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The great (driverless) race

Mar 1, 2006 12:00 PM
By Wayne Wenzel


IT TOOK two tries over two years and a doubling of the prize money from $1million to $2 million, but in October 2005, the DARPA Grand Challenge found a winner.

The challenge was to develop an autonomous robotic vehicle capable of successfully navigating a rugged 132-mile desert course from Barstow, CA, to Primm, NV. To make matters more challenging, DARPA (Defense Advanced Research Projects Agency) dictated a 12-hour time limit to complete the course. This meant vehicles would have to complete a complex course at speeds approaching 20 mph. Five vehicles were able to complete the entire route without a human controlling the throttle, brake or steering wheel. Stanford University finished first with a time of 6 hours and 54 minutes.

Farmers and custom applicators familiar with modern precision ag and automated tractor guidance technology may wonder what all the hoopla is about. Automated steering systems, which temporarily relieve the human driver, have been commercially available for several years.

Ag's automated drivers

Today's automated steering systems on tractors and sprayers do a good job using GPS and correctional software to drive straight lines across the field. With the right software, some systems can even accurately steer predetermined curved paths.

But such automated steering systems cannot yet totally replace a human driver, who must remain in the cab just in case an unexpected obstacle appears on the way from point A to point B. With a view toward high-risk military missions, totally replacing the human driver is DARPA's goal.

The challenge in developing a vehicle that can always drive itself is in pairing different sensing technology components with enough computing power to make sense of it all and take the correct actions.

In 1999, New Holland and Carnegie Mellon University debuted a driverless self-propelled windrower. The machine used GPS guidance and VisionGuidance (a technology that paired cameras and computers) to “see” differences in the crop line. The machine harvested 100 acres of alfalfa without stopping.

In 2001, John Deere debuted an autonomous orchard tractor that used a variety of sensing systems to steer the machine between rows of trees during pesticide spraying operations. Both of these machines, while successful experimental projects, did not approach the complexity of a system required for high-performance desert driving.

Harder than you'd think

The 2005 DARPA outcome was a marked improvement from the results of the first DARPA Grand Challenge just a year earlier, where no vehicles finished in the required time limit. In that inauspicious start, most of the vehicles ended up overturned, stuck in ditches, broken down, or just hopelessly lost in the desert. It turned out that the computing power needed for a vehicle to drive itself in challenging conditions was much greater than anyone expected.

Whether it's cruising down the highway or between rows of corn, our brains analyze multiple data inputs from our eyes, ears, fingertips and sometimes even the seat of our pants. We consciously and unconsciously analyze the data and, most of the time, take the right actions to keep the vehicle rubber-side down.

The same holds true for an autonomous vehicle that relies on computer processing. Each of the DARPA contest finishers used multiple high-powered computers to tie the various sensing systems together and correct the vehicle's course based on rapidly changing terrain.

Tunnels, hills, rocks and gullies were among the obstacles and vehicle traps to be found along DARPA's desert course. The computer-driven vehicles identified these challenges with integrated systems that included lasers, machine-vision cameras, inertial measurement, long- and short-range radar and differentially corrected GPS systems.

Try pulling that off on a farm without a corporate budget and team of brainiac engineers. For more information, visit www.grandchallenge.org.







 

SEFP ATE




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