Fast and Precise Detection of Object Grasping Positions with Eigenvalue Templates

Abstract

Fast Graspability Evaluation (FGE) has been proposed as a method for detecting grasping positions on objects and is now being used for industrial robots. FGE uses convolution of hand templates with regions on the target object to estimate the optimum grasping posture. However, the hand opening width and rotation angles must be set with high resolution to achieve highly accurate results and the computational load is high. To address that issue, we propose a method in which hand templates are represented in compact form for faster processing by using singular value decomposition. Applying singular value decomposition enables hand templates to be represented as linear combinations of a small number of eigenvalue templates and eigenfunctions. Eigenfunctions take discrete values, but response values can be calculated with arbitrary parameters by fitting a continuous function. Experimental results show that the proposed method reduces computation time by two thirds while maintaining the same detection accuracy as conventional FGE for both parallel hands and three-finger hands.