so i made this chart using the calculations from the JVN design calculator to calculate the theoretical pushing power of teams robots neglecting CoF and weight to avoid the about 220 lb pushing limit most robots end up having. the scale is an open ended scale because each year different gearboxes and motor configurations are thought up. this ranks teams with a numerical value which is one or two numbers in the ones and tens place, with a single decimal (ie:12.7).
the numbers were decided putting Lightning Robotics team 862’s drive-train rated as exactly 7 for low gear. the scale is attached below with information from the Michigan Southfield, Escanaba, and state events, and obviously world championships Galileo division.
the point of posting? so other teams will have a chance to see it, comment on where it is wrong, and maybe adapt it for their own use in the future. if you have any questions feel free to post and ask.
(EDIT: sorry it says “team” i meant division, but i’m tired from worlds…)
if you are referring to the notes in the column marked as “notes” it was mainly used while scouting other teams to put the speeds that they told me their robot moved at, and usually also their wheel type if not some form of a traction wheel. sorry this was not made clear.
It seems that you are only considering number of motors and gearing, ie the robot with the most motors and geared the slowest will push the best. Is that correct?
I’m not really sure about this either, it also seems to only account for motors per side and not their configuration or the type of motor. Also there is no way to account for drive train inefficiencies.
Just curious what would our drivetrain, 6 CIMs and 2 MiniCIMs geared 12:72, 18:42 with 4" wheels in low gear, get in your system?
Also weight is really very important in who wins a pushing match. As is CoF, leaving those out don’t really allow you to compare much of anything.
@Joe Ross
that’s not nessisarily true, a robot with more motors will have more theoretical pushing power than one with less, while also having more speed. when talking about the same number of motors though, that is true.
@AllenGregoryIV
when you factor weight and CoF into these calculations it will cause most robots to have a max pushing power of about 220 lbs because their wheels will spin before their motors will stall. it is true that a lighter robot will be able to push less but the majority of teams are pretty close to their weight limit. now for ground friction, every robot of equal weight should have the same ammount of friction no matter how many wheels they have because force of friction= (CoF)*(Weight)
now on my chart your robot scores a 15.78 because it has theoretical pushing force of 1083 lbs and velocity of 5.4 ft/s. if i had put this in counting CoF and weight it would most likely end up at about 220 lbs, the limit because of how much friction you can get with a 120lb robot+battary+bumpers.
now for drivetrain inefficencys: if i remember correctly that calculated currently as the .81 (81%) in the two RPM columns, a figure i think i borrowed from what the JVN design calculator had. (this could easily be made into another column to be entered by hand, but how many teams know how efficent their drivetrain is?)
i hope that this helps clear some of the confusion up
Pushing matches in the “real world” have a lot more variables than max torque. Traction, drive efficiency (changes for each robot), bumper configuration, center of gravity, etc all have a large effect on each individual pushing match.
Plus lots of pushing is in the form of T-bones, where you just need a robot to be able to push a certain critical amount.
yes, and it is hard to calculate all of these; however this chart helps to quickly identify which teams in a match you should all together avoid trying to start a pushing match with. this chart is not supposed to be a super accurate but it does help. throughout the season my teams drivers only indicated two occasions where they were unable to push someone that we should have been able to push.
for simplicity’s sake there are many variable that were cut from calculations, or they were placed in as a constant. its hard enough getting the gearbox reduction ratio from 100 teams, and frankly most just do not know much about their robot.
EDIT: also i would like to factor in weight as it is noticeably the biggest area of error in this chart; however, there is no calculation that even needs the weight of the robot as it is only used in the friction calculations.
While CoF is pretty important, I would argue that most teams can simply swap out their tread for blue nitrile or another high-CoF tread to get good results.
Drive efficiency is pretty variable, but it’s almost negligible. A spur gear gearbox will have around 94% efficiency, so it really comes down to chain vs. belt drive. Even with that, the difference is less than 10%.
Drivetrain efficiency is extremely hard to calculate accurately anyway because of manufacturing tolerances.
Of course, this is not something that can be taken to heart, but for reference purposes it seems to do the job pretty well.
Drivetrain efficiency is that simple if everyone built perfect gearboxes, if you mess up the spacing of gears or have things out of alignment you can easily make your drivetrain much less efficient. Theoretical efficiency is one thing, real life efficiency makes a huge difference for a lot of teams.
The answer is all of them. The more time you spend in pushing matches the less time you’re spending scoring or helping your partners score, unless you’re delaying your opponent in which case there are more efficient defensivemaneuvers than direct pushing in most cases anyways. Frankly I don’t get the obsession with pushing matches that people seem to have. This year we ignored “pushing match” scenarios and went for speed and maneuverability. Even though we seeded lower than the last two years at both our tournaments, I’d still say that our robot at North Star was probably the best the team has ever produced.
Although, once you start getting 2v1’d and it becomes very difficult to maneuver through two bots zoning you out, sometimes it’s helpful to be able to just push through towards the goal.
The most important thing about this scale is that it is used with other data. It is highly useful, depending on what your strategy is, for your team, it looks like you focused (when you did defense) on the maneuverable defense, and focused on getting around opponents, instead of pushing through them. For my team, we used both versions (Pushing vs Maneuvering) on both offense or defense, depending on the robots on the field. I adjusted the strategy for my team, based on what I observed my opponents doing (I am the match strategy lead, along with a member of the drive team for 862)
In the beginning of the season (Southfield/Escanaba) pushing power took a lot more priority as the defensive strategy then maneuverable hitting of the shooter/blocking, mainly because the teams were not so experienced, along with almost a third of the robots at those competitions being rookies, who would only try to push you backwards. My drivers weren’t as experienced as they were later on when we used maneuverability, so getting around opponents wasn’t the easiest thing for them to do.
The data let me see who I could push into the low goal and shoot over (Which is what you saw 254 doing on Einstein), or who I could simply just shut down (again, we weren’t as good at the maneuverable defense at this time, we got better later on) while the ball was in my teammates zones, often we would just pass the ball, or finish it, then go back to shutting down a single opponent. When my robot was decided to be a finisher, I needed to know which robots had stronger drive trains, which when I compared to the other data that I got from my scouts, would determine who would be playing defense, I had to make the decision for my team on how to get around the defense. Could we push them to the wall and shoot over them? Or should we try to be more maneuverable, and attempt to hug the wall until we got to the goal.
Basically, early on, the data told me who to avoid and who we could easier defend. Later on, such as the Bedford competition. We became a target for teams to try to defend. This scale would along with watching how they played, would tell me how they would play defense, and how to beat them at it again. Whether I had to get around them, or push through them.
By MSC and Galileo, I used this quite a bit less, mainly for when I knew a team would be playing defense on my team, or the other team, and how to best utilize that.
In summary, this is useful for multiple things, depending on what your strategy is, not just the “pushing matches”. It really is just more information that you can use in your matches. The more you know about your opponents, the better you could prepare to beat them. Now, this isn’t the only data that I used, I also used Dragonking’s data that he would collect for me, along with LoneGhost’s data: http://www.chiefdelphi.com/forums/showthread.php?t=127040