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 navigate those environments successfully.


Potential applications include domestic robots and military and law enforcement robots that search buildings and other environments.


The method works by using a new software algorithm that enables a robot to create partial maps as it travels through an environment for the first time.


The robot refers to a partial map to predict what lies ahead. The more repetitive the environment, the more accurate the prediction and the easier it is for the robot to navigate successfully, says C.S. George Lee, a Purdue professor of electrical and computer engineering who specializes in robotics.


For example, its going to be easier to navigate a parking garage using this map because every floor is the same or very similar, and the same could be said for some office buildings, Lee says.


Both simulated and actual robots in the research used information from a laser rangefinder and odometer to measure the environment and create the maps of the layout.


The algorithm modifies an approach, called SLAM, that originated in the 1980s.


The acronym stands for simultaneous localization and mapping. SLAM uses data from sensors to orient a robot by drawing maps of the immediate environment.


Because the new method uses those maps to predict what lies ahead, it is called P-SLAM.


Future research will extend the concept to four robots working as a team, operating with ant-like efficiency to explore an unknown environment by sharing the mapped information through a wireless network.


The researchers also will work toward creating an object-based prediction that recognizes elements such as doors and chairs, as well as increasing the robots energy efficiency.


Robots operating without the knowledge contained in the maps must rely entirely on sensors to guide them through the environment.


Those sensors, however, are sometimes inaccurate, and mechanical errors also cause the robots to stray slightly off course.


The algorithm enables robots to correct such errors by referring to the map, then navigate more precisely and efficiently.


The research has been funded by the National Science Foundation.