By Dirk Buchholz
This e-book is dedicated to at least one of the main well-known examples of automation dealing with initiatives – the “bin-picking” challenge. to select up gadgets, scrambled in a field is a straightforward activity for people, yet its automation is particularly advanced. during this e-book 3 diversified methods to resolve the bin-picking challenge are defined, exhibiting how glossy sensors can be utilized for effective bin-picking in addition to how vintage sensor ideas may be utilized for novel bin-picking suggestions. 3D aspect clouds are to begin with used as foundation, making use of the recognized Random pattern Matching set of rules paired with a truly effective intensity map established collision avoidance mechanism leading to a really powerful bin-picking method. lowering the complexity of the sensor info, all computations are then performed on intensity maps. this enables using 2nd picture research concepts to meet the projects and leads to actual time facts research. mixed with force/torque and acceleration sensors, a close to time optimum bin-picking procedure emerges. finally, floor basic maps are hired as a foundation for pose estimation. unlike identified methods, the traditional maps aren't used for 3D information computation yet without delay for the thing localization challenge, permitting the appliance of a brand new classification of sensors for bin-picking.
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Additional resources for Bin-Picking: New Approaches for a Classical Problem
11c). To analyze the translational DOFs, a simple correlation-like procedure of the scan and the gripper images is performed using the free DOF. The gripper images are shifted pixel-wise along the axis of the free DOF over the rendered scan image. Then, at each step, the rendered images are analyzed pixel wise for collisions as explained above. The collision volume is stored for each step resulting in a 1D collision function dependent of the variable parameter. Using this function, all parameter values below a predefined collision threshold tc are located and used to build a distance map for all possible collisions (Fig.
Dvw p −p w v A similar adaptation has also been used in  where also no normals are available for the localization problem. The relative transformation of two equal triangles can then be calculated by estimating the relative transformation of the local coordinate systems defined on each of the triangles. The centroid of the triangle defines its origin. The x-axis points from the centroid to the middle of the shortest triangle side, the z-axis is defined by the normal of the triangle pointing towards the sensor and Fig.
C Simplified model, sufficient and used for the KGF concept model’s coordinate system, a set of degrees of freedom (DOF) and an associated range for each DOF in which this base pose can be varied. This concept will still allow a wide variety of grasping positions (due to a quasi continuous variation of poses), while maintaining the demands of the industry to be in control of which region of the object classifies as a picking position. Furthermore, KGFs can easily be defined using the CAD model.