Carnegie Mellon University
Browse

On the Importance of Asymmetries in Grasp Quality Metrics for Tendon Driven Hands

Download (294.2 kB)
journal contribution
posted on 1985-01-01, 00:00 authored by Jiaxin L. Fu, Nancy S. Pollard
Grasp quality measures are important for understanding how to plan for and maintain appropriate and secure grasps for pick and place operations and tool use. Most grasp quality measures assume certain symmetries about the mechanism or the task. For example, contact points may be considered to be independent and identical, or an ellipsoidal measure such as the force manipulability ellipsoid may be used. However, many tasks have strong asymmetries, where wrenches in certain directions dominate. Tendon driven hand designs may also have strong asymmetries, leading to differing abilities to apply contact forces in different directions. This paper begins to explore empirically the validity of some of the symmetry assumptions employed by common grasp quality metrics. We examine the human hand and the shadow robot hand, and find that force abilities vary with finger choice and with location of the contact on the finger for both hands. However, while the human hand shows dramatic changes for different poses due to its asymmetric design, the shadow hand, with a symmetric design shows much smaller changes and resembles the assumption of identical and independent contact points reasonably well. Thus, we suggest that the underlying design of the hand is a very important factor to consider for grasp quality metrics and for grasp planning and control. The specific grasp quality metric we study in this paper also brings together a variety of previous research. We outline a linear programming approach for computing a grasp quality metric that includes tendon force constraints and contact constraints and can handle any task described as a polytope in wrench space

History

Publisher Statement

All Rights Reserved

Date

1985-01-01

Usage metrics

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC