Regrasping by Using Active Visual Servoing for Bin-picking and Kitting Applications

Abstract

To automate bin-picking and kitting applications for texture-less objects with utilizing general-purpose hands, changing the grasping pose is required when the grasping pose is not suitable for kitting. We present a regrasping method to change the grasping pose with general-purpose hands mounted on a dual-arm robot. The success rates of the regrasping with general-purpose hands becomes lower due to a grasping error that occurs on the picking process. The proposed scheme corrects the grasping error before the regrasping by active visual servoing. Active visual servoing performs a visual servoing by projecting a pattern to the object. Active visual servoing does not require feature extraction of target parts; thus, positioning accuracy does not depend on the richness of texture. Experimental results show that the proposed method can achieve high success rates for kitting three target objects, compared to the conventional image-based visual servoing method.