We here at Team 900 have a favor to ask. Can you show us your balls?
We’ve been hard at work training our vision system to recognize our own balls and I can safely report that we’re beginning to get a feel for them. That being said, we’re always looking to the future and we will need to be able to spot balls other than our own.
We would like pictures of your balls resting on various surfaces. We would like to see your balls on your field elements. Videos of your balls moving around would be helpful too. Some teams have been uploading teaser videos of their balls flying in the air and those are great.
If you have access to a full practice field and can get footage of balls being played with on it from a robot’s viewing angle then that would be awesome!
If you have damaged your balls in the course of your prototyping then we would love pictures and video of that too. We would of course like to see all teams treating their balls with care but we understand that incidental damage to balls can occur during rough matches and would like to prepare for that too.
(Yes, we really would like pictures and videos of the game pieces/boulders/balls.)
(Yes, I will report you to the admins if you take the opportunity of my carefully worded post to send me inappropriate pictures, get your mind out of the gutter!)
You do realize that you could have avoided all possible retaliation in terms of inapropriate responses by calling them by their PROPER name, Boulders, right???
(G39 avoided the fate of the identical rule from 2014 by just that method…)
I presume that you guys will be using some learning algorithm to find balls in your image. Congrats if you get it to work, that is no easy task. While I don’t have a dataset to give you of my own, you might be interested in taking your learning algorithm a step further and computing the “DeepPose” of your balls from images. Remove the middle man if you will. Your algorithm last year ran at a speed that left some to be desired. The jetson boards can handle CNN models like no other (thanks to their cuda cores), with inference that is, and not necessarily with training. If you have a CNN with, say, 3 convolution layers, it would run at well over 30 fps.
We’ll try to get some pictures up tonight! We got some damaged ones actually.
Shall I PM them to you? Or through mail?
In week 6 we have a scrimmage going on with some EU teams and we will be playing (hopefully) some matches. Let me know which angles u want shots from and we could send it your way.
Our field is from wood fyi.
During a practice match in 2012 our drives team snuck one out into the field and threw it on the field during the endgame. They should have game them a red card to carry for the rest of practice day.
Would it be possible for other teams to use the pictures and videos you receive so we can also use deep learning and neural networks? As Ron said we have some damaged balls and more new ones on the way, as soon as we have those we will upload more pictures.
In all seriousness, can people post pictures of how the game pieces are dealing with wear and tear for those of us who are thinking about using mechanisms designed to squish the ball when scoring.
This is actually surprisingly significant and here’s why:
For a stencil, I used an old aluminum plate with our number milled into it. I took the first boulder that we had been testing with for the past couple weeks, and pressed the stencil against the foam to compress it so all the edges were in contact with the boulder surface. This is to keep the edges crisp. I was able to do this by myself with my left hand pressing the stencil down and my right spray paining. I took the next boulder, which had never been used, and found that I could not press the stencil hard enough to compress the boulder so that all edges touched the boulder surface. I had to ask a student to press the stencil down while I sprayed, and eventually after getting a few boulders painted, he was showing signs of fatigue and his arms were shaking a bit.
The moral: The new balls are much, much harder to compress than ones that have been used over and over.
