In robot navigation, it is common to develop machines that create a map-like representation of their world, and at the same time represent their own location in this cognitive map. This complex type of processing is called simultaneous localization and mapping, or slam for short. Behavior-based roboticists attempt to eliminate representations from their robots as much as possible, and there have been some attempts to develop navigating machines that do not engage in slam.
Dawson, Dupuis, and Wilson (2010) described a Lego robot that also falls within this behavior-based robotics tradition. Their robot, which they called antiSLAM, began as a version of a Braitenberg (1984) Vehicle 2 that used two ultrasonic sensors as sonar devices to control the speeds of two rear motors. A light sensor was added to permit the robot to distinguish bright areas from dark areas. This robot was able to generate the standard results associated with the reorientation task, which is a paradigm that is frequently used to study navigation in animals and humans. That this set of results was obtained suggests that reorientation does not necessarily depend upon the use of internal representations such as cognitive maps.