In this paper we present an approach to planning sensing strategies in a robot work cell with multi-sensor capabilities. The system first forms an initial set of object hypotheses by using one of the sensors. Subsequently, the system reasons over different possibilities for selecting the next sensing operation, this being done in a manner so as to maximally disambiguate the initial set of hypotheses. The "next sensing operation" is characterized by both the choice of the sensor and the viewpoint to be used. Aspect graph representation of objects plays a central role in the selection of the viewpoint, these representations being derived automatically by a solid modelling program.

In this paper we present an approach to planning sensing strategies in a robot work cell with multi-sensor capabilities. The system first forms an initial set of object hypotheses by using one of the sensors. Subsequently, the system reasons over different possibilities for selecting the next sensing operation, this being done in a manner so as to maximally disambiguate the initial set of hypotheses. The "next sensing operation" is characterized by both the choice of the sensor and the viewpoint to be used. Aspect graph representation of objects plays a central role in the selection of the viewpoint, these representations being derived automatically by a solid modelling program.

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We discuss here the model-acquisition and modelmatching aspects of a model-based vision system for the bin-picking of twisted tubular parts. The system uses both 3-0 structured-light vision and 2-0 binary vision in a synergistic manner. Binary vision kicks in when the robot picks up a tube using a graspablefragment identified as such b y the 3-0 vision system; the tube thus picked up is placed on a backlit table so that its pose can be calculated from its 2 0 image with sufficient precision to allow its assembly with another object. Binary vision carries out pose calculation b y affine matching the projected image with, a sm,all set of model projections, each corresponding t o a separate stable pose of the object on a flat surface and each represented by the coordinates of only four points. This paper presents a heuristic algorithm for determining the the placement of the four points for representing the model projections.