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Collab Log #014: Water, Ice, and 3D printed parts: An experiment

We’ve begun some experimenting with PLA 3D printed parts to determine how they’re affected by exposure to water and ice, and to see if we can use a coating to make them waterproof.

Erin has designed an STL of a nalgene lid, and Beck has printed it. For the first round of testing, we’ll do the following experiments:

  • Placing a part in water for a set amount of time
  • Placing a part in water, freezing it, thawing it, and repeating it.

Next we’ll try the same experiments, but we’ll treat the part with sealant first.

In this first round of testing, we used a plastic tupperware container to freeze the part.

Surprisingly, it survived fairly well, even without any surface treatment! One small crack occurred on the inside of the cap.

Frozen lid!

Small break around center piece,

Just a small break!

Collab Log #013: 3D printing updated IR camera pieces

Collab Log #013: 3D printing updated IR camera pieces

Today we started 3D printing the updated CAD model pieces from the uOttawa group! Just like a teleporter, from their computer to reality over here. It’s great! These pieces will be used to assemble the complete IR camera pieces and mount. The Raspberry Pi goes onto the case and into the shell, which mounts onto the top of Bowie’s chassis.

Next update will show the complete assembly of this, stay tuned! And good luck to the uOttawa team this week with their presentation!

Collab Log #012: Reviewing CAD model

Collab Log #012: Reviewing CAD model

We met with some of the uOttawa group and discussed the CAD model and the object detection training model. It was interesting, for the object detection model, there are some out there, like this one on kaggle for garbage detection. If we can build on this, it will help get ahead faster.

The Garbage Classification Dataset contains 6 classifications: cardboard (393), glass (491), metal (400), paper(584), plastic (472) and trash(127).

The CAD model is ready to be printed! We have a printer here, and this seems quite like teleportation. We will begin printing it asap (waiting on some PTFE oil before starting a long print).

Beck is still working on the drive system instructions, but no progress was made since last update.

The uOttawa team has their presentation coming up soon! Thank you to their effort this semester!

Collab Log #011: Virtual meet

Collab Log #011: Virtual meet

Our first virtual meeting! This week we went over button parsing for the Operator Interface, and discussed about the RPi mount for the pan-tilt and IR camera. We had a few people from the uOttawa group connect, it was good to test out the technology. We used Jitsi Meet for the video call, it is open source!

Collab Log #010: [VIDEO] Water bottle detected! (in IR)

Collab Log #010: [VIDEO] Water bottle detected! (in IR)

There was a good amount of progress that culminated this week!The uOttawa group got Tensorflow object detection working with the IR camera attached to the RPi! We were able to see what the objects were being detected as. This was just using the standard MobileNet model, and there were results such as surfboard in there too.

Beck completed the Bowie Brain Kit! It is now completely soldered. Next step is programming the first blink! As well, we had the chance to meet someone new, Queenie, who was interested in learning more about Robot Missions.

Hooray! It’s great to see it when progress meets a milestone. Congrats to the uOttawa team!

Collab Log #007: Pan-Tilt Mech Printing, Progress on Brain Kit

Collab Log #007: Pan-Tilt Mech Printing, Progress on Brain Kit

The uOttawa Masters Engineering group made progress on 3D printing the pan-tilt mechanism for the IR camera. Beck gave a tutorial to the group on how to 3D print with the Tinkerines and how to slice the models. Meanwhile, another member of their group worked on installing Tensorflow on the Raspberry Pi. The servos fit perfectly into the mount, and it gets assembled with M2 screws. Beck and Brenda made progress on their Bowie Brain Kits as well. Brenda had a thought about a 3D object detection method, so we brainstormed a bit about that on the whiteboard. Next steps is to continue the work.