This paper shows a method for object pose detection and gripping point determination that is successfully applied to industrial applications running a three-shift system. The industrial applications are fully automated feeding systems, commonly known as bin-picking. The proposed method for object detection is a generic approach to detect 6 degrees of freedom of any solid objects with arbitrary geometry. The proposed method is using 3D range data and is based on a heuristic tree search using a 6D pose voting scheme. For gripping point determination the object removal is simulated using the range data and a CAD model of the gripper in order to avoid any kind of collisions. During long-term operations in different use-cases, the method showed its usability regarding the crucial requirements, such as robustness, accuracy, portability and speed
The determination of collision free grips is an important aspect of random bin picking, which describes the separation of unordered workpieces stored in bins by an industrial robot. Heuristic search algorithms are an appropriate approach to this problem. In this paper, we analyze the influence of relative height and lateral position of workpieces as well as orientation of tool center points on finding valid grips. From these results, we deduce heuristic functions, which can be used to improve the aforementioned heuristic search algorithms. Being able to reduce the calculation time for grip determination reduces overall cycle time and therefore makes bin picking more applicable for the producing industry
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