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DARPA Announces 2005 Grand Challenge Semifinalists
Mockingbird writes "DARPA announced 40 semifinalists for the 2005 Grand Challenge autonomous robot race today. Notable remaining teams include the Carnegie Mellon University Red Team, Stanford Racing and a high school team, the Palos Verde Road Warriors. 78 teams missed the cut. The race, which will take place on Oct. 8, 2005 features a $2 million prize for the first team whose robot crosses 175 miles of the Mojave in under ten hours. The robots must be fully autonomous, with no team intervention allowed once the vehicle is launched. The first race was held in 2003, when the most successful team managed to log only 7.4 miles."
The Roomba actually only made it 4 miles, but it cleaned up the competition...
(groan)
I have entered my Terminator this year as I think he needs to interact with other machines more, I still expect him to destroy all the other competitors but a day out and some challenge-response kaboom action will probably do him no harm. Also if your name happens to be John Conner I would recommend staying away from the competition site.
Do not try to read the dupe, thats impossible. Instead, only try to realize the truth
What truth?
There is no dupe
Today's Indianapolis Star. The mention of Scott Jones - the guy who invented voicemail - has a good project background.
People have been coming from all over the state (literally) to work on the project (just down the road a piece) on a very regular basis, just for the fun of it.
I've talked to several people who have been tinkering with it and are having a good time. Sometimes, bordering on obsession.
while(true) { follow_road(); }
I'm a coder, not an AI or image-processing geek, so these might be dumb questions... but...
Why the need for so many sensors? I can understand a use for them in low-visibility, eg dust or darkness, but the current models seem excessive to a layman. I mean, can one not use steroscopic cameras (scanning the field, as our eyes do), run edge and shade detection over the frames, and generate 3D terrain models in real time?
How does a vehicle determin terrain density and route selection? Can terrain texture be estimated based on reflection or image matching, so the vehicle can decide not to drive over some water or a bog, for example?
Even a good human driver is going to get stuck in the deset without learning how to handle a truck offroad. Is it feasible to train a neural-net system to select a likely course, possibly with a set of hardwired rules as a base? Eg, make your own way, but don't sink the car.
I've no doubt this stuff is Hard, but much of this appears to be done via brute force...
Forget thrust, drag, lift and weight. Airplanes fly because of money.
As if a DARPA authorized vehicle is actually going to get pulled over by the police? Haha, I can see the cop walking up beside that one now.....
If you can't say something nice, make sure you have something heavy to throw.
Solar powered Gauss rifle? That must take what, like several years to build up enough charge?
Real_men_don't_need_spacebars.
Legos are very patient.
My other car is a Popemobile
Whenever the offence inspires less horror than the punishment, the rigour of penal law is obliged to give way...
Team DAD's vehicle was street legal last year. It created a pretty funny situation. They brought it to the raceway the first day and were told they had to impound the vehicle for the duration of the QED. They asked "How are we going to get back to the hotel?" DARPA rented them a car.
Of course. The purpose of the military is to kill people and break things. Ask any Marine. If you can't accept that, you shouldn't be in the arms business. Entering the DARPA Grand Challenge is being in the arms business.
The Golem I last year finished fourth, travelling 5.2 miles. It had the lowest budget of only $35,000 dollars (whereas some other teams' have a reputed budget of over a million...). And based on this image here, what I believe makes it uber-awesome is that they are cheating the competition by installing an elf under the hood and letting him drive.
Not 2003...
Kevin Fox
I've met the Team Dad people. I'm very impressed.
I'm studying a course in 3D Computer Vision right now, at TUHH. It's part of the Erasmus exchange program I'm having here - the eigth and last semester (excluding the thesiswork) of my master of engineering in automation and mechatronics at Chalmers in Gothenburg. I can easily say this course is the most difficult one of all I've been taking for all of my study time, hopefully the three weeks I have between that exam and the last of my others, will be enough to learn what doesn't stay in my head during the lectures...
In fact, I have the course book right beside me. To begin, the description of it would be more or less along the lines "an orgy in linear algebra, mathematical statistics, with some flavouring of image processing, geometry, optimization and algorithms". Basically, it's 30-40% mathematical formulas, 650 pages, some containing things not even all MSc even learn like tensor notations etc. Not something I'm even sure is a good thing to recommend to very many slashdotters, even. You'll get its name though - "Multiple View Geometry in Computer Vision", by Hartley & Zisserman. ISBN 0-521-54051-8.
What I see as problems in the book, is that almost everything is working on corner detection. This is great, if you want to make 3D-models of houses or other man-made objects (at least half of the examples in the book are architectural, I would say). It's not so great if you want to image bushes, rocks and other things with not so obvious corners on them. Also, the process involves quite heavy processing - both image processing, finding all those corners, statistical processing (to sort out outliers, which there will be), and optimization to find the best fitting backprojection of the image planes). I don't have a sure grip on the needed processing power but I doubt, when considering realtime demands in a car, that it'll hardly be easy to get it working.
Also, it's still to a big deal itself an area under research. The situation with using 5+ images (from different cameras och just consecutive images from the same, moved camera), isn't very well known. Using more images, of course would mean a bigger chance to get a decent 3D model of the scene...
And still, you would at least need two cameras to do anything useful. You can't reconstruct 3D space without having at least two images of the object to reconstruct. And probably you will need more - you would probably want to reconstruct all the way around (ie more cameras on the sides and backwards), and add extra sensors like radar etc for extra checks.
And then you really haven't solved the problem of driving the car. You have only built a decent mapping of the 3D surroundings of it. You have to add AI/some kind of steering logic, which only in itself is a demanding task. Just look at all FPS games out there - if it would be easy to construct good AI, with a known 3D-world, tailormade for the figures, would we really be seeing that many games with crap-AI? I'm happy I ain't taking an AI course too, for sure!
I have a really elegant proof for Fermat's last theorem. If this sig was only a bit longer...
Blue Team
Wired magazine has an pretty funny article on the results of the 2003 race with a description with what went wrong for each team.m l?pg=15
http://www.wired.com/wired/archive/12.05/start.ht
DARPA, and the DOD would *love* to have semi to full autonomous "kill bots" - in a way, today, they already have them for some tasks - they are called "cruise missles", which can be launched, told to stay on "hold" above possible targets, then commanded to strike on located targets. I would assume "located" likely means some form of lat/lon coordinates or painted with a laser (either by troops or from the air).
The exact same thing could be done with a kill bot: send it to a predetermined position, and tell it to "hold fire" unless acted upon agressively, or if non-friendly comes into position (at which point it could bark a series of commands in different languages to the offender - think of it as an active landmine with intelligence that can move on command), which if not heeded, shoots a warning, then if continued, shoots to kill. Friendlies are identified by RFID or similar tags. Equip them with the ability to identify each other, as well as to flock or coordinate efforts with one another. Other commands could be something like "fire on ident", where they could be set up, then when a target is painted with a laser (perhaps from a troop's rifle), it fires on that target.
You better bet that the DOD and DARPA would be all over such a system if it was proven field safe (to our troops) and easy/quick to use, and rugged. They are half way there with the TALON robots already, they just lack the rest of the package, which the Grand Challenge is dealing with...
Of course, one can also easily see the potential of scaled up versions - robotic Humvees and M1A tanks, as well as robotic quads, and perhaps legged versions...
BTW - this last was actually funded by DARPA back in the 1980's, which culminated in the Odetics, Inc. (now known as Iteris, Inc. - based in Anaheim, California - interesting the strange things going on at this company, whatwith name changes, etc - plus, they are developers of an "electronic highway" concept - I am sure there is no relation to the Grand Challenge - wink, wink) ODEX-1 legged walker - a very unique leg design that proved to be fairly robust and strong, while keeping outboard weight (on the legs) to an absolute minimum by moving all the electric motors inward toward the torso of the machine.
Think about it - if you could, in addition to GPS coordinates, vision systems, etc - also bury in the ground or nearby some form of active or passive "locator" beacons, such as what Odetics - oops, I mean Iteris - is developing - wouldn't the problem become just a little bit simpler...?
Nah - DARPA hasn't been thinking about this, not at all, not at all...
Reason is the Path to God - Anon
Team Jefferson uses Fedora Core 3 and Java, even embedded java (http://jstamp.com/) http://teamjefferson.com/