The availability of grasp quality measures is fundamental for grasp planning and control, and also to drive designers in the definition and optimization of robotic hands. This work investigates on grasp robustness and quality indexes that can be applied to power grasps with underactuated and compliant hands. When dealing with such types of hands, there is the need of an evaluation method that takes into account the forces that can be actually controlled by the hand, depending on its actuation system. In this paper, we study the potential contact robustness and the potential grasp robustness (PCR, PGR) indexes. They both consider main grasp properties: contact points, friction coefficient, etc., but also hand degrees of freedom and consequently, the directions of controllable contact forces. The PCR comes directly from the classical grasp theory and can be easily evaluated, but often leads to too conservative solutions, particularly when the grasp has many contacts. The PGR is more complex and computationally heavier, but gives a more realistic, even if still conservative, estimation of the overall grasp robustness, also in power grasps. We evaluated the indexes for various simulated grasps, performed with underactuated and compliant hands, and we analyzed their variations with respect to the main grasp parameters.
|Titolo:||On Grasp Quality Measures: Grasp Robustness and Contact Force Distribution in Underactuated and Compliant Robotic Hands|
|Rivista:||IEEE ROBOTICS AND AUTOMATION LETTERS|
|Citazione:||Pozzi, M., Malvezzi, M., & Prattichizzo, D. (2017). On Grasp Quality Measures: Grasp Robustness and Contact Force Distribution in Underactuated and Compliant Robotic Hands. IEEE ROBOTICS AND AUTOMATION LETTERS, 2(1), 329-336.|
|Appare nelle tipologie:||1.1 Articolo in rivista|
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|Grasp quality measures.pdf||Accepted version. European Union (EU), Horizon 2020 programme. European project “Synergy-based Open-source Foundations and Technologies for Prosthetics and RehabilitatiOn” (SoftPro), Research & Innovation Action, Grant Agreement n. 688857. © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Digital Object Identifier (DOI): 10.1109/LRA.2016.2612304||Post-print||PUBBLICO - Pubblico con Copyright||Open Access Visualizza/Apri|