Engineers at Purdue University are developing robots able to make "educated guesses" about what lies ahead as they traverse unfamiliar surroundings, reducing the amount of time it takes to successfully navigate those environments. The method uses a new software algorithm that lets a robot create partial maps as it travels through an unfamiliar environment. The robot refers to this map to predict what may be ahead.

The more repetitive the environment, the more accurate the prediction. For example, it will be easier to navigate a parking garage using the map because every floor is the same or very similar, and the same could be said for some office buildings. Both simulated and actual robots in the research used information from a laser rangefinder and odometer to measure the environment and create the maps.

Called P-SLAM, the algorithm modifies an approach, called SLAM, which originated in the 1980s. SLAM (simultaneous localization and mapping) uses data from sensors to orient a robot by drawing maps of the immediate environment. The methodís effectiveness depends on the presence of repeated features, similar shapes, and symmetric structures, such as straight walls, right-angle corners, and a layout that contains similar rooms.

Applications include domestic robots, and military and law enforcement robots that search buildings and other environments.

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