Knowledge-Based Position Estimation for a Multisensor House Robot
This paper considers a knowledge-based approach to position estimation for mobile house or office robots. It deals with the robots which are, for cost reasons, equipped with inexpensive sensors only. These sensors have limited resolution and their readings are subject to distortion and errors. The approach presented exploits the stationary nature of major structural features and large objects (e.g. rooms, windows, beds) in the domain of movement of the robot. To reduce the real-time computational requirements, the knowledge of what each sensor would be expected to see is stored in a suitable representation in the robot database.
The paper presents a two blackboard architecture for the position estimator of the robot. During position estimation, specialist knowledge sources (sensor experts) interpret the readings from the sensors, generate position hypotheses and post them on the domain blackboard. Other knowledge sources control the scheduling of sensor experts and integration of their findings by using information recorded on the control blackboard.
A prototype position estimator has been implemented and evaluated on an experimental system consisting of a mobile robot with limited sensory capability (a Heath Hero-1 robot) controlled by a high performance personal computer (IBM PC/XT). The position estimation software has been implemented in GCLISP, a subset of Common LISP. The experimental testbed is described and initial position estimation results are discussed.
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