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Cast parts have inconsistent geometry and grinding and deburring operations is to be carried out based on individual observation of every single workpiece. Normally, these operations are carried out manually by humans. However, due to the health risk associated with the grinding process, there is a strong incentive to explore new automated solutions. The industrial robot is viewed as a strong component for this job.Programming industrial robots is traditionally done by Teach or Offline programming methodologies. Both methods encounter problems in grinding/deburring operations. In traditional Offline programming the robot path is generated from a CAD model of the workpiece. This CAD model holds no information on the irregularities (burrs) and then the necessary path cannot be created. This paper presents a new approach for supervised robot programming, which allows new field of application of industrial robots. In the near future, automatization of manufacturing processes with industrial robots in small and medium sized enterprise would be common. Instead of a costly, but fully automated solution, which works only from CAD model of workpiece and does not provide 100% satisfying result, an operator is involved in robot programming. The result is a 90% automated solution with the expertise of the worker. This interactive vision-based robot programming adds the required information into the Offline programming environment. Thus, location and shape of any irregularities can be identified and the necessary deburring path created.

Driven by the need for higher -flexibility and -speed during initial programming and path adjustments, new robot programming methodologies quickly arises. The traditional "Teach" and "Offline" programming methodologies have distinct weaknesses in machining applications. "Teach" programming is time consuming when used on freeform surfaces or in areas with limited visibility /accessibility. Also, "Teach" programming only relates to the real work-piece and there is no connection to the ideal CAD model of the work-piece. Vice versa during offline programming there is no knowledge about the real work-piece, only the ideal CAD model is used. To be able to relate to both the real-and ideal-model of the work-piece is especially important in machining operations where the difference between the models often represents the necessary material removal . In this chapter an introduction to a programming methodology especially targeted for machining applications is given. This methodology use a single camera combined with image processing algorithms like edge-and colour-detection, combines information of the real and ideal work-piece and represents a human friendly and effective approach for robot programming in machining operations.

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Until recently, underwater intervention tasks have been conducted by human divers alone or in co-operation with remotely operated vehicles (ROV' s). Divers are flexible and can perform a lot of different work task, but diving operations are expensive and there is a great deal of risk to enter such a hostile environment. Oil drilling operations around the world are moved into deeper waters compared with those only a few years ago, and the humans can not be present in water depths below 500-600 meters maximum. So to be able to interfere with the sub-sea installations, to conduct inspection, repair and maintenance (IRM) operations there is a need to have automatic equipment with the necessary capability to perform both planned and unplanned intervention tasks.This paper gives a general architecture for development of ROV-manipulator systems used in underwater intervention operations. First we identify a set of intervention operations, then put focus on the capability requirements connected to these operations. Operations (tasks) and capabilities make a requirement 2Dmatrix. Then we identify all sub-systems that will be effected by the requirement matrix. From this a 3D architecture for methodology research in the development of ROV-manipulator systems arise.