The UC Berkeley Lab Notes has an article on a project to give UAVs (unmanned aerial vehicles) the capability to perform automated tasks for civilian use. UAVs have been used by the U.S. military-- they played a prominent role in the Afghanistan campaign, and when equipped with air-to-ground missiles are able to attack ground targets-- and their potential for civilian applications is obvious. They're relatively small, cheap (about $20k will buy you a good one), and can navigate using GPS. They also are pretty flexible systems, in that you can hack one together using off-the-shelf technology: the Predator, for example, is powered by a four-cylinder snowmobile engine.
The problem is that they haven't been very good at doing some simple things, like following roads. Military UAVs have pilots, for obvious reasons: you need someone to make the decision to fire on a target, or to take evasive action against enemy fire. In fact-- if memory serves-- the military has had to make sure that time logged "flying" UAV missions would be counted towards promotion, just like flying in a real plane.
But in the civilian world, there are lots of cases where you'd want the UAV to basically work like a flying surveillance camera: to take a route and look for anything out of the ordinary. As Berkeley professor Raja Sengupta realized,
"I realized that it's very difficult for a UAV to follow a road. GPS errors can cause a UAV to veer off its path quite easily."The Berkeley team's approach is to augment GPS with machine vision software and a $120 off-the-shelf video camera. The challenge, Sengupta explains, is for the computer to discern the road from the rest of the terrain from altitudes up to several hundred feet.
The solution is a two-step process devised by Zu Kim, a researcher with the UC Berkeley-based PATH (Partners for Advanced Transit and Highways) program. First, the software distinguishes the road from the surrounding area based on differences in contrast. In the desert, for instance, the asphalt is much darker than the sand. Next, the lane boundaries are identified based on the lightness of lane markings as compared to the asphalt. Once the boundaries are located, the plane follows the lane from above.
"GPS will get the plane in the vicinity and once our system locks onto the road, the plane can adjust itself regardless of what the GPS says," Sengupta explains.
There's also a biomimicry component to the story.
The final prong in the Berkeley UAV research is focused on what Sengupta calls the "canyon problem." While monitoring traffic in an urban setting, a UAV may be forced to fly between buildings lining the road it's tracking. One intriguing solution was inspired by the navigation of bees. The insects are able to fly down a corridor without bouncing between the walls by determining if they seem to be moving past both walls at the same rate. If there's a discrepancy between walls, the bee adjusts its trajectory. Sengupta hopes that borrowing this biological principle behind bee flight will lead to a computationally quick way to deal with "urban canyons."
It'll be interesting to see if there develop applications in which you'd want UAVs to fly in flocks.
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