The problem of grasping novel objects in a fully automatic way has gained increasing importance. In this work we consider the problem of grasping novel objects with the help of a laser range scanner. This includes autonomous object detection and grasp motion planning. The used system consists of a fixed working station equipped with a laser range scanner, a seven degrees of freedom manipulator and a hand prosthesis as gripper. We present a method for segmentation of a 2 1 / 2 D point cloud into parts, assembly of parts into objects and calculation of grasping points, which works for cylindrical objects and arbitrary objects. We successfully demonstrate this approach by grasping a variety of different shapes and present a step towards full automation.

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Nowadays, robust and light-weight parts used in the automobile and aeronautics industry are made of carbon fibres. To increase the mechanical toughness of the parts the carbon fibres are stitched in the preforming process using a sewing robot. However, current systems miss high flexibility and rely on manual programming of each part. The main target of this work is to develop an automatic system that autonomously sets the structure strengthening seams. Therefore, a rapid and flexible following of the carbon textile edges is required. Due to the black and reflective carbon fibres a laser-stripe sensor is necessary and the processing of the range data is a challenging task. The paper proposes a real time approach where different edge detection methodologies are combined in a voting scheme to increase the edge tracking robustness. The experimental results demonstrate the feasibility of a fully automated, sensorguided robotic sewing process. The seam can be located to within 0.65mm at a detection rate of 99.3% for individual scans.

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