Hey, Cool Drivetrain!

This thread is dedicated to appreciating teams who have displayed fast, smooth, robust, maneuverable, or powerful drivetrains in the 2022 season. If you’ve noticed a team (maybe yours!) that has put a lot of effort into their drivetrain, show your appreciation by acknowledging them in this thread!

Of course, the choice of drivetrain does impact the qualities of robot movement a team is aiming for, so feel free to point out what you think is most impressive about their design/code (or simply applauding their great drivers!)

(if this is a replicate thread, feel free to post the original!)

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5687 The Outliers have an incredible differential swerve. They were more than happy to show a module and explain how it worked and they challenge making it work; maybe someone from their team will pop in to this thread.

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4143 has a Differential Swerve as well. We are at Champs at pit D02. We’d love to answer any questions and show off a few of our modules we’ve been working on. Feel free to stop by!

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Are you seeing clear performance advantages with diff swerve over conventional swerve?

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I think we are equal to a traditional cots swerve this season. We might be able find an advantage next iteration.

Would you mind to share some pics? I’m surprised this hasn’t gotten more attention

This is our current gen model. We have some older generations on showcase in our pits as well.

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5687 developed a differential swerve last year during the pandemic. It was in large part driven by one of our programming students, who did an amazing job developing the controls for it. You can find a link to our CAD on a CD post we made last year: 5687 2021 CAD release

Before I get into any details about the swerves, I should probably share some of the lessons we learn while developing them. Differential swerves require a lot of work to develop, and most teams would not see any benefit from them. We build heavy robots that are usually right at max weight. Even still our swerves are traction limited. Given how powerful the brushless motors are, if your robot is not at max weight a traditional swerve is probably traction limited as well, so the extra torque is wasted.

One of the major down sides of differential swerves is that turning becomes difficult to control at high speeds. When turning, the robot is essential a differential - add differential swerves to that, the the difference is speed of the fastest motor on the swerve located on outside of the turn, and the slowest motor located on the inside or the turn become very large, and it becomes impossible to turn rapidly at high speeds.

However, there are some benefits. We definitely notice the added traction when pushing (again, we are at max weight), and the acceleration is impressive. Also, each motor draws little current - under normal operation a little over 10 amps, and underload they usually run at 20amp. Additionally, each motor is operating in a more efficient region of their power curve. Lastly, because the load is shared between two motors, these modules have held up very well. We never had to replace or even repair a module all year (more on this later).

Would we recommend developing a differential swerve? For most teams no. I don’t think there is much of a performance advantage (unless you really need the torque), and there are significant disadvantages. Additionally, they take a lot of time to develop properly and that energy that would go into fussing with the swerves is probably better spent elsewhere. However, if you have some students that really want to spend a few years tinkering with mechanical drive systems or explore complex control systems, then it offers a great learning opportunity.

About our swerves. Our priorities in designing them were:

  • Keep COG as low as possible
  • Keep top below the standard bumper height of 7”
  • Maintain a 1.5” ground clearance
  • Use as many COTS and 3D printed parts as possible
  • The translation (wheels) ratio is 6.47:1 and the rotation (steering) ratio is 9.2:1

We have gone through about 5 interiations on the design, and will probably do one or two more this offseason. Last year we were constantly breaking components, and each iteration was more robust, until the current interaction, which seems to have held up well. But, that is what made it such a great project to do while we were remote during the pandemic. Below are some CAD images:

Here is a section view:

And here is a picture of our fist iteration (I don’t have a picture of our current iteration right now):

Please let us know if you have any questions.

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If you have 2 motors per module that can be applied to the translation, at this ratio, you are going to be traction limited at motor stall. For 8 motors, 1.2 CoF, 4" wheels and 145 lb robot weight you need to get down to 4.5:1 - 4.7:1 range before you are no longer traction limited. At 4.5:1, your top speed would be in the neighborhood of 20 ft/s.

So, it seems to me that 6.47:1 does not really take advantage of having all 8 motors able to be applied to the wheels. Did you consider gearing it for higher speed? Can you share your rationale for why you selected this ratio?

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That is very astute. We had similar calculations, but chose 6.47:1 for several reasons. First, our drivers never went full speed and even at 6:47:1 (we actually limit top speed in code) so there was no reason to go faster. More importantly we wanted to put less demand on the motors and battery. Lastly, while we never drove close to top speed, we did like the acceleration of the higher gear ratio, and prioritized that over speed.
However, we are looking at experimenting with a lower gear ratio this off season. Not to increase top speed, but because we think it might improve controlling turning at speed.

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jack in the bot has such a good swerve drive in my opinion. They’re robot is really fast!

From what I understand, one of their mentors is in charge of SwerveDriveSpecialties, and they are running the same mk4i modules most of the other exceptional teams are. But they really deserve extra credit for developing them and iterating them from 2017 till now.

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