Chapter

Grasping

Abstract and Figures

This chapter introduces fundamental models of grasp analysis. The overall model is a coupling of models that define contact behavior with widely used models of rigid-body kinematics and dynamics. The contact model essentially boils down to the selection of components of contact force and moment that are transmitted through each contact. Mathematical properties of the complete model naturally give rise to five primary grasp types whose physical interpretations provide insight for grasp and manipulation planning. After introducing the basic model and types of grasps, this chapter focuses on the most important grasp characteristic: complete restraint. A grasp with complete restraint prevents loss of contact and thus is very secure. Two primary restraint properties are form closure and force closure. A form closure grasp guarantees maintenance of contact as long as the links of the hand and the object are well approximated as rigid and as long as the joint actuators are sufficiently strong. As will be seen, the primary difference between form closure and force closure grasps is the latterʼs reliance on contact friction. This translates into requiring fewer contacts to achieve force closure than form closure.
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... Based on previous works [15], we identified quality criteria considering the contact points locations and also hand properties. We first introduce the contact modeling to formalize these grasp criteria. ...
Article
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The development of algorithms capable of automatically generating optimal grasp involves first of all the necessity to define the notion of optimal grasp in relation to the target task. To address this problem, the scientific community offers many quality criteria in the literature and continues to propose new ones for grasp synthesis purpose. This paper aims at proposing a synthesis and a fine analysis of the quality criteria useful to evaluate a grasp in a context of adaptive grasping, as well as in the perspective of in-hand manipulation. These criteria are divided in two categories, the first one has 11 criteria and focuses exclusively on the location of contact points while the second one has 5 criteria and takes into account the kinematics of the robotic hand as well. Evaluation of the criteria is proposed with a common evaluation framework based on reference objects and reference manipulation tasks. The evaluation and illustration of the resulting grasps with the different criteria allow to appreciate the physical meaning of each of these criteria with this common evaluation framework. In order to reduce the number of criteria to be used in the context of a grasping synthesis process, a correlation study is carried out. The results show that several criteria in the literature are strongly correlated. Four criteria are finally chosen. Thus, to demonstrate the relevance of the selected criteria, a grasp synthesis process is used for in-hand manipulation purpose. An evolutionary approach is used to solve this multi-criteria optimization problem. The approach is validated in the OpenRAVE simulation environment and then demonstrated with the new RoBioSS hand: a fully actuated dexterous robotic hand with four fingers and sixteen degrees of freedom. Experimental results illustrate the relevance of the choice of these criteria to produce robust grasps leading to stable in-hand manipulations with large amplitudes.
Chapter
The goal of this chapter is to provide a concise overview about the state of the art in dexterous manipulation, both in hardware and software. In the first section, the progress in the development of robotic hands, which are able to support the execution of advanced in-hand manipulation tasks, is reviewed. It culminates in the presentation of the DLR humanoid robot David and, in particular, its anthropomorphic hand, which was the primary research platform for the work in this manuscript.
Chapter
This work is concerned with the development of methods for the localization and control of manipulated objects. However, the discussion of the proposed algorithms first requires a common model of the hand-object system, on which both components can be built. This chapter presents the utilized grasp model.
Conference Paper
Grasp planning and most specifically the grasp space exploration is still an open issue in robotics. This article presents an efficient procedure for exploring the grasp space of a multifingered adaptive gripper for generating reliable grasps given a known object pose. This procedure relies on a limited dataset of manually specified expert grasps, and use a mixed analytic and data-driven approach based on the use of a grasp quality metric and variational autoencoders. The performances of this method are assessed by generating grasps in simulation for three different objects. On this grasp planning task, this method reaches a grasp success rate of 99.91% on 7000 trials.
Chapter
Considerable attention has been paid recently by researchers and developers to manipulator robots (MR) which operate autonomously. Increasing MR autonomy degree allows to significantly reduce, and in some cases, to exclude humans participation in MR management. This issue is especially important if there are restrictions on the communication channels when performing operations in conditions of incomplete information (un-oriented objects, etc.) Tasks of autonomous working are considered related to MR for a wide variety of purposes. One of the most striking manifestations of MR autonomy is space manipulation robots (SMR) which perform assembly operations. Currently, it is planned to perform assembly operations using SMR in autonomous systems when solving problems of constructing and repairing orbital stations, space telescopes and other spacecraft, moreover; in space satellites refueling, etc. This research discusses some issues of assembling objects using MR in an autonomous system. The main idea of this research is based on the general principles of objects automatic grasping theory using multi-finger gripper (MFG) MR. In this work, an approach for solving the problem of objects automatic grasping as one of the assembly operation phases has been proposed. This research describes an experimental hardware and software system. This system has been created at Bauman Moscow state technical University for solving problems of objects automatic grasping and performing assembly operations in an autonomous system. In this work the simplest examples, that clearly illustrate the proposed approach for solving the problem of automatic grasping in one of the assembly operation phases, have been given.
Article
Multi-finger equilibrium grasps form the basic building block for synthesizing secure grasps by robot hands. This letter describes a catalog of the 2-D equilibrium grasps. The letter reviews graphical techniques for synthesizing two and three-finger equilibrium grasps, then describes new techniques for identifying and synthesizing four-finger equilibrium grasps as well as higher number-of-finger grasps. For each of these grasps, the letter describes special geometric properties associated with frictionless and frictional contacts. The letter establishes that every 2-D equilibrium grasp can be realized by two or three finger forces that rely on friction effects, or by four finger forces that need not rely on friction effects. The four finger forces can be realized along the grasped object contact normals and are thus maximally robust with respect to uncertainty in the amount of friction at the contacts. Examples illustrate the techniques and a simple experiment demonstrates the ability of four-finger grasps to align their forces towards the contact normals while maintaining an equilibrium grasp. Analogous properties of the 3-D equilibrium grasps are also summarized.
Article
This paper proposes a controller for stable grasping of unknown-shaped objects by two robotic fingers with tactile fingertips. The grasp is stabilised by rolling the fingertips on the contact surface and applying a desired grasping force to reach an equilibrium state. The validation is both in simulation and on a fully-actuated robot hand (the Shadow Modular Grasper) fitted with custom-built optical tactile sensors (based on the BRL TacTip). The controller requires the orientations of the contact surfaces, which are estimated by regressing a deep convolutional neural network over the tactile images. Overall, the grasp system is demonstrated to achieve stable equilibrium poses on a range of objects varying in shape and softness, with the system being robust to perturbations and measurement errors. This approach also has promise to extend beyond grasping to stable in-hand object manipulation with multiple fingers.
Thesis
Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1982.
Book
1. Introduction.- 2. Previous Investigations of Fine Manipulation and Grasping.- 2.1 Fine Motion and Control.- 2.2 Robotic Wrists.- 2.3 Applications to Assembly and Surface Finishing Tasks.- 2.3.1 Assembly.- 2.3.2 Surface Finishing.- 2.4 Passive Hands and Grippers.- 2.5 Active Hands and Grasping.- 3. Robot Tasks in a Metal-Working Cell.- 3.1 Task Descriptions.- 3.1.1 Materials Handling.- 3.1.2 Assembly.- 3.1.3 Grasping.- 3.1.4 Surface Finishing and Shaping.- 3.1.4.1 Contour following.- 3.1.4.2 Contour modification.- 3.2 Discussion: Coupled Fine and Gross Motions.- 4. A Wrist for Fine-Motion Tasks.- 4.1 Wrist Description.- 4.2 Control Architecture.- 4.3 Experiments.- 4.3.1 Contour Following.- 4.3.1.1 State estimation.- 4.3.1.2 Control law.- 4.3.2 Grinding.- 4.4 Discussion of Results.- 5. Analysis for an Active Robot Hand.- 5.1 The Promise of Further Dexterity.- 5.2 Introduction to Grasp Analysis.- 5.2.1 Grasping Model and Assumptions.- 5.2.2 Stiffness, Strength and Stability of a Grasp.- 5.2.3 Procedure for Establishing Grip Properties.- 5.2.4 Two-Dimensional Examples.- 5.2.4.1 Choosing among five grips: an example.- 5.2.4.2 An unstable example.- 5.3 Extension to Three-Dimensional Problems.- 5.3.1 Forward Force and Displacement Relations.- 5.3.2 Summary of Forward Transformations.- 5.3.3 Finger Motions and Constraints.- 5.3.4 Constraints at a Contact.- 5.3.4.1 Case 1: exactly determined.- 5.3.4.2 Case 2: under determined.- 5.3.4.3 Case 3: over determined.- 5.3.5 Computing Changes in Grip Force.- 5.4 A Closer Look at Contact Conditions.- 5.4.1 Point Contact.- 5.4.2 Curved Finger Contact.- 5.4.2.1 Effects of rolling motion.- 5.4.3 Very Soft Finger.- 5.4.3.1 Effects of deforming fingertip.- 5.4.4 Soft, Curved Fingertips.- 5.5 Examples.- 5.5.1 Pointed Fingers.- 5.5.2 Procedure for Left Finger.- 5.5.2.1 Discussion.- 5.5.3 Curved Fingertips.- 5.5.3.1 Discussion.- 5.5.4 Very Soft Fingers.- 5.5.4.1 Discussion.- 5.6 Summary.- 6. Natural Examples of Grasping.- 6.1 The Human Hand.- 6.1.1 Conformability.- 6.1.2 Muscles.- 6.1.3 Hand/Wrist Interaction.- 6.1.4 Finger Coupling and Specialization.- 6.1.5 Grasps.- 6.1.6 Sensation and Control.- 6.2 Other Natural Examples.- 7. Designing Hands and Wrists for Manufacturing.- 7.1 Wrist Design.- 7.2 Hand Design.- 7.2.1 Grasping vs Manipulation.- 7.3 Control.- 8. Summary and Conclusions.- Appendix for Grasp Analysis.- A.1 Matrix Identities.- A.2 Matrix Method for Under Determined Finger Motions.- A.3 Differential Jacobians.- A.4 Rolling Contact.- A.5 Details for Examples in Section 5.5.- References.
Article
A robot hand with three elastic fingers which are independently driven by individual motors is applied to assembling jobs of two-dimensional mechanical parts.Firstly, the handling force of the active elastic fingers in the prehension situation is analyzed by introducing the potential of the prehension system. Then the parameters of the prehension system with the specified handling force are determined by solving the inverse problems.Next, assembling jobs of two-dimensional mechanical parts are explained with an example. The active elastic fingers handle one objects so as to fit its position and orientation with those of the another object, keeping specified contact forces. The force balance condition during such a positioning process is discussed. Then the control scheme to the required handling force is made clear.Finally, experiments are carried out for verifying the theoretical results and the feasibility of the practical application. Representative forces during the assembly process are measured and the feasible region of the positioning process is determined.
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The abridged contents include: Kinematic and force analysis of articulated hands: contact - freedom and constraint; contacts in groups; force application and velocity analysis; force error analysis. Manipulator grasping and pushing operations: theory of pushing; application; conclusion. Index. This book, based on the doctoral dissertations of the two authors, examines several aspects of manipulating objects. At present, the authors believe that industrial robots are not used effectively. Tasks performed by robot manipulators are now limited to simple packing and stacking operations. By understanding the principles discussed in this book, better industrial robots are presented.
Article
Grasping an object with a multifingered robot hand requires complete constraint of its motion by contacts. Complete constraint of an object can also be described using force equilibrium. If any external force on the object can be balanced by applicable contact forces, a stable grasp has been achieved. A force closure grasp is such a grasp. This article presents a simple and efficient algorithm to find an optimum force closure grasp of a planar polygon using a three-fingered robot hand. The optimum grasp is defined as a grasp that has the minimum value of a heuristic function. The heuristic function is formulated from the consideration of the possible uncertainties inherent in implementation. Even though a planar force closure grasp can be obtained using only two friction contacts, we consider only grasps where all three friction contacts explicitly participate. To find the optimum grasp, all feasible combinations of edges and/or vertices are identified first. An edges and/or vertices combination is feasible when a stable grasp is attainable by an appropriate selection of three contacts on them. Then, using computational geometry, a single grasp is constructed on each feasible combination. Finally, the grasps obtained are compared using the heuristic quality measure. A test of our algorithm on several different polygons shows that the resulting grasp is realistic and is obtained fast enough for real-time use.