In robot motion planning, geometric modeling plays an important role. Generally the shapes of objects such as factory products can be stored in a computer software database, so the elements that need to be decided are positions and orientations. In this paper surface-based geometric modeling using a task-oriented teaching tree is proposed. In this modeling, combinations of surfaces for deciding positions and orientations of objects are represented in a depth-first tree based on a kind of task. Therefore, the operator can choose easily one combination out of several compared with the obtained data. Moreover a geometric model of the object suited to the manipulation task can be obtained.

Geometric modeling of the environment is important in robot motion planning. Generally, shapes can be stored in a database, so the elements that need to be decided are positions and orientations. In this paper surface-based geometric modeling using a teaching tree is proposed. In this modeling method, combinations of surfaces are considered in order to decide the positions and orientations of the object. The combinations are represented by a depth-first tree, which makes it easy for the operator to select one combination out of several. This method is effective not only in the case when perfect data can be obtained but also when conditions for measurement of three-dimensional data are unfavorable which often is the case in the environment of a working robot.

A new approach to integrate range data from multiple viewpoints is described. Complicated objects, for example flowers, are observed by a range finder from multiple viewpoints whose relative position is unknown. After simple segmentation, correspondences of regions between two consecutive data are examined. This matching process relies on regions which are not supposed to be influenced by occlusion. Transformation parameters between two data are calculated by referring to the matching result. An iteration process to minimize difference estimates of corresponding regions is devised so that a more accurate result is obtained. Data observed from multiple viewpoints are transferred to standard coordinates and integrated. Results on some objects, for example flowers, show that so far that this method is promising.>

This paper describes an approach to the recognition of stacked objects with planar and curved surfaces. The system works in two phases. In the learning phase, a scene containing a single object is shown one at a time. The range data of a scene are obtained by a range finder. The description of each scene is built in terms of properties of regions and relations between them. This description is stored as an object model. In the recognition phase, an unknown scene is described in the same way as in the learning phase. Then the description is matched to the object models so that the stacked objects are recognized sequentially. Efficient matching is achieved by a combination of data-driven and model-driven search processes. Experimental results for blocks and machine parts are shown.