Tag Archives: robot

FIRST Tech Challenge: Iowa Championship Competition

First off, my congratulations to the students of FIRST Tech Challenge team 5445, the Trohawks, for making it all the way to the Iowa State Championship yesterday!  Buster (the robot) performed wonderfully.  He managed bowling balls, racquetballs, and crates, all while navigating the FTC arena war zone.

Although we didn’t quite make it to the national competition, we certainly had a lot of fun competing.  Although I don’t have any video this time (lighting was atrocious), take a look through these exciting action shots from our five matches (mouse over for captions):

The FTC Game Arena (Notice the spot lighting? This just didn't agree with my cell phone video camera.)

Tipping Crates (They had to be uprighted, of course, before we could put racquetballs into them.)

Raising a Crate (We were only able to manage this once in five matches, but when we did, it was an easy win.)

Tangled up with our alliance partner (Somehow they fell off the ramp in autonomous mode, causing us to miss the bowling ball.  We did, however, barely make it into the parking zone for some points.)

Buster’s Debut: The FTC Davenport Regional Qualifier

As I’ve mentioned in the past, I’m a mentor for a local FIRST Tech Challenge team (Go Trohawks!) here in Waterloo, IA.  For the past two months, my students have been busy designing, building, and programming Buster, their robot:

Buster: Competition Ready!

This past Sunday, we were put to the test at the Davenport Regional Qualifier.  The students did exceptionally well.  They impressed the judges with their knowledge and teamwork, their robot consistently captured their team’s bowling ball (both autonomously and via remote control), and they somehow managed to survive the bad jokes and the cha cha slide performance of our MCs and referees.

calm before the storm...

We did have a little trouble with the racquetballs though.  Those little devils kept getting stuck in our drive mechanisms.  Usually it was possible to rock the robot back and forth a bit until they dislodged, but it still caused us quite a bit of delay.

Now our main goal, as you may have guessed from the first picture above, was to retrieve and park our team’s bowling ball.  This is something we can do quite well both autonomously and via remote.  However, we were so good at capturing the ball that we were left without much to do for the majority of the competition.  We’ll have to think of ways to deal with avoiding/collecting racquetballs for the next meet.

At the end of the day, we wound up ranked seventh out of twenty-one teams.  Not bad considering the fact that none of us had ever done this before.  And yes, I did manage to capture several of our matches on video using my BlackBerry Storm.  Unfortunately, the video quality is very poor, and nowhere near HD resolution.  Nevertheless, I present to you our fourth match.  Feel free to comment below!

FIRST Tech Challenge – Buster’s Perspective

So recently I’ve been having fun acting as a mentor for a local FIRST Tech Challenge (FTC) team.  In case you haven’t heard of FTC, or any of the other FIRST (For Inspiration and Recognition of Science and Technology) robotics programs, they were created by Dean Kamen (inventor of the Segway, among other things) in an effort to get kids interested in careers as engineers and scientists.  They are, in essence, sporting events for robots, like this one we call Buster:

Buster the Robot

This is a robot developed by the Waterloo Upward Bound team (we don’t have an official name yet).  Actually, this is just the prototype version of Buster.  Our real robot is still in development and isn’t quite driveable yet.  Fortunately, we have this very simple prototype which the students can use to create and test their operating programs.

Buster will be competing in a competition which lasts just two and a half minutes.  For the first thirty seconds, he must act in complete autonomy – no human interaction is allowed.  After that, the students can use up to two different controllers to navigate around the arena.  Check out this page for details and video of this year’s challenge.

So I wanted to just quickly post a video I shot from on-board the robot this afternoon.  In this clip, we’re testing autonomous operation.  Buster is driving completely on his own using an ultrasonic rangefinder and a light sensor.  The ultrasonic sensor is always visible in the video, and points forward attempting to detect obstacles.  The light sensor is not visible, but points at the ground and helps Buster avoid dark-colored objects on the floor (green tiles, the black mat, and dark spaces).  When a wall is encountered, Buster picks a random direction and turns until no obstacles are in range.  At this point, he drives forward once again.  When a dark-colored object is detected on the floor, the robot stops, reverses, turns slightly to the left, and continues forward again.  Check it out:

Pretty neat perspective, right?  Feel free to post comments and questions below!

Talking in Toronto: IEEE CASE 2010

Last weekend (August 21-24) the sixth annual IEEE Conference on Automation Science and Engineering (CASE) was held in Toronto, Ontario. So I, being fortunate enough to have the General Chair as my thesis advisor, was invited to attend. Actually, I did more than just attend, I presented a portion of my MS thesis to a group of about 30 people. I have to say, it was pretty exciting. Now I think everything went well, but it’s hard to be objective about your own presentation. Plus, when you’re up there talking (and when you have no means of judging time because your cell phone died), time seems to run faster. Toronto was an interesting place – it reminded me a lot of New York City, actually. This was my view from the hotel room Sunday morning:

Toronto, Ontario

If you’re interested, my paper’s abstract can be found here: CASE 2010 Paper Abstract. It was given the very exciting title, “Application of 6-DOF Sensing for Robotic Disturbance Compensation.” As a quick summary, the paper begins with a discussion of my work developing a 6-DOF (degree-of-freedom) laser-based sensor. This custom sensor measures not only the position of an object in 3D space, but also its orientation – pitch, roll, and yaw. Its positional accuracy is as good as 1mm over a 15m x 10m x 1m area. The end application for this sensor involves measuring the base position of a robot and using that data to stabilize the robot’s end-effector at a desired location. Here’s one example we whipped up to show the robot’s motion with and without compensation:

Disturbance Compensation ExampleThe robot in use here is the Stäubli TX90. It’s a six-axis industrial robot capable of moving a 25kg payload within its 1m reach. In this example it’s mounted to a freely oscillating platform (think diving board) along with our sensor package. The sensors measure the position of the base, then relay that information to the control system. The control system makes the appropriate adjustments and calculations, then sends corrections to the robot.

In the picture above, the robot was first programmed to draw a house shape using a red dry-erase marker without disturbance compensation. That is, its base was oscillating, but the end-effector was not compensating. Then, the compensation system was enabled and a black marker was used to draw the same shape. As you can see from the above image, after an initial “turn-on” squiggle, the black line shows very little error compared with the red line. This is the ultimate goal of the system – to maintain a desired position despite potentially large motions in the base of the robot. In this case, the base was moving by up to 50mm while the end-effector was stabilized to within 2mm.

I’m looking forward to writing more about the inner workings of the sensor system at some point in the future (particularly after our patent clears the USPTO). So, until next time!

P.S. Driving across the US-Canada border is no picnic. That is, unless you brought food and drink, in which case just roll a blanket over the hood of your car and settle down for a bite. Actually, I only had to wait about 30 minutes to get into Canada and 40 minutes to return to the US. My friends on the train, however, were stopped for a full three hours on the return trip while everyone was processed. Anyway, advice for anyone stuck in this situation: don’t change lanes. It never helps, plus it frustrates the agents and pretty much every driver in your vicinity. Yay for courtesy! 🙂