Laser sensors from Sick help identify the terrain and obstacles in front of the A.I. Motorvator unmanned vehicle that competed in the Darpa Grand Challenge in March.

Laser sensors from Sick help identify the terrain and obstacles in front of the A.I. Motorvator unmanned vehicle that competed in the Darpa Grand Challenge in March.


Several vehicles competing in the Darpa Grand Challenge last March got navigational help from a laser-measurement system from Sick, Minneapolis (www.sickusa.com). The lasermeasurement system (LMS) helped sense the terrain and obstacles in front of the unmanned vehicles.

"The LMS units play a key role in our terrain sensing capabilities" said Chris Pederson, team leader of A.I. Motorvators. The sensors help generate a 3D image of the immediate terrain in front of the vehicle.

The race, held March 13th, covered a rugged course between Barstow, Calif., and Primm, Nev. Sponsored by the Dept. of Defense, it was meant to test state-of-the-art autonomous vehicle technology. Although no vehicles completed the 150-mile course, this is seen by most as a first step towards improving autonomous vehicle design. Part of the motivation for the race is that Congress has mandated the DOD to have one third of its ground vehicles unmanned by 2015.

Developments in aerial robotics are also keeping pace with those on the ground. In July, Georgia Tech will hold the fourth annual International Aerial Robotics Competition in Fort Benning, Georgia. Robotic aircraft must fly 3 km to an urban setting, find a building, then enter it via a window or a hole in the roof to find a target inside. For more information, check out http://avdil.gtri.gatech.edu/AUVS /IARCLaunchPoint.html.