Autonomy. Lawrence Burns

Minerva even provided tours to visitors of Washington’s Smithsonian Museum.

      It turned out programming a robot to navigate through a museum was a surprisingly complex challenge. Minerva would share the museum floor with dozens of human tourists—as well as valuable museum exhibits. How to engineer the creature so that it didn’t bump into an exhibit? How to write the code so that it didn’t roll over a child?

      Six years before DARPA staged its first Grand Challenge, in 1998, Thrun equipped Minerva with laser-range finders. Then he loaded the robot with a machine-learning algorithm and sent it out on the museum floor at night, without any tourists around. Minerva wandered around the exhibits, sending out laser beams and creating a map of its environment. Then, when the museum was open, with humans sharing the same floor as the robot, Minerva would use this map to locate itself. The map also provided a way for Minerva to avoid running into humans. The robot would assume any new obstacle that hadn’t been on the original map was a human, causing Minerva to stop safely.

      The tour guide was a big hit, and Thrun used the acclaim to handle the software side of other projects. For example, Whittaker convinced Thrun to join the team that built the Groundhog robot that aimed to make it safer for Appalachian coal miners to retrieve their underground ore. Maps didn’t exist for older, decommissioned mines in the area, which could cause problems. In 2002, for example, nine workers toiling in Pennsylvania’s Quecreek mine were trapped by water when they breached an adjacent passageway that had been abandoned for years and flooded sometime along the way. The miners escaped after three days, but Whittaker took the accident as a challenge and, in just two months, with Thrun working on the SLAM programming, created a robot that could be dropped into old mines to scan the passageways and create 3-D maps for reference.

      DARPA’s series of challenges fascinated Thrun. When Thrun was eighteen, in 1986, his best friend, Harald, was invited for a ride in another friend’s new Audi Quattro. It was an icy day, and the driver was going too fast and ran the Quattro headfirst into a truck. Harald died instantly. The impact was so strong that his seat belt was shredded. The crash would forever haunt the German robotics professor.

      Thrun saw self-driving cars as a way to make automobile transportation safer, to avoid crashes like the one that killed his friend. He did some thinking about the problem after the first Grand Challenge. The fact that DARPA created waypoints along the route really simplified the problem, he figured. Programming Minerva to navigate the fast-changing and crowded environment of the Smithsonian Museum rivaled the complexity of the self-driving-car problem. Before he left Carnegie Mellon, he went to Red Whittaker with an offer. “Look,” Thrun told the older robotics legend. “I’ve been recruited from Stanford, but for the next Grand Challenge, I would love to help you.”

      “Had he said yes,” Thrun recalls, “I would have happily served on his team and never have started my own team.”

      But Whittaker declined Thrun’s offer, presumably because he wanted to keep Red Team exclusive to people associated with Carnegie Mellon. After Montemerlo’s presentation, Thrun considered whether to enter the second challenge himself. Red Team had taken a year to build a robot that went 7.3 miles. If Thrun’s new lab could do better, they’d go a long way toward establishing a national reputation. SLAM would be integral to a successful performance, and Thrun and Montemerlo were two of the world’s leading experts on the topic. Thrun basically figured, why not?

      So on August 14, when DARPA staged a conference for potential competitors, Thrun brought Montemerlo and several other members of his team. The conference was held in Anaheim, California. Despite the negative media coverage of the first race, even more competitors came out this time around: more than 500 people from 42 states and 7 different countries attended the 2004 competitors’ conference. Ultimately, 195 teams would register to compete, nearly double the number that signed up for the first race.

      Including, of course, the Red Team. The summer after the debacle in the desert, Urmson went off and completed his PhD, then got a job working for Science Applications International Corporation, the government contractor that had sponsored Sandstorm. Urmson’s assignment was to work with Red Whittaker and Red Team on the second DARPA race. Urmson’s hopes were considerably higher for the second challenge. They’d have another eighteen months to perfect Sandstorm’s development. And they’d be doing so with a more professional group, including several engineers from Caterpillar, the construction-equipment manufacturer. The budget was bigger, at $3 million. The atmosphere was different, too. The first time out there was youthful enthusiasm. This time, there was an almost grim determination.

      “I signed up to win the Grand Challenge,” Whittaker proclaimed. “This time around, the Red Team will be more like a Red Army.”

      It was inevitable that the Stanford and Carnegie Mellon teams would bump into each other at the preliminary conference. Urmson noticed that Montemerlo was carrying a sheaf of papers in his hand that turned out to be the technical paper Urmson had written after the first race. The paper described the most intimate details of Red Team’s approach. Publishing for the rest of the robotics community the secrets of all competitors’ approaches had been one of DARPA’s conditions of entry. It was a good strategy. In the spirit of academia, sharing intelligence meant the whole field progressed faster. But it also made things more difficult for Whittaker and Urmson. As the country’s leading robotics lab they’d had a head start for the first race. Publishing their approach brought everyone else closer to the Red Team’s level. And the defectors, Montemerlo and Thrun, were brilliant people. That they were entering meant the prize was no longer Carnegie Mellon’s to take. Now, heading into the second challenge, Red Team faced its most serious competition yet.

      Early on in its preparations, Red Team decided to hedge its bets by entering two robots. (There was a precedent for this. SciAutonics had entered two vehicles in the first race.) Partially, the step was designed to smooth relations between team software lead Kevin Peterson and project manager Chris Urmson, who were apt to butt heads in the latter half of Sandstorm’s development. There was talk of giving each deputy his own vehicle, although years later Whittaker would insist that Peterson and Urmson contributed to both robots in the lead-up to the second race. And partially, the move was pragmatic. After all, thanks to AM General’s donation, Red Team had enough Humvees.

      The second vehicle, which became known as H1ghlander, was a 1999 model year, making it thirteen years younger than Sandstorm. The AM General–donated vehicle came with a 6.5-liter turbocharged engine. One of the challenges of autonomous driving involved controlling acceleration and steering. Most vehicles of the era were mechanically controlled. They relied on a human being twisting steering wheels, pushing accelerators, shifting gears, which complicated matters when a computer was supposed to do the driving. There was a margin of error when a digitally controlled actuator pressed against, say, a gas pedal.

      This new Humvee, H1ghlander, featured drive-by-wire capability embedded in its controls. It had been designed to be controlled by a computer. The throttle, for example, was operated by a factory-installed engine control module. So instead of rigging up an electric motor and lever to actually push against the gas pedal, as with Sandstorm, the H1ghlander crew could hack into the newer Humvee’s computer system and control the throttle electronically. It all meant less margin of error, which made H1ghlander a better driver.

      Another change was that Whittaker and his students had tracked down a different, more accurate location-tracking system. The system used in the first race had a margin of error of about a yard. This new one, from a sponsor named Applanix, featured a margin of error of about twenty-five centimeters, or less than a foot—a big improvement for the second race.

      So the Red Team had a lot going for it. But so, too, did Thrun’s team. In his heart, Whittaker was a hardware guy, who came from an era when making robots work involved the precise interplay between actuators and carburetors, electric motors and solar-powered chargers. This was reflected in Red Team’s approach to the first challenge, which saw his charges spending as much time perfecting the e-box and gimbal mechanisms as writing code for the computers. But as computing power improved, robotics was increasingly becoming a software problem, which computer