Exploiting In-Hand Knowledge in Hybrid Joint-Cartesian Mapping for Anthropomorphic Robotic Hands

Abstract

Replication of human hand motions on anthropomorphic robotic hands is typically treated in literature as the combination of two sub-problems: the measurement of human hand motions, and the mapping of such motions on the robotic hand. In this letter we focus on the second one. Different approaches have been proposed to deal with this problem, but none of them preserves both master finger shapes and fingertip positions on the robotic hand, i.e. ensuring predictability and natural motion for the teleoperator. In this article, we propose a novel hybrid approach that combines both joint and Cartesian mappings in a single solution. In particular, we exploit the a priori, in-hand information related to the areas of the workspace in which thumb and finger fingertips can get in contact. This allows to define, for each finger, a zone of transition from joint to Cartesian mapping. As a consequence, both hand shape during volar grasps and correctness of the fingertip positions for precision grasps are preserved, despite the master-slave kinematic dissimilarities. The proposed hybrid mapping is presented and experimentally evaluated both in simulation and with a real slave anthropomorphic robotic hand.