This study proposes a method of robust regrasping an object using a
dual-arm robot with general-purpose hands, which is robust against the
error of grasping. In this paper, one arm is assigned to hand over the
object to the other arm that is named a receiver arm. The grasping error
must be considered to increase the success rate of the regrasping since
a hand-over arm first picks up the object with the general-purpose hand.
In an online phase, the proposed method performs object positioning at
an optimal pose at the time of regrasping using an image-based visual
servoing (IBVS) approach to reduce the effect of the grasping error. In
the planning phase, the proposed method computes the optimal pose for
regrasping by maximizing the minimum singular values of the image
Jacobian of IBVS to achieve a high positioning accuracy using a 3D model
of the target object. To achieve the regrasping objects with various
shapes robustly against image noises and changes in light environments,
the image Jacobian of IBVS is computed by numerical differential using
an actual data set. A large number of data sets corresponding to each
candidate grasp are usually required for computing the image Jacobian.
To reduce the number of data sets, we propose a conversion method of the
image Jacobian requiring only one data set corresponding to one
representative grasp. The experimental results show that the proposed
method achieves regrasping of target objects with the general-purpose
hands with high success rates and performs target object positioning
with less than 0.7[mm] positioning error.