  • Collaborate with the team to finalize the Iteration 1 mechanical and electrical design.
  • Use updated simulation data to determine the torque requirements for each smart bus serial servo.
  • Narrow down and finalize the specific smart bus serial servo motors for each joint.
  • Assist the team with refining the master parts list, including component prices, links, specifications, and quantities.
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Bryan’s Status Report for 9/19/26

For the week of September 13th – 19th, I primarily focused on researching electrical and embedded-system components required for Iteration 1 of our 6-DOF surface-drawing robot arm. My work centered on identifying suitable power supplies, smart serial bus servo motors, microcontrollers, and supporting drivers. In addition, I investigated object-detection models that could run on the NVIDIA Jetson platform, specifically the YOLOv26 model and the process of training and deploying the model using Google Colab.

In order to provide adequate power to the NVIDIA Jetson Orin Nano, Intel RealSense Depth Camera D455, 6 smart serial bus servo motors, and possibly a microcontroller, I found that a MeanWell LRS-350-24 24V, 14.6A, 350.4W power supply would be the safest bet, paired with a fuse distributor; it would be easier to have a pair of wires from the power supply to the fuse distributor than to have multiple wires coming straight from the power supply. Given  that 24V may be too much for certain components (namely the Jetson), I figured that we would also require a 24V to 12V step-down converter to avoid overcurrent.

The biggest change from our original concept to the Iteration 1 design was the transition to using smart bus serial servo motors rather than traditional stepper motors. This is because these smart servos provide live position data, which we would have otherwise needed to reverse-engineer using traditional servos. For a project like this that requires exact precision, smart servos would save our group much-needed R&D time that could be used for testing and verification. As we do not yet have accurate simulation data from AutoCAD, we can only estimate our torque requirements. From my preliminary research, I found that Hiwonder, Feetech, and Waveshare offer affordable servos within our budget; I found Robotis Dynamixel servos cost upwards of $100 each, which is unsustainable.

Our team also explored the prospect of using an object detection model to run on the Jetson, for the purpose of identifying different color pens to pick up. Although this is not yet an official requirement, I conducted research into how to implement this. After comparing current commercial object detection models such as SSD, YOLO, DETR, and Faster R-CNN, I established that the only model to combine high accuracy, real-time detection, and compatibility with the Jetson Orin Nano platform was the YOLO family of models. More specifically, there are a variety of YOLOv26 models that are available based on inference speed and GPU strength. Since we do not require many detection classes, the YOLOv26n (nano) model will be sufficient.

Reinforcement learning for the model will be performed on Google Colab, since high-end GPU accelerators are free. To create the dataset, the workflow that Ultralytics provides will be sufficient, since it allows for drawing specific detection polygons onto input images and the simplified creation of different detection classes. As for implementation on the Jetson, I would use OpenCV to grab frames from the camera feed, run the trained YOLOv26n model (exported to a TensorRT-optimized engine file), then output coordinates for the object detection box.

For next week, I aim to complete the following:

  • set up Ultralytics workspace
  • start training YOLOv26 model on dataset with different color pens
  • collaborate with team to finalize design of Iteration 1 and obtain accurate simulation data for servo torque requirements
  •  assist team to refine master parts list into spreadsheet with prices, links, and any other information
    • specifically finalize brands of smart bus serial servo motors