Mapping Finger Motions on Anthropomorphic Robotic Hands: Two Realizations of a Hybrid Joint-Cartesian Approach Based on Spatial In-Hand Information

In literature, two sub-problems are typically identified for the replication of human finger motions on artificial hands: the measurement of the motions on the human side, and the mapping method of human hand movements on the robotic hand. In this study, we focus on the second sub-problem. During human to robot hand mapping, ensuring natural motions and predictability for the operator is a difficult task, since it requires to preserve the Cartesian position of the fingertips and the finger shapes given by the joint values. Several approaches have been presented to deal with this problem, which is still unresolved in general. In this work, we propose an approach for combining joint and Cartesian mapping in a single method. More specifically, we exploit the spatial information available in-hand, in particular, related to the thumb-fingers relative position. In this way, it is possible to perform both volar grasps (where the preservation of finger shapes is more important) and precision grips (where the preservation of fingertip positions is more important) during primary-to-target hand mappings, even if kinematic dissimilarities are present. We therefore report for two specific realizations of this approach: a distance-based hybrid mapping, in which the transition between joint and Cartesian mapping is driven by the approaching of the fingers to the current thumb fingertip position, and a workspace-based hybrid mapping, in which the joint-Cartesian transition is defined on the areas of the workspace in which thumb and finger fingertips can get in contact.