Abstract
This study focuses on the opening width of parallel grippers in bin picking and proposes a method to optimize this width based on the evaluation of graspability in distance images. The method relies on fast graspability evaluation (FGE), which reduces the robot's grasp position calculation to a convolution problem between binary images representing the object and hand. This approach determines the hand parameters that yield the highest graspability score for the entire bin-picking scene. When introduced in multi-bin-picking tasks involving eight types of industrial parts, this optimization method demonstrated improvements in grasp success rates of up to 35% and an average improvement of approximately 14.4%. The proposed method confirms the importance of optimizing hand parameters in challenging industrial bin-picking scenarios.
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