Last week, the US Navy opened its Laboratory for Autonomous Systems Research (LASR) for testing some robots’ robotic systems. Now, with a 3D imaging system and high definition cameras, the US Navy plans to use drone helicopters to identify potential pirate ships. The name of this air drone is ‘Fire Scout.’ The nave is already using it. But due to some lackings, the need for robotic and machine-learning software systems has gone up.
Last week, the Office of Naval Research reported that, they are planning to use a drone helicopter which can better identify small boats using high-definition cameras and sensors with laser-radar (LADAR) technology. This technology is also called LIDAR. The Office of Naval Research said the lasers are ‘eye safe.’ US Navy plans to use Fire Scout. Fire Scout’s cameras and the LADAR sensor system are equipped with Multi-Mode Sensor Seeker (MMSS). After collecting the images, the software sifts the data and compare the collected images to images of reference vessels. In other word, LADAR systems send lasers beams onto an objects and process the signal echoed from the targets to create a 3D image. The software algorithms have been tested from shore-based systems on vessels at sea. Now the Navy plans to test the software on a manned helicopter.
“The 3D data gives you a leg up on target identification,” Dean Cook, principal investigator for the MMSS program at Naval Air Warfare Center Weapons Division, said. “Infrared and visible cameras produce 2D pictures, and objects in them can be difficult to automatically identify. With LADAR data, each pixel corresponds to a 3D point in space, so the automatic target recognition algorithm can calculate the dimensions of an object and compare them to those in a database.”
It’s not a new thing for the Us Navy because US Navy’s Robots Are Taking Preparation To Conquer Fires, Jungle, Sandstorms As autonomous vehicles are widely used by the U.S. military, we believe that this highly trained robots would be used in Somalia to detect pirated ships.
Source : CNet
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