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Stanley: the Robot That Won the DARPA Grand Challenge Stanley: The Robot that Won the DARPA Grand Challenge ••••••••••••••••• •••••••••••••• Sebastian Thrun, Mike Montemerlo, Hendrik Dahlkamp, David Stavens, Andrei Aron, James Diebel, Philip Fong, John Gale, Morgan Halpenny, Gabriel Hoffmann, Kenny Lau, Celia Oakley, Mark Palatucci, Vaughan Pratt, and Pascal Stang Stanford Artificial Intelligence Laboratory Stanford University Stanford, California 94305 Sven Strohband, Cedric Dupont, Lars-Erik Jendrossek, Christian Koelen, Charles Markey, Carlo Rummel, Joe van Niekerk, Eric Jensen, and Philippe Alessandrini Volkswagen of America, Inc. Electronics Research Laboratory 4009 Miranda Avenue, Suite 100 Palo Alto, California 94304 Gary Bradski, Bob Davies, Scott Ettinger, Adrian Kaehler, and Ara Nefian Intel Research 2200 Mission College Boulevard Santa Clara, California 95052 Pamela Mahoney Mohr Davidow Ventures 3000 Sand Hill Road, Bldg. 3, Suite 290 Menlo Park, California 94025 Received 13 April 2006; accepted 27 June 2006 Journal of Field Robotics 23(9), 661–692 (2006) © 2006 Wiley Periodicals, Inc. Published online in Wiley InterScience (www.interscience.wiley.com). • DOI: 10.1002/rob.20147 662 • Journal of Field Robotics—2006 This article describes the robot Stanley, which won the 2005 DARPA Grand Challenge. Stanley was developed for high-speed desert driving without manual intervention. The robot’s software system relied predominately on state-of-the-art artificial intelligence technologies, such as machine learning and probabilistic reasoning. This paper describes the major components of this architecture, and discusses the results of the Grand Chal- lenge race. © 2006 Wiley Periodicals, Inc. 1. INTRODUCTION sult of an intense development effort led by Stanford University, and involving experts from Volkswagen The Grand Challenge was launched by the Defense of America, Mohr Davidow Ventures, Intel Research, ͑ ͒ Advanced Research Projects Agency DARPA in and a number of other entities. Stanley is based on a 2003 to spur innovation in unmanned ground vehicle 2004 Volkswagen Touareg R5 TDI, outfitted with a six navigation. The goal of the Challenge was to develop processor computing platform provided by Intel, and an autonomous robot capable of traversing unre- a suite of sensors and actuators for autonomous driv- hearsed off-road terrain. The first competition, which ing. Figure 2 shows images of Stanley during the race. carried a prize of $1M, took place on March 13, 2004. The main technological challenge in the develop- It required robots to navigate a 142-mile long course ment of Stanley was to build a highly reliable system, through the Mojave desert in no more than 10 h. 107 teams registered and 15 raced, yet none of the par- capable of driving at relatively high speeds through ticipating robots navigated more than 5% of the entire diverse and unstructured off-road environments, and course. The challenge was repeated on October 8, to do all this with high precision. These requirements 2005, with an increased prize of $2M. This time, 195 led to a number of advances in the field of autono- teams registered and 23 raced. Of those, five teams mous navigation, as surveyed in this paper. Methods finished. Stanford’s robot “Stanley” finished the were developed, and existing methods extended, in course ahead of all other vehicles in 6 h, 53 min, and the areas of long-range terrain perception, real-time 58 s, and was declared the winner of the DARPA collision avoidance, and stable vehicle control on slip- Grand Challenge; see Figure 1. pery and rugged terrain. Many of these develop- This paper describes the robot Stanley, and its ments were driven by the speed requirement, which software system in particular. Stanley was developed rendered many classical techniques in the off-road by a team of researchers to advance the state-of-the- driving field unsuitable. In pursuing these develop- art in autonomous driving. Stanley’s success is the re- ments, the research team brought to bear algorithms Figure 1. ͑a͒ At approximately 1:40 pm on Oct 8, 2005, Stanley was the first robot to complete the DARPA Grand Challenge. ͑b͒ The robot is being honored by DARPA Director Dr. Tony Tether. Journal of Field Robotics DOI 10.1002/rob Thrun et al.: Stanley: The Robot that Won • 663 Figure 3. A section of the RDDF file from the 2005 DARPA Grand Challenge. The corridor varies in width and maximum speed. Waypoints are more frequent in turns. Figure 2. Images from the race. CA, approximately 100 miles northeast of Los Ange- from diverse areas including distributed systems, les, and finished in Primm, NV, approximately machine learning, and probabilistic robotics. 30 miles southwest of Las Vegas. The 2005 course both started and finished in Primm, NV. The specific race course was kept secret from all 1.1. Race Rules teams until 2 h before the race. At this time, each The rules ͑DARPA, 2004͒ of the DARPA Grand Chal- team was given a description of the course on CD- lenge were simple. Contestants were required to ROM in a DARPA-defined route definition data for- build autonomous ground vehicles capable of tra- mat ͑RDDF͒. The RDDF is a list of longitudes, lati- versing a desert course up to 175-miles long in less tudes, and corridor widths that define the course than 10 h. The first robot to complete the course in boundary, and a list of associated speed limits; an under 10 h would win the challenge and the $2M example segment is shown in Figure 3. Robots that prize. Absolutely no manual intervention was al- travel substantially beyond the course boundary risk lowed. The robots were started by DARPA personnel disqualification. In the 2005 race, the RDDF con- and from that point on had to drive themselves. tained 2,935 waypoints. Teams only saw their robots at the starting line and, The width of the race corridor generally tracked with luck, at the finish line. the width of the road, varying between 3 and 30 m Both the 2004 and 2005 races were held in the in the 2005 race. Speed limits were used to protect Mojave desert in the southwest United States. The important infrastructure and ecology along the course terrain varied from high-quality graded dirt course, and to maintain the safety of DARPA chase roads to winding rocky mountain passes; see Figure drivers who followed behind each robot. The speed 2. A small fraction of each course traveled along limits varied between 5 and 50 mph. The RDDF de- paved roads. The 2004 course started in Barstow, fined the approximate route that robots would take, Journal of Field Robotics DOI 10.1002/rob 664 • Journal of Field Robotics—2006 Figure 4. ͑a͒ View of the vehicle’s roof rack with sensors. ͑b͒ The computing system in the trunk of the vehicle. ͑c͒ The gear shifter, control screen, and manual override buttons. so no global path planning was required. As a result, 2. VEHICLE the race was primarily a test of high-speed road finding, obstacle detection, and avoidance in desert Stanley is based on a diesel-powered Volkswagen terrain. Touareg R5. The Touareg has four-wheel drive The robots all competed on the same course; ͑4WD͒, variable-height air suspension, and automatic starting one after another at 5 min intervals. When a electronic locking differentials. To protect the vehicle faster robot overtook a slower one, the slower robot from environmental impact, Stanley has been outfit- was paused by DARPA officials, allowing the second ted with skid plates and a reinforced front bumper. A robot to pass the first as if it were a static obstacle. custom interface enables direct electronic actuation of This eliminated the need for robots to handle the both the throttle and brakes. A DC motor attached to case of dynamic passing. the steering column provides electronic steering con- trol. A linear actuator attached to the gear shifter shifts the vehicle between drive, reverse, and parking 1.2. Team Composition gears ͓Figure 4͑c͔͒. Vehicle data, such as individual The Stanford Racing Team team was organized into wheel speeds and steering angle, are sensed auto- four major groups. The Vehicle Group oversaw all matically and communicated to the computer system modifications and component developments related through a CAN bus interface. to the core vehicle. This included the drive-by-wire The vehicle’s custom-made roof rack is shown in ͑ ͒ systems, the sensor and computer mounts, and the Figure 4 a . It holds nearly all of Stanley’s sensors. computer systems. The group was led by researchers The roof provides the highest vantage point of the ve- from Volkswagen of America’s Electronics Research hicle; from this point, the visibility of the terrain is Lab. The Software Group developed all software, in- best, and the access to global positioning system ͑ ͒ cluding the navigation software and the various GPS signals is least obstructed. For environment health monitor and safety systems. The software perception, the roof rack houses five SICK laser range group was led by researchers affiliated with Stan- finders. The lasers are pointed forward along the ford University. The Testing Group was responsible driving direction of the vehicle, but with slightly dif- for testing all system components and the system as ferent tilt angles. The lasers measure cross sections of a whole, according to a specified testing schedule. the approaching terrain at different ranges out to The members of this group were separate from any 25 m in front of the vehicle. The roof rack also holds of the other groups. The testing group was led by a color camera for long-range road perception, which researchers affiliated with Stanford University. The is pointed forward and angled slightly downward. Communications Group managed all media relations For long-range detection of large obstacles, Stanley’s and fund raising activities of the Stanford Racing roof rack also holds two 24 GHz RADAR sensors, Team.
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