Rockwell Innovation in Control

I was just wondering what Innovation in Control winners did to win their award.

Care to share?

I know 269 received it (Wisconsin) largely because of their ability to score in autonomous.

1124 won that award in CT this year, and I think one thing that might have helped is that our programmer made nice handouts with block diagrams to explain how the control system worked to other teams and the judges. Unfortunately, I’m not familiar enough with it to fully understand it or explain it here, but I’ll send someone along who can give you the details on what it did.

1640 won the award at the Chesapeake Regional for our 7th wheel, multiple field setable autonomous and the “heads up” LED display on the robot. Our co-captain Siri wrote a things we did posting.

From the letter we sent out to our sponsors

In presenting this award, the Judges cited a number of innovative design and control features, including the team’s unconventional approach to mathematics: 6 + 1 = 3.

The robot (DEWBot V) possesses a novel dual-mode drive-train which alternates between a 6-wheel drive tank and a unique 3 wheel drive pivot mode. This is achieved by adding a 7th Wheel at the back center of the robot oriented at right angle to the 6 main drive wheels. When driving normally, the 7th Wheel is held above the playing surface and does not affect driving. When the need to brake or turn quickly arises, however, the Driver can almost instantly drop and activate the 7th Wheel, lifting the rear four traditional wheels off the playing surface. This drive-train provides exceptional agility with intuitive control.

The team designed a field-settable autonomous operation; the programmed mode which controls the robot during the initial 15 seconds of each match. Two switches on DEWBot can be set to provide 6 different autonomous routines, keeping opposing players guessing.

Team 1640 also employs the vision system to detect when the turret’s shooter is aimed at an opponent’s trailer. Blue lights around the turret flash when a target is acquired. The Operator also has a “cry wolf!” button to lead opponents to think DEWBot has a lock on them.

2046 won at the Microsoft Seattle Regional for the use of the Axis camera on our turret shooter to locate the vision target orientation and distance to score in autonomous, and the use of skateboard wheels as the friction wheels to edge drive the turret and also for the multi-turn potentiometer which tracks turret position. Secondarily, we have gyro code which stabilizes the direction the turret points relative to the field during teleop. We had that indexed to the initial starting position in autonomous

1771 received the automation award at Peachtree for a combination of things: follower wheel traction control using a PID to achieve a desired slip ratio, Target recognition and turret tracking using the axis camera, along with a “locked on” indicator to tell the operator when the turret is in a position likely to score.

868 won at Boilermaker for a button that gives them more pushing power

Were they using fans? If not, I guess I’m just curious as to why they wouldn’t be running at maximum power all the time?

604 received the award at the Sacramento Regional for our ability to create a smooth servoing algorithm, despite the 30 fps limit of the camera, to auto-lock the camera on the trailer. Our driver used the autolock 99% of the time during teleoperated mode and our accuracy was very high.

Team 1504 won the award at the Kettering district for the driver controls on our crab drive. Nicknamed Waddle Drive, we track our orientation to the field with a gyro and use that data to always have control that is driver centric. No matter the orientation of the robot on the field forward on the control stick is away for the robot. This allows for simpler and easier driver control.

904 won this award in week 1 @ Traverse City - MI District Event. I believe it was for their implementation of “traction control” at the push of a button and also their autonomous mode that scored at least twice.

I’m no programmer, but this is what I know about this subject- acceleration in any direction follows the graph y=x^3, and we adjusted it so that there is no slipping, but it doesnt take a long time to accelerate. We have deceleration on a y=x graph, but theres also a PID loop that measures a floor encoder and a shaft on the gearbox, to compare shaft spin to floor distance traveled. If at any time the floor distance traveled is less than the motor spin, that means its skidding, and the programming will tell the motor to slow down accordingly. The PID loop allows good pushing too- because when being pushed, it virtually eliminates any unnecessary wheel spin, giving the robot nice traction.

Our camera tracks all the time too… For those of you who dont know our robot- its a turret with a camera above it that tracks a trailer and turns the turret accordingly. Sometimes it locks on other trailers we dont want to attack, but we changed manual mode so it turns the camera along with the turret. This way, when we switch back into manual, the camera can lock onto the correct trailer…

1075 won it at Greater Toronto for our 4wd (1 motor per wheel) 4ws crab drive, 2+2 style steering used in 4 different modes (Car, Monster Truck, Strafe, and Tank)

So in 2015 and 2016, what did your team do to earn the Innovation in Control Award? I quite curious…

In 2015 we won it at our district event by having a button that will quickly auto-align to the totes by moving a fork left/right to align the tote to our forks so we didnt have to maneuver around to get the totes, which helped a ton in the early weeks of competition where the scores were a tight 12-4, getting us 2nd qual seeding at that event.

In 2015, we autostacked totes. From detection to waiting for the next tote to come in was pretty much completely automatic, and we had one more tote in the stack when the next one slid down.

We won it in 2015 for optimized autostacking. We had two IR sensors, one two slow down the intakes once the robot had grabbed the totes to prevent bounce back and another one to sequence the totes.

From what I understand as a non-programmer - the 2485 2015 robot, Valkyrie used the vertical angle of our strongback as feedback to a PID controlling a motor to keep it vertical while the robot went over things.

In 2016, we won it for-
[li]Intricate autonomous modes
[/li][li]Trajectory planning
[li]Live setpoint-actual graph for tuning PIDVA loops
[/li][li]Both drivetrain and arm motion profiled
[li]High level of autonomous function
[li]Same vision tracking used throughout match
[/li][li]Driver instructs robot what to do, not how to do it. Shooter arm is all autonomous, from angle to reloading. The copilot just says to aim and if it is good to shoot.

In 2014, we won it for (starting to forget :o ):
[li]Robust software that actually accounted for mechanical failures and could be enabled to adapt in such a case
[/li][li]Two ball autonomous
[/li][li]Intelligent robot lighting (using a small coprocessor to drive them) that would alert the driver to robot conditions

In 2013, we won it for (don’t remember much :frowning: ):
[li]Anti-tip code on a robot that needed to be tippy for our climbing strategy
[/li][li]Good autonomous mode?

And every year, regardless of the robot, we won awards because our students could convey the robot’s functions with clarity and enthusiasm. If you can be really excited about what your robot can do, odds are the judges will too.

We won it this year at the Boston District Qualifier and the New England District Championship for our extremely consistent autonomous and automated vision shooting. Our lead programmer put in a ridiculous amount of work in making sure that our vision tracking (new for the team this year) allowed to consistently make shots anywhere from 4 to 10 feet out.

It also helped that we produced flyers with a write-up of many of the components of our control system, and handed them out to judges who stopped by our pit (see attached PDF).

Vision control system flyer 2.4 as pages (1).pdf (3.28 MB)

Vision control system flyer 2.4 as pages (1).pdf (3.28 MB)