Abstract
Cognitive architectures, computer programs that define mechanisms that are important for domain-independent intelligent behavior, have the potential for generating intelligent and autonomous behavior in unmanned vehicles. The cognitive robotic system has demonstrated how the Soar cognitive architecture can be integrated with common robotic motor and perceptual systems that complement the strengths of Soar for unmanned vehicle control. The cognitive robotic system has been tested using an indoor search mission, during which the cognitive robotic system searches a building for common intersection types and an object of interest using information from robotic mapping, computer vision, and fuzzy logic algorithms. The Soar agent builds a topological map of the environment, using information about the intersections the cognitive robotic system detects, and it uses this topological model to make intelligent decisions about how to effectively search the building. Once the object of interest has been detected, the Soar agent uses the topological map to make decisions about how to efficiently return to the location where the mission started as well as to generate step-by-step directions using the intersections in the environment as landmarks that describe a direct path from the mission’s start location to the object of interest.
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