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We discuss the generation of symbolic feedback con- trol sequences for navigating a sparsely-described and uncertain environment, together with the problem of sensing landmarks sufficiently well to make feedback meaningful. We explore the use of a symbolic control approach for mitigating the lack of a detailed map of the environment and for reducing the complexity associated with finding control laws which steer a control system between distant locations. Under our language-based approach, control inputs take the form of symbolic strings. The decision process that generates those strings is guided by estimates of the vehicle's location within a set of important landmarks and by the statistical effectiveness of each string. This arrangement, and in particular the symbolic nature of the control set, allows us to formulate and solve a class of optimal navigation problems which would be exceedingly difficult to handle if approached at the level of sensors and actuators. Our approach is illustrated in a series of numerical indoor navigation experiments. [ABSTRACT FROM AUTHOR]

The article offers information on the Talon Special Weapons Observation Reconnaissance Direction System (SWORDS ), which is the latest combat system used by the U.S. Army. The Talon SWORDS, designed by Foster Miller Inc., is a tractor-mounted, remotely controlled platform that can be equipped with M16, M240, M249, or Barrett 50-caliber machine guns. In defensive mode, the Talon uses a camera and other sensors to detect dangers in its environment while in offensive mode, it can use machine guns, grenade launchers, and rocket systems to eliminate those dangers. According to Robert Quinn, VP of Talon operations for Foster Miller, dual-track robots like Talon can roll over any terrain whether it is sand, mud, snow or ice.