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! :)

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