By Dirk Buchholz
This publication is dedicated to at least one of the main recognized examples of automation dealing with initiatives – the “bin-picking” challenge. to select up items, scrambled in a field is a simple job for people, yet its automation is especially complicated. during this ebook 3 diversified methods to resolve the bin-picking challenge are defined, displaying how glossy sensors can be utilized for effective bin-picking in addition to how vintage sensor ideas will be utilized for novel bin-picking suggestions. 3D element clouds are to start with used as foundation, using 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 strategy. decreasing the complexity of the sensor information, all computations are then performed on intensity maps. this permits using second picture research recommendations to satisfy the initiatives and leads to genuine time facts research. mixed with force/torque and acceleration sensors, a close to time optimum bin-picking process emerges. finally, floor common maps are hired as a foundation for pose estimation. unlike recognized techniques, the traditional maps should not used for 3D facts computation yet at once for the item localization challenge, permitting the applying 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
The second set of experiments was performed with the Ruler, which is more accurate. As objects served a set of industrial metal parts, which were scrambled unmixed in the bin. As can be seen in the simulated experiments above, the amount of noise effects the accuracy and the computation time of the localization approach. The performance of the RANSAM algorithm is still very good and even with the LMS400, the experiments showed good results. LMS400 Experiment Series. To test the applicability, the piston rod of the industrial parts data set was used.
The analysis for a vacuum gripper is trivial. It is suitable to find a planar region and no orientation has to be estimated. Now, only one additional parameter, the approach depth da , has to be found. This parameter contains information about how far the gripper may approach the gripper pose and can easily be estimated using the pixels at the computed jaw positions. In this context, the jaw position means the hull of the footprint of the fingers during their close or open motion. Subtracting z g from the minimum depth of all pixels covered by the gripper footprint yields da .
Each localization took 4 s. The overall pick and place success rate was 95 % at 100 trials. The applied collision avoidance was only checking for collisions at predefined, fixed grasp poses defined relative to the models’ coordinate systems. All unsuccessful grasp attempts did not result in collisions due to the collision avoidance mechanism but resulted in time overhead due to empty placement movements of the robot. 3 Experimental 3D Point Cloud Based Pose Estimation 35 Fig. 18 Visual comparison of the scan quality of the SICK LMS and Sick IVP Ruler.