Grasp quality measures: review and performance

Autonomous Robots, Sep 2014

The correct grasp of objects is a key aspect for the right fulfillment of a given task. Obtaining a good grasp requires algorithms to automatically determine proper contact points on the object as well as proper hand configurations, especially when dexterous manipulation is desired, and the quantification of a good grasp requires the definition of suitable grasp quality measures. This article reviews the quality measures proposed in the literature to evaluate grasp quality. The quality measures are classified into two groups according to the main aspect they evaluate: location of contact points on the object and hand configuration. The approaches that combine different measures from the two previous groups to obtain a global quality measure are also reviewed, as well as some measures related to human hand studies and grasp performance. Several examples are presented to illustrate and compare the performance of the reviewed measures.

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Grasp quality measures: review and performance

Mximo A. Roa 0 Ral Surez 0 Grasping 0 Manipulation 0 Robotic hands 0 0 R. Surez Institute of Industrial and Control Engineering (IOC), Universitat Politcnica de Catalunya (UPC) , 08028 Barcelona, Spain The correct grasp of objects is a key aspect for the right fulfillment of a given task. Obtaining a good grasp requires algorithms to automatically determine proper contact points on the object as well as proper hand configurations, especially when dexterous manipulation is desired, and the quantification of a good grasp requires the definition of suitable grasp quality measures. This article reviews the quality measures proposed in the literature to evaluate grasp quality. The quality measures are classified into two groups according to the main aspect they evaluate: location of contact points on the object and hand configuration. The approaches that combine different measures from the two previous groups to obtain a global quality measure are also reviewed, as well as some measures related to human hand studies and grasp performance. Several examples are presented to illustrate and compare the performance of the reviewed measures. Grasping and manipulation with complex grippers, such as multifingered and/or underactuated hands, is an active research area in robotics. The goal of a grasp is to achieve a desired object constraint in the presence of external distur- - bances (including the objects own weight). Robot grasp synthesis is strongly related to the problems of fixture design for industrial parts (Brost and Goldberg 1996; Wang 2000) and design of cable-driven robots (Bruckmann and Pott 2013). Dexterous manipulation involves changing the objects position with respect to the hand without any external support. Grasp planning includes the determination of finger contact points on the object and the choice of an appropriate gripper configuration. Two approaches have been used to solve this problem (Sahbani et al. 2012; Mishra and Silver 1989): an empirical (physiological) approach, trying to mimic the behavior of the human hand (Feix et al. 2009; Cutkosky 1989), and an analytical (mechanical) approach, considering the physical and mechanical properties involved in grasping (Shimoga 1996). The empirical grasp synthesis chooses the most appropriate hand configuration for the object and task to be performed using tools such as learning by demonstration (Aleotti and Caselli 2010; Jakel et al. 2010; Kroemer et al. 2010), neural networks (Pedro et al. 2013; Leoni et al. 1998), fuzzy logic (Bowers and Lumia 2003), or knowledge-based systems (Bekey et al. 1993). Analytical grasp synthesis relies on mathematical models of the interaction between the object and the hand. It has been used for 2D polygonal (Liu 2000) and non-polygonal (Cornell and Surez 2005a) objects, and for 3D polyhedral objects (Ponce et al. 1997), objects based on complex surfaces (Zhu and Wang 2003) or 3D discrete objects (Liu et al. 2004c; Roa and Surez 2009b). A recent survey on grasp planning methods for 3D objects is presented in (Sahbani et al. 2012). Grasp synthesis algorithms take into account the following basic properties: Disturbance resistance: a grasp can resist disturbances in any direction when object immobility is ensured, either by finger positions (form closure) or, up to a certain magnitude, by the forces applied by the fingers (force closure) (Bicchi 1995; Rimon and Burdick 1996). Main problem: determination of contact points on the object boundary. Dexterity: a grasp is dexterous if the hand can move the object in a compatible way with the task to be performed. When there are no task specifications, a grasp is considered dexterous if the hand is able to move the object in any direction (Shimoga 1996). Main problem: determination of hand configuration. Equilibrium: a grasp is in equilibrium when the resultant of forces and torques applied on the object (by the fingers and external disturbances) is null (Kerr and Roth 1986; Buss et al. 1996; Liu 1999; Liu et al. 2004a). Main problem: determination and control of the proper contact forces. Stability: a grasp is stable if any error in the object position caused by a disturbance disappears in time after the disturbance vanishes (Howard and Kumar 1996; Lin et al. 1997; Bruyninckx et al. 1998). Main problem: control of restitution forces when the grasp is moved away from equilibrium. In general, given an object and a hand there is more than one grasp that fulfills a desired property; therefore, an optimal grasp is chosen using a quality measure, i.e. an index that quantifies the goodness of a grasp. This paper presents a review of the grasp quality measures related to disturbance resistance and dexterity, the first two properties to be considered in analytical grasp synthesis. Examples and weak and strong points in each case are also given. Most quality measures have been developed for fingertip precision grasps; the extension of these measures to underactuated and power g (...truncated)


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Máximo A. Roa, Raúl Suárez. Grasp quality measures: review and performance, Autonomous Robots, 2015, pp. 65-88, Volume 38, Issue 1, DOI: 10.1007/s10514-014-9402-3