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Team Stanford aiming for the jackpot at DARPA Grand Challenge 2005

Team Stanford aiming for the jackpot at DARPA Grand Challenge 2005
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Fontana (CA) - 23 vehicles are picked to enter the DARPA Grand Challenge 2005 race. Overall capability of the robot vehicles has dramatically improved over the previous year, but there are some teams that in fact may be capable of the winning the $2 million cash price - one of them being Stanford, which completed four perfect runs in the qualification round.

The DARPA Grand Challenge is full of robotic vehicles that entertain the crowd by making unexpected turns, crashing into walls or catching on fire. Team Stanford's (http://www.stanfordracing.org/) vehicle, Stanley, has completed four perfect runs at the National Qualification Event at the Fontana Speedway - all without hitting an obstacle or making wild turns. We chatted with Michael Montemerlo, Software Lead of the Stanford Racing Team, to find out what makes their vehicle the current leader of the pack.

According to him, Stanford chose the VW Touareg since the electronics already are tightly integrated with the vehicle and the car "is mostly" drive by wire. Wheel speeds, engine RPM, the current gear and suspension deflection are natively recorded by a "CAM bus" and Montemerlo told us it was trivial to tap into the data stream. In contrast, other teams apparently burned precious development time by hacking their own way of getting to this information.

The function of primary obstacle avoidance is accomplished with the five LIDARs (light detection and ranging) units, costing $12,000 each, mounted on the roof of the car. They can detect obstacles 30 to 35 meters away and according to Montemerlo, are accurate within 1 cm. Each unit produces 181 points of data for every line drawn with 75 lines are drawn per second. The units are focused at different distances in front of the vehicle with the closest focus distance just beyond the front bumper. "It's useless 99 percent of the time, but is necessary to see when you are cresting a hill," said Montemerlo.

The raw laser data is saved to laptop hard-drives using a compressed ASCII format that is easy to handle and edit. "The laser data stream is about 250 MByte per hour, which is not that bad," said Montemerlo. While other teams have gone with flash memory, Team Stanford decided against solid-state memory as harddrive never failed during the development of the vehicle.

Two radar units mounted to the left and right of the LIDARs. Each of the 3 X 3 square inch radars have one transmitter and two receivers, allowing the vehicle to generate range and intensity readings to a list of targets. While the LIDARs produce a terrain map of the world, the radar units help with general detection of dense obstacles such as telephone poles, metal signs and cars. Montemerlo said that they have tremendous range allowing the vehicle to see out about 200 meters (600 ft).

Sensor data then is fused with the information coming from the navigation sensors mounted on the roof of the car. A Novatel GPS receiver provides world location within 10 cm (4 inches) of accuracy. In addition, two antennas form a GPS compass that determines pitch and yaw to two degrees of error. The position estimator software combines the signals from the GPS units on top, the inertial measuring unit in the back and the CAM bus. Stanley can estimate its current orientation (xyz, roll, pitch and yaw) accurately to a quarter of a degree. Montemerlo told us that the Stanford team cares more about pitch rather than roll, "We don't worry about roll because by the time you get into a serious situation, it's too late," he said.