ThErE WoulD sEEm market niche for unmanned helicopters. Equipped with lightweight onboard cameras, they could serve as
to be a clear
mapping agents or search-and-rescue “eyes” in places where using a full-sized helicopter and a human crew are life threatening or cost prohibitive. Motion-picture producers have explored the use of autonomous helicopters in filming action scenes in locations where the safety of both flight crews and movie cast members could be at risk from using larger aircraft. Humanitarian groups have considered using autonomous helicopters for land-mine detection, while public safety agencies have explored using them for inspecting bridges and other structures where human inspectors might be endangered. And they are becoming mainstays in applications such as crop dusting in Japan, where the need to fly at a low altitude and spray chemicals can be dangerous for pilots.
Academic and commercial research teams have been perfecting the capabilities of autonomous helicopters for nearly two decades, with such widespread deployments as a goal. Algorithmic and technological advances are occurring at a steady pace, but regulatory roadblocks and trade restrictions are hampering market acceptance. And, though much of the cutting-edge research in autonomous helicopters demonstrates significant crossover potential between disparate computational and scientific disciplines as well as other aviation applications, many researchers find themselves stymied by these non-technological obstacles that stem from policy concerns.
“A lot of vehicles have at least kinematics that are similar to helicopters,” says Adam Coates, a Stanford University Ph.D. student who coauthored Learning for Control from Multiple Demonstrations, which won the
One of stanford university’s autonomous helicopters flying upside down in an aerobically challenging airshow. for more photos and video, visit http://heli.stanford.edu/index.html.
International Conference for Machine Learning’s best application paper for 2008, and describes how he and colleagues programmed an autonomous helicopter to perform complex aerobatics. “But I think the biggest hurdle is regulatory. It’s virtually impossible to do real UAV [unmanned aerial vehi-cle] operations unless you’re a defense contractor or the military—so you have to go to a big defense contractor if you want to do real UAV research.”
Regulatory hurdles vary, depending on the sovereignty involved. In the U.S., for example, the Federal Aviation Administration (FAA) has yet to issue regulations regarding the use of autonomous helicopters in public airspace. A 2008 report by the U.S. General Accountability Office (GAO) noted that unmanned aircraft, whether fixed wing or rotor powered, cannot meet the National Airspace System’s safety regulations for tasks such as avoiding
other aircraft. Therefore, autonomous crafts’ use is limited to case-by-case approval by the FAA, and usually restricted to line-of-sight operation. In Japan, the government has placed strict trade restrictions on the Yamaha RMAX autonomous helicopter, which is regarded as the industry benchmark, to prevent it from being used for military operations by unfriendly nations.
Omead Amidi, a research faculty member at Carnegie Mellon University and CEO of SkEyes Unlimited, a Washington, PA-based firm that manufactures instruments for autonomous aircraft, concurs with Coates’ observation about the dearth of regulatory infrastructure hindering wider development and deployment of the craft.
“If you have a helicopter flying over your head, it’s because everything about it is regulated,” Amidi says. “No such thing exists for autonomous helicopters. If you could convince me to
PhotograPh by eugene fratkin
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