By Dirk Buchholz
This booklet is dedicated to 1 of the main well-known examples of automation dealing with projects – the “bin-picking” challenge. to choose up gadgets, scrambled in a field is a straightforward job for people, yet its automation is particularly advanced. during this booklet 3 varied techniques to unravel the bin-picking challenge are defined, exhibiting how smooth sensors can be utilized for effective bin-picking in addition to how vintage sensor techniques could be utilized for novel bin-picking concepts. 3D element clouds are to start with used as foundation, utilizing the identified Random pattern Matching set of rules paired with a truly effective intensity map dependent collision avoidance mechanism leading to a really strong bin-picking method. lowering the complexity of the sensor information, all computations are then performed on intensity maps. this enables using second photograph research innovations to satisfy the initiatives and leads to genuine time info research. mixed with force/torque and acceleration sensors, a close to time optimum bin-picking approach emerges. finally, floor common maps are hired as a foundation for pose estimation. not like recognized techniques, the traditional maps aren't used for 3D information computation yet at once for the thing localization challenge, permitting the appliance of a brand new type of sensors for bin-picking.
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Additional info for Bin-Picking: New Approaches for a Classical Problem
A The scan of the LMS has less occlusions but a much higher amount of noise. b The Ruler has much less noise but a significant amount of holes caused by shadows between laser line and camera. c 5 located piston rods in the LMS scan, showing the robustness of the approach against noise. d 5 located piston rods in the Ruler scan, showing the robustness of the approach against occlusions this setup were the limited accuracy of the 3D scanner and the collision avoidance mechanism. Therefore, after a short set of experiments these parts were replaced and a new set of experiments was performed.
The described steps to compute a valid gripper pose are summarized in Algorithm 1. Note that the output W P G does not contain the pitch and yaw angles since they are fixed by the sensor setup as described above. nor mal; estimate approach depth da ; if da > p then if gripper palm is collision-free then valid pose found; else continue; end else continue; end delete local area in C around (xm , ym ); end transform (xm , ym ) from pixel values into world coordinates; xg ← T (xm ); yg ← T (ym ); Algorithm 1: Gripper pose estimation.
A match between CAD model and scan, its matching quality has to be estimated. Therefore, the subset C of points on the surface of CAD model A which are in contact with the surface of scan B given a relative pose A T B has to be computed. It is assumed that the surfaces are in contact at areas where the distances between surface points are smaller than a predefined ε p : C := a ∈ A | ∃ b ∈ B with pa − A T B pb < ε p . 5) The tolerance value ε p is necessary to handle noise and is therefore adapted to the surface accuracy.