برنامه ریزی مسیر برای اسکن لیزر با ربات های صنعتی

Reverse Engineering is concerned with the problem of creating CAD models of real objects by measuring point data from their surfaces. Current solutions either require manual interaction or expect the nature of the objects to be known. We believe that in order to create a fully automatic system for RE of unknown objects the software that creates the CAD-model should be able to control the operation of the measuring system. This paper is based on a real implementation of a measuring system controlled by CAD software, capable of measuring along curved paths. Some details of the system have been described in earlier publications. This paper is concerned with the problem of automatic path planning for a system that can move along curved paths.

In the development of new products CAD systems are often used to model the geometry of the objects to be manufactured. Reverse Engineering (RE) of geometry is the reverse process where the objects already exist and CAD models are created by interpreting geometrical data measured directly from the surfaces of the objects. An introduction to RE which is often referred to is a paper by Varady, Martin and Cox 1997 [1]. In that paper the RE process is divided into the following four steps: (1) Data capture (2) Preprocessing (3) Segmentation and surface fitting (4) CAD-model creation. Step 1 is closely related to measurement technology. Optical systems such as laser scanners in combination with manually-controlled mechanisms for orientation are often used to measure the 3D coordinates of large numbers of points from the surface of the object. In the context of automatic RE, however, we are only interested in systems where the scanner orientation is controlled by the RE software itself.

In this paper we have presented a method for automatic path planning capable of planning paths along 3D curves with continuously changing viewing direction. The advantage of this method is that it automatically adopts to the shape of the object and thus, if possible will scan the object using an orientation of the scanner which gives the most accurate result. It will also try to avoid occlusions which is important for objects with complex shape to be fully covered. In [8] Table 1 Scott et al. list a number of requirements for view planning. We believe that our method fulfils the following requirements: • Generalizable algorithm. Our method is general in the sense that it is adoptable to a robot of different size or a scanner with different specifications. • Generalizable viewpoints. Our scanner is moving along 3D curves with continuously changing viewing direction. • View overlap. Successive scans in our method always overlap. • Self-terminating. Our method is self-terminating. • Limited a priori knowledge. Our method does not need any a priori knowledge. Bounding box and centroid are established automatically. • Shape constraints. Our method does not imply shape constraints. • Frustum. The sensor frustum is modelled, in our text referred to as the scanning window or scanning volume. • Shadow effect. We model shadow effects (occlusions) as a part of the classification of air points, but also to decrease the number of occlusions. • 6D pose. Our method uses six degrees of freedom.