The impact of hand parameter optimization on industrial bin picking
Year: 2024
Type: article
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.
Source: Advanced Robotics
Authors: Yukiyasu Domae, Koshi Makihara, Ryo Hanai
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4403534176