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Carnegie Mellon Robots Add Aggressive Soccer Moves

A team of students from Carnegie Mellon University is hoping to make headlines with some football wins in coming days. No, they're not part of the FIFA World Cup taking place in South Africa. This team is taking on contenders in the RoboCup 2010 world championship in Singapore, starting June 19. RoboCup is an international initiative to foster artificial intelligence and robotics research; the ultimate goal of the project is to develop a team of fully autonomous humanoid robots that can play and win against the human world champion soccer team by 2050.

For the current competition, a student at the Pittsburgh-based university has developed a new algorithm that helps the robot soccer players predict the ball's behavior based on physics principles. The hope is that the CMDragons, the Carnegie Mellon team that competes in RoboCup's Small-Size League, will be able to outmaneuver opponents and find creative solutions to game situations that could even surprise their programmers. It's possible that the physics-based planning algorithm also might enable the players, who stand at six inches tall, to invent some new kicks. The team has posted a YouTube video showing the robots in action.

"Physics-based planning gives us an advantage when a robot is dribbling the ball and needs to make a tight turn, or any other instance that requires an awareness of the dynamics of the ball," said Stefan Zickler, a newly minted Ph.D. in computer science who developed the algorithm for his thesis. "Will the ball stick with me when I turn? How fast can I turn? These are questions that the robots previously could never answer."

The algorithm could enable the robots to concoct some new kicks, including bank shots, Zickler said. But the processing power to calculate kicks are greater than for dribbling, so limited computational power and time will keep this use to a minimum.

In addition to physics-based planning, the CMDragons are preparing to use a more aggressive strategy than in previous years. "We've noticed that in our last few matches against strong teams, the ball has been on our side of the field way too much," Zickler said. "We need to be more opportunistic. When no better option is available, we may just take a shot at the goal even if we don't have a clear view of it."

Each Small-Size League team consists of five robots. The CMDragons robots include two kicking mechanisms--one for flat kicks and another for chip shots. They also come with a dribble bar that exerts backspin on the ball. Each team builds its own players; that job fell to Michael Licitra, an engineer at Carnegie Mellon's National Robotics Engineering Center.

"Over the years, we have developed many successful teams of robot soccer players, but we believe that the physics-based planning algorithm is a particularly noteworthy accomplishment," said Manuela Veloso, professor of computer science and leader of Carnegie Mellon's two robot soccer teams. "Past teams have drawn from a repertoire of pre-programmed behaviors to play their matches, planning mostly to avoid obstacles and acting with reactive strategies.

"To reach RoboCup's goal of creating robot teams that can compete with human teams, we need robots that can plan a strategy using models of their capabilities as well as the capabilities of others, and accurate predictions of the state of a constantly changing game," said Veloso, who also is president of the International RoboCup Federation.

Carnegie Mellon also plans to run a Standard Platform League team that uses 22-inch-tall humanoid robots as players. Both teams will join 500 other teams with about 3,000 participants. RoboCup includes five different robot soccer leagues, as well as competitions specifically for search-and-rescue robots, assistive robots, and students up to age 19.

The CMDragons have won before, taking the competition in both 2006 and 2007.

About the Author

Dian Schaffhauser is a former senior contributing editor for 1105 Media's education publications THE Journal, Campus Technology and Spaces4Learning.

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