The method of relaxation labeling, which is a parallel distributed processing mechanism, is presented. Relaxation labeling is able to integrate the sensor data with some known knowledge. Such an integration is implemented by forcing constraint satisfaction. One type of constraint satisfaction in intelligent robotic systems is described. A relaxation labeling algorithm is presented. The local convergence properties of a labeling process are established. Its global behavior was examined numerically. To illustrate its potential applications in robotic workstations, the relaxation labeling algorithm was applied to perform an object identification task in an intelligent robotic workstation with two cameras.
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