On grasp choice, grasp models, and the design of hands for manufacturing tasks
01 Jun 1989-Vol. 5, Iss: 3, pp 269-279
TL;DR: Comparisons of the grasp taxonomy, the expert system, and grasp-quality measures derived from the analytic models reveal that the analytic measures are useful for describing grasps in manufacturing tasks despite the limitations in the models.
Abstract: Current analytical models of grasping and manipulation with robotic hands contain simplifications and assumptions that limit their application to manufacturing environments. To evaluate these models, a study was undertaken of the grasps used by machinists in a small batch manufacturing operation. Based on the study, a taxonomy of grasps was constructed. An expert system was also developed to clarify the issues involved in human grasp choice. Comparisons of the grasp taxonomy, the expert system, and grasp-quality measures derived from the analytic models reveal that the analytic measures are useful for describing grasps in manufacturing tasks despite the limitations in the models. In addition, the grasp taxonomy provides insights for the design of versatile robotic hands for manufacturing. >
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TL;DR: A critical overview of soft robotic grippers is presented, covering different material sets, physical principles, and device architectures, and improved materials, processing methods, and sensing play an important role in future research.
Abstract: Advances in soft robotics, materials science, and stretchable electronics have enabled rapid progress in soft grippers. Here, a critical overview of soft robotic grippers is presented, covering different material sets, physical principles, and device architectures. Soft gripping can be categorized into three technologies, enabling grasping by: a) actuation, b) controlled stiffness, and c) controlled adhesion. A comprehensive review of each type is presented. Compared to rigid grippers, end-effectors fabricated from flexible and soft components can often grasp or manipulate a larger variety of objects. Such grippers are an example of morphological computation, where control complexity is greatly reduced by material softness and mechanical compliance. Advanced materials and soft components, in particular silicone elastomers, shape memory materials, and active polymers and gels, are increasingly investigated for the design of lighter, simpler, and more universal grippers, using the inherent functionality of the materials. Embedding stretchable distributed sensors in or on soft grippers greatly enhances the ways in which the grippers interact with objects. Challenges for soft grippers include miniaturization, robustness, speed, integration of sensing, and control. Improved materials, processing methods, and sensing play an important role in future research.
1,028 citations
TL;DR: RBO Hand 2 is presented, a highly compliant, underactuated, robust, and dexterous anthropomorphic hand that is inexpensive to manufacture and the morphology can easily be adapted to specific applications, and it is demonstrated that complex grasping behavior can be achieved with relatively simple control.
Abstract: The usefulness and versatility of a robotic end-effector depends on the diversity of grasps it can accomplish and also on the complexity of the control methods required to achieve them. We believe that soft hands are able to provide diverse and robust grasping with low control complexity. They possess many mechanical degrees of freedom and are able to implement complex deformations. At the same time, due to the inherent compliance of soft materials, only very few of these mechanical degrees have to be controlled explicitly. Soft hands therefore may combine the best of both worlds. In this paper, we present RBO Hand 2, a highly compliant, underactuated, robust, and dexterous anthropomorphic hand. The hand is inexpensive to manufacture and the morphology can easily be adapted to specific applications. To enable efficient hand design, we derive and evaluate computational models for the mechanical properties of the hand's basic building blocks, called PneuFlex actuators. The versatility of RBO Hand 2 is evaluated by implementing the comprehensive Feix taxonomy of human grasps. The manipulator's capabilities and limits are demonstrated using the Kapandji test and grasping experiments with a variety of objects of varying weight. Furthermore, we demonstrate that the effective dimensionality of grasp postures exceeds the dimensionality of the actuation signals, illustrating that complex grasping behavior can be achieved with relatively simple control.
867 citations
Cites background or methods from "On grasp choice, grasp models, and ..."
...The taxonomy encompasses 33 grasp types, out of which the first 17 are identical to the grasps in the Cutkosky taxonomy [7]....
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...By the term dexterous, we refer to the postural variability of the hand: the higher this variability, the more dexterous we consider a hand (for examples of grasping postures, refer to the grasp taxonomies presented in Cutkosky (1989) and Feix et al. (2009))....
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...The taxonomy encompasses 33 grasp types, out of which the first 17 are identical to the grasps in the Cutkosky taxonomy (Cutkosky, 1989)....
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...grasping with postural variability comparable to that observed in human grasping (see, for example, the grasp taxonomies of Cutkosky [7] and Feix et al....
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TL;DR: This paper reviews the state-of-the-art in force and tactile sensing technologies that can be suitable within the specific context of dexterous in-hand manipulation and provides a review of models describing human hand activity and movements.
Abstract: As the field of robotics is expanding from the fixed environment of a production line to complex human environments, robots are required to perform increasingly human-like manipulation tasks, moving the state-of-the-art in robotics from grasping to advanced in-hand manipulation tasks such as regrasping, rotation and translation. To achieve advanced in-hand manipulation tasks, robotic hands are required to be equipped with distributed tactile sensing that can continuously provide information about the magnitude and direction of forces at all contact points between them and the objects they are interacting with. This paper reviews the state-of-the-art in force and tactile sensing technologies that can be suitable within the specific context of dexterous in-hand manipulation. In previous reviews of tactile sensing for robotic manipulation, the specific functional and technical requirements of dexterous in-hand manipulation, as compared to grasping, are in general not taken into account. This paper provides a review of models describing human hand activity and movements, and a set of functional and technical specifications for in-hand manipulation is defined. The paper proceeds to review the current state-of-the-art tactile sensor solutions that fulfil or can fulfil these criteria. An analytical comparison of the reviewed solutions is presented, and the advantages and disadvantages of different sensing technologies are compared.
692 citations
TL;DR: This article presents a survey of the existing computational algorithms meant for achieving four important properties in autonomous multifingered robotic hands, collectively referred to in this article as robot grasp synthesis algorithms.
Abstract: This article presents a survey of the existing computational algorithms meant for achieving four important properties in autonomous multifingered robotic hands. The four properties are: dexterity, equilibrium, stability, and dynamic behavior The multifingered robotic hands must be controlled so as to possess these properties and hence be able to autonomously perform complex tasks in a way similar to human hands.Existing algorithms to achieve dexterity primarily involve solving an unconstrained linear programming problem where an objective function can be chosen to represent one or more of the currently known dexterity measures. Algorithms to achieve equilibrium also constitute solving a linear program ming problem wherein the positivity, friction, and joint torque constraints of all fingers are accounted for while optimizing the internal grasping forces. Stability algorithms aim at achiev ing positive definite grasp impedance matrices by solving for the required fingertip impedances. This problem reduces ...
671 citations
TL;DR: The resulting taxonomy incorporates all grasps found in the reviewed taxonomies that complied with the grasp definition and is shown that due to the nature of the classification, the 33 grasp types might be reduced to a set of 17 more generalgrasps if only the hand configuration is considered without the object shape/size.
Abstract: In this paper, we analyze and compare existing human grasp taxonomies and synthesize them into a single new taxonomy (dubbed “The GRASP Taxonomy” after the GRASP project funded by the European Commission). We consider only static and stable grasps performed by one hand. The goal is to extract the largest set of different grasps that were referenced in the literature and arrange them in a systematic way. The taxonomy provides a common terminology to define human hand configurations and is important in many domains such as human–computer interaction and tangible user interfaces where an understanding of the human is basis for a proper interface. Overall, 33 different grasp types are found and arranged into the GRASP taxonomy. Within the taxonomy, grasps are arranged according to 1) opposition type, 2) the virtual finger assignments, 3) type in terms of power, precision, or intermediate grasp, and 4) the position of the thumb. The resulting taxonomy incorporates all grasps found in the reviewed taxonomies that complied with the grasp definition. We also show that due to the nature of the classification, the 33 grasp types might be reduced to a set of 17 more general grasps if only the hand configuration is considered without the object shape/size.
636 citations
Cites background or methods from "On grasp choice, grasp models, and ..."
...The taxonomy of Cutkosky [53], which is widely used in the field of robotics, lists 15 different grasps....
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...While Cutkosky [53] distinguishes grasps also by object size, many other authors do not....
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...[51] M. Cutkosky and P. Wright, “Modeling manufacturing grips and correlations with the design of robotic hands,” in Proc....
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...As Cutkosky mainly differentiates grasps by the object properties, this reduction is only natural....
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...[53] M. R. Cutkosky, “On grasp choice, grasp models, and the design of hands for manufacturing tasks,” IEEE Trans....
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References
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TL;DR: It is shown that movements of the hand consist of two basic patterns of movements which are termed precision grip and power grip, which appear to cover the whole range of prehensile activity of the human hand.
Abstract: 1. The prehensile movements of the hand as a whole are analysed from both an anatomical anda functional viewpoint. 2. It is shown that movements of the hand consist of two basic patterns of movements which are termed precision grip and power grip. 3. In precision grip the object is pinched between the flexor aspects of the fingers and that of the opposing thumb. 4. In power grip the object is held as in a clamp between the flexed fingers and the palm, counter pressure being applied by the thumb lying more or less in the plane of the palm. 5. These two patterns appear to cover the whole range of prehensile activity of the human hand.
1,446 citations
TL;DR: It was found that the applied grip force was critically balanced to optimize the motor behaviour so that slipping between the skin and the gripped object did not occur and the grip force did not reach exeedingly high values.
Abstract: A small object was gripped between the tips of the index finger and thumb and held stationary in space. Its weight and surface structure could be changed between consecutive lifting trials, without changing its visual appearance. The grip force and the vertical lifting force acting on the object, as well as the vertical position of the object were continuously recorded. Likewise, the minimal grip force necessary to prevent slipping, was measured. The difference between this minimal force and the employed grip force, was defined as the safety margin to prevent slipping.
954 citations
TL;DR: This paper discusses three fundamental problems relating to grasping and manipulating objects within an articulated, multifingered hand: determining how hard to squeeze an ob ject in order to ensure a secure grasp, determining the finger- joint motions required to produce a desired motion of the object, and determining the workspace of the hand.
Abstract: This paper discusses three fundamental problems relating to grasping and manipulating objects within an articulated, multifingered hand: determining how hard to squeeze an ob ject in order to ensure a secure grasp, determining the finger- joint motions required to produce a desired motion of the object, and determining the workspace of the hand.Squeezing the object, or the application of internal grasp forces, is reduced to a linear programming problem which considers friction and joint torque limit constraints. The relationship between the finger-joint motions and the motion of the object, for the case of pure rolling between the finger tips and the object, is formulated as a set of differential equa tions. The total workspace for a hand is determinedfor spe cial cases of planar and spatial hands.
864 citations
Book•
30 May 1985TL;DR: This book, based on the doctoral dissertations of the two authors, examines several aspects of manipulating objects and believes that better industrial robots are presented by understanding the principles discussed.
Abstract: Robot Hands and the Mechanics of Manipulation explores several aspects of the basic mechanics of grasping, pushing, and in general, manipulating objects. It makes a significant contribution to the understanding of the motion of objects in the presence of friction, and to the development of fine position and force controlled articulated hands capable of doing useful work. In the book's first section, kinematic and force analysis is applied to the problem of designing and controlling articulated hands for manipulation. The analysis of the interface between fingertip and grasped object then becomes the basis for the specification of acceptable hand kinematics. A practical result of this work has been the development of the Stanford/JPL robot hand - a tendon-actuated, 9 degree-of-freedom hand which is being used at various laboratories around the country to study the associated control and programming problems aimed at improving robot dexterity. Chapters in the second section study the characteristics of object motion in the presence of friction. Systematic exploration of the mechanics of pushing leads to a model of how an object moves under the combined influence of the manipulator and the forces of sliding friction. The results of these analyses are then used to demonstrate verification and automatic planning of some simple manipulator operations. Matthew T. Mason is Assistant Professor of Computer Science at Carnegie-Mellon University, and coeditor of Robot Motion (MIT Press 1983). J. Kenneth Salisbury, Jr. is a Research Scientist at MIT's Artificial Intelligence Laboratory, and president of Salisbury Robotics, Inc. Robot Hands and the Mechanics of Manipulation is 14th in theArtificial Intelligence Series, edited by Patrick Henry Winston and Michael Brady.
807 citations
01 Mar 1987
TL;DR: Three quality measures for evaluating a grasp are proposed and one is task-oriented and needs the development of a procedure for modeling tasks as ellipsoids in the wrench space of the object.
Abstract: The problem of optimal grasping of an object by a multifingered robot hand is discussed. Using screw theory and elementary differential geometry, the concept of a grasp is axiomated and its stability characterized. Three quality measures for evaluating a grasp are then proposed. The last quality measure is task-oriented and needs the development of a procedure for modeling tasks as ellipsoids in the wrench space of the object. Numerical computations of these quality measures and the selection of an optimal grasp are addressed in detail. Several examples are given using these quality measures to show that they are consistent with measurements yielded by the authors' experiments on grasping. >
443 citations