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A system which performs task-oriented navigation for an intelligent mobile robot is described in this paper. This navigation system is based on a dynamically maintained model of the local environment, called the "Composite Local Model." The Composite Local Model integrates information from a rotating sonar sensor, the robot's touch sensor and a pre-learned Global Model as the robot moves through its environment. Techniques are described for constructing a line segment description of the most recent sensor scan (the Sensor Model), and for integrating such descriptions to build up a model of the immediate environment (the Composite Local Model). Model integration is based on a process of reinforcing the confidence in consistent information while decaying the confidence in inconsistent information. The estimated position of the robot is corrected by the difference in position between observed sensor signals and the corresponding symbols in the Composite Local Model. This system is useful for navigation in a finite, pre-learned domain such as a house, office, or factory.