Title :
Object recognition: Bin-picking for industrial use
Author :
Spenrath, Felix ; Palzkill, Matthias ; Pott, Andreas ; Verl, A.
Author_Institution :
Fraunhofer IPA, Stuttgart, Germany
Abstract :
This paper shows a method for object pose detection and gripping point determination that is successfully applied to industrial applications running a three-shift system. The industrial applications are fully automated feeding systems, commonly known as bin-picking. The proposed method for object detection is a generic approach to detect 6 degrees of freedom of any solid objects with arbitrary geometry. The proposed method is using 3D range data and is based on a heuristic tree search using a 6D pose voting scheme. For gripping point determination the object removal is simulated using the range data and a CAD model of the gripper in order to avoid any kind of collisions. During long-term operations in different use-cases, the method showed its usability regarding the crucial requirements, such as robustness, accuracy, portability and speed.
Keywords :
CAD; collision avoidance; control engineering computing; grippers; industrial robots; object detection; object recognition; pose estimation; production engineering computing; robot vision; search problems; trees (mathematics); 3D range data; 6D pose voting scheme; CAD model; arbitrary geometry; automated feeding systems; bin-picking; collisions avoidance; gripping point determination; heuristic tree search; industrial applications; industrial use; object pose detection; object recognition; object removal; three-shift system; Indexes; Robot sensing systems; Robustness; Automated feeding system; gripping point determination; object pose detection;
Conference_Titel :
Robotics (ISR), 2013 44th International Symposium on
Conference_Location :
Seoul
DOI :
10.1109/ISR.2013.6695743