fly one of these over the head of my daughter, OK, it’s ready, but I’m not doing it now.”

ai to the forefront

Despite the regulatory issues, which the GAO estimated might take 10 years to resolve in the U.S., researchers have continued to improve autonomous helicopters’ capabilities. The most advanced can take off, hover, and maintain flight autonomously through a combination of advanced sensing and navigation equipment such as laser sensors, GPS modules, inertial measurement units that contain accelerometers and gyroscopes, and communications modules that communicate with ground-based computers or human pilots when necessary. The RMAX, for example, first flew fully autonomously out of visual range in Japan in 2000, following prepro-grammed instructions.

While the RMAX is well suited for commercial purposes, it is also prohibitively large and expensive for applications such as the surveillance of building interiors or for bootstrapped university research programs. A base model used by the U.S. Army for research weighs approximately 185 pounds, has a rotor diameter of three meters, and costs $86,000, while fully autonomous units, complete with navigational and control equipment, can cost $1 million.

Researchers are successfully applying disparate technologies to improve the vehicles, using much smaller and cheaper helicopters than the RMAX. For example, Coates and coauthor Pieter Abbeel, now a professor in the department of electrical engineering and computer sciences at the University of California, Berkeley, utilized artificial intelligence principles to demonstrate their assertion that an off-the-shelf expectation-maximization algorithm could result in the most advanced autonomous aerobatics yet performed, using a commercially available radio-controlled hobbyist helicopter that weighed about 10 pounds.

Coates says the Stanford project was the culmination of five years of effort, in which numerous approaches were discussed and dismissed. Andrew Ng, a professor of computer science at Stanford, who advised Coates and Abbeel

human-generated
mapping can cost
$20,000 per square
mile; an autonomous
helicopter could
produce the same
results 10 times
cheaper, says
Omead amidi.

in their project and was a coauthor of the Learning for Control paper, says the project successfully transferred machine learning techniques into a discipline that had hitherto been extremely labor-intensive, relying on painstaking expert modeling of likely behaviors. Ultimately, they decided to have the helicopter “watch” an expert human pilot’s maneuvers via data input from onboard controls and a radio receiver that saved a copy of the human pilot’s control stick positions during demonstration flights.

“From those two things, you can examine state changes over time and what the pilot does, and can record a whole trajectory to build up a model,” Coates says.

“Previously, the most common approach to designing controllers for autonomous aircraft, both helicopters and fixed wing, was to hire a human engineer to choose parameters for the controller,” Ng says. “For example, if the helicopter is pitched forward a little more than you want, how aggressively do you want to pull back on the stick? The traditional approach was to have a person knowledgeable in aerodynamics and helicopters sit down and model that. This approach can often work, but it is a very slow design process and often doesn’t perform nearly as well as modern machine learning methods.”

Coates and Abbeel discovered that even the most expert human pilot’s aerobatic routine contains errors (or, in the language of the problem, is suboptimal). “However, repeated expert

Programming
Repeat
Winners

For the second year in a row, students from st. Petersburg university of Information technology, mechanics and optics won the annual aCm International Collegiate Programming Contest (ICPC). With this year’s victory, st. Petersburg university has now won the aCm-ICPC world championship three times in the last four years.

Known as “the Battle of the Brains,” the aCm ICPC World Finals took place this year at the royal Institute of technology in stockholm, sweden. the world’s top 100 university teams used open standard technology to solve 11 real-world problems involving traffic congestion, suffix-replacement grammars, and other issues, with the goal being to correctly solve the largest number of problems in the shortest amount of time.

the 33rd annual aCm ICPC, sponsored by IBm, was dominated by teams from russia and China. this year’s top 12, medal-winning teams are st. Petersburg university (russia), which solved nine problems, followed by tsinghua university (China), st. Petersburg state university (russia), saratov state university (russia), the university of oxford (u. K.), and Zhejiang university (China). massachusetts Institute of technology (u.s.) finished in seventh place, followed by altai state technical university (russia), university of Warsaw (Poland), university of Waterloo (Canada), I Javakhishvili tbilisi state university (Georgia), and Carnegie mellon university (u.s.).

“It is clear that computational thinking, which is at the heart of the information technology revolution, is the engine that is driving innovation in these countries,” says aCm President Professor Dame Wendy hall. “as we seek to strengthen computing education and fill the talent pipeline for future workers, it is an important reminder that, while u.s. enrollment in computer science programs may have increased, we need to continue investing in programs that attract women and other underrepresented groups to this field.”