Finger design automation for industrial robot grippers: A review <s> Physical Experiment <s> The authors address the problem of planning optimal grasps. Two general optimality criteria that consider the total finger force and the maximum finger force are introduced and discussed. Their formalization using various metrics on a space of generalized forces is detailed. The geometric interpretation of the two criteria leads to an efficient planning algorithm. An example of its use in a robotic environment equipped with two-jaw and three-jaw is described. > <s> BIB001 </s> Finger design automation for industrial robot grippers: A review <s> Physical Experiment <s> Modular fixturing kits are sets of components used for flexible, rapid construction of fixtures. A modular vise is a parallel-jaw vise, each jaw of which is a modular fixture plate with a regular grid of precisely positioned holes. To fixture a part, one places pins in some of the holes so that when the vise is closed, the part is reliably located and completely constrained. The modular vise concept can be adapted easily to the design of modular parallel-jaw grippers for robots. By attaching a grid-plate to each jaw of a parallel-jaw gripper we gain the ability to easily construct high-quality grasps for a wide variety of parts from a standard set of hardware. Wallack and Canny (1994) developed an algorithm for planning planar grasp configurations for the modular vise. We expand this work to produce a 3D fixture/gripper design tool. We describe several analyses we have added to the planar algorithm, including a 3D grasp quality metric based on force information, 3D geometric loading analysis, and inter-gripper interference analysis. Finally, we describe two applications of our code. One of these is an internal application at Sandia, while the other shows a potential use of our code for designing part of an agile assembly line. <s> BIB002 </s> Finger design automation for industrial robot grippers: A review <s> Physical Experiment <s> In support of rapid-response manufacturing, we propose a new technique for automated design and fabrication of fixture and gripper tooling for mechanical assembly. Our technique uses geometric computations on CAD part descriptions to derive form and frictional form closure tooling. In addition, we invoke rapid prototyping technology to fabricate the computed tooling directly from the derived CAD descriptions. This paper introduces the concepts of our approach to automatic tooling generation, illustrates initial results, and surveys the additional work to be done to further exploit the technique. <s> BIB003 </s> Finger design automation for industrial robot grippers: A review <s> Physical Experiment <s> Although parallel-jaw grippers play a vital role in automated manufacturing, gripper surfaces are still designed by trial-and-error. This paper presents an algorithmic approach to designing gripper jaws that mechanically align parts in the vertical (gravitational) plane. We consider optimal edge contacts, based on modular trapezoidal segments that maximize contact between the gripper and the part at its desired final orientation. Given the n-sided 2D projection of an extruded convex polygonal part, mechanical properties such as friction and center of mass, and initial and desired final orientations, we present an O(n3 log n) numerical algorithm to design optimal gripper jaws. We also present an O(n log n) algorithm to compute tolerance classes for these jaws, and report on an online implemented version of the algorithm and physical experiments with the jaws it designed. This paper extends earlier results that generated optimal point contacts [M. T. Zhang and K. Goldberg, “Gripper point contacts for part alignment,” IEEE Trans. Robot. Autom.18(6), 902–910 (2002)]. <s> BIB004 </s> Finger design automation for industrial robot grippers: A review <s> Physical Experiment <s> This paper describes the development of a novel vision-based modeling and grasping system for three-dimensional (3D) objects whose shape and location are unknown a priori. Our approach integrates online computer vision-based 3D object modeling with online 3D grasp planning and execution. A single wrist-mounted video camera is moved around the stationary object to obtain images from multiple viewpoints. Object silhouettes are extracted from these images and used to form a 3D solid model of the object. To refine the model, the object's top surface is modeled by scanning with a wrist-mounted line laser while recording images. The laser line in each image is used to form a 3D surface model that is combined with the silhouette result. The grasp planning algorithm is designed for the parallel-jaw grippers that are commonly used in industry. The algorithm analyses the solid model, generates a robust force closure grasp, and outputs the required gripper position and orientation for grasping the object. The robot then automatically picks up the object. Experiments are performed with two real-world 3D objects, a metal bracket and a hex nut. The shape, position and orientation of the objects are not known by the system a priori. The time required to compute an object model and plan a grasp was less than 4 s for each object. The experimental results demonstrate that the automated grasping system can obtain suitable models and generate successful grasps, even when the objects are not lying parallel to the supporting table. <s> BIB005 </s> Finger design automation for industrial robot grippers: A review <s> Physical Experiment <s> This paper presents a new method for solving the grasp optimization problem by a multi-finger robotic hand; this method allows gripping an object using three articulated fingers, in order to manipulate it later. Because of the large number of operations and the high computation time, online grasp has not yet been reported. In this study, we propose a method that is able to provide an optimized initial grasp in a short time before online manipulation. <s> BIB006 </s> Finger design automation for industrial robot grippers: A review <s> Physical Experiment <s> We present a novel and simple experimental method called physical human interactive guidance to study human-planned grasping. Instead of studying how the human uses his/her own biological hand or how a human teleoperates a robot hand in a grasping task, the method involves a human interacting physically with a robot arm and hand, carefully moving and guiding the robot into the grasping pose, while the robot's configuration is recorded. Analysis of the grasps from this simple method has produced two interesting results. First, the grasps produced by this method perform better than grasps generated through a state-of-the-art automated grasp planner. Second, this method when combined with a detailed statistical analysis using a variety of grasp measures (physics-based heuristics considered critical for a good grasp) offered insights into how the human grasping method is similar or different from automated grasping synthesis techniques. Specifically, data from the physical human interactive guidance method showed that the human-planned grasping method provides grasps that are similar to grasps from a state-of-the-art automated grasp planner, but differed in one key aspect. The robot wrists were aligned with the object's principal axes in the human-planned grasps (termed low skewness in this paper), while the automated grasps used arbitrary wrist orientation. Preliminary tests show that grasps with low skewness were significantly more robust than grasps with high skewness (77-93%). We conclude with a detailed discussion of how the physical human interactive guidance method relates to existing methods to extract the human principles for physical interaction. <s> BIB007 </s> Finger design automation for industrial robot grippers: A review <s> Physical Experiment <s> Grasp quality metrics which analyze the contact wrench space are commonly used to synthesize and analyze preplanned grasps. Preplanned grasping approaches rely on the robustness of stored solutions. Analyzing the robustness of such solutions for large databases of preplanned grasps is a limiting factor for the applicability of data driven approaches to grasping. In this work, we will focus on the stability of the widely used grasp wrench space epsilon quality metric over a large range of poses in simulation. We examine a large number of grasps from the Columbia Grasp Database for the Barrett hand. We find that in most cases the grasp with the most robust force closure with respect to pose error for a particular object is not the grasp with the highest epsilon quality. We demonstrate that grasps can be reranked by an estimate of the stability of their epsilon quality. We find that the grasps ranked best by this method are successful more often in physical experiments than grasps ranked best by the epsilon quality. <s> BIB008 </s> Finger design automation for industrial robot grippers: A review <s> Physical Experiment <s> Although there has been great progress in robot grasp planning, automatically generated grasp sets using a quality metric are not as robust as human-generated grasp sets when applied to real problems. Most previous research on grasp quality metrics has focused on measuring the quality of established grasp contacts after grasping, but it is difficult to reproduce the same planned final grasp configuration with a real robot hand, which makes the quality evaluation less useful in practice. In this study, we focus more on the grasping process, which usually involves changes in contact and object location, and explore the efficacy of using dynamic simulation in estimating the likely success or failure of a grasp in the real environment. Among many factors that can possibly affect the result of grasping, we particularly investigated the effect of considering object dynamics and pose uncertainty on the performance in estimating the actual grasp success rates measured from experiments. We observed that considering both dynamics and uncertainty improved the performance significantly, and when applied to automatic grasp set generation, this method generated more stable and natural grasp sets compared with a commonly used method based on kinematic simulation and force-closure analysis. <s> BIB009 </s> Finger design automation for industrial robot grippers: A review <s> Physical Experiment <s> This paper introduces ModGrasp, an open-source virtual and physical rapid-prototyping framework that allows for the design, simulation and control of low-cost sensorised modular hands. By combining the rapid-prototyping approach with the modular concept, different manipulator configurations can be modelled. A real-time one-to-one correspondence between virtual and physical prototypes is established. Different control algorithms can be implemented for the models. By using a low-cost sensing approach, functions for torque sensing at the joint level, sensitive collision detection and joint compliant control are possible. A 3-D visualization environment provides the user with an intuitive visual feedback. As a case study, a three-fingered modular manipulator is presented. Related simulations are carried out to validate efficiency and flexibility of the proposed rapid-prototyping framework. <s> BIB010 </s> Finger design automation for industrial robot grippers: A review <s> Physical Experiment <s> This paper presents a three dimensional (3D) grasp synthesis algorithm to achieve distinguished grasps supporting both stability and human-like grasping. The algorithm, which is based on the concepts of a graspable boundary and convex segments, was developed to enable a two-fingered gripper to grasp any unknown object, regardless of its shape, texture, or concavity, given a single 3D image data from depth sensors. The proposed algorithm provides ways to grasp any object using boundary, envelope, and functional grasps. The algorithm is based on identifying graspable segments, analyzing them geometrically, and incorporating the memory of grasping experience. Unlike most grasp synthesis research that focuses on complete 3D contours, our algorithm concentrates only on the graspable boundary and convex segments and thereby achieves stable grasps with less computational complexity. The experimental results show that the proposed algorithm provides distinguished and stable grasps for various objects in various environments, and is suitable for robots to grasp the objects successfully. <s> BIB011 </s> Finger design automation for industrial robot grippers: A review <s> Physical Experiment <s> This paper proposes a task-oriented grasp quality metric based on distribution of task disturbance, which could be used to search for a grasp that covers the most significant part of the disturbance distribution. Rather than using a uniformly distributed task wrench space, this paper models a manipulation task with a non-parametric statistical distribution model built from the disturbance data captured during the task demonstrations. The grasp resulting from maximizing the proposed grasp quality criterion is prone to increasing the coverage of most frequent disturbances. To reduce the computational complexity of the search in a high-dimensional robotic hand configuration space, as well as to avoid the correspondence problem, the candidate grasps are computed from a reduced configuration space that is confined by a set of given thumb placements and thumb directions. The proposed approach has been tested both in simulation and on a real robotic system. In simulation, the approach was validated with a Barrett hand and a Shadow hand in several manipulation tasks. Experiments on a physical robotic platform verified the consistency between the proposed grasp metric and the success rate. <s> BIB012
Unlike the virtual experiments, very few studies have physically verified their approaches. The main reason is that not every researcher has access to the expensive robotic hardware (e.g. robot, gripper, sensor, etc.). Besides, setting up a physical verification system requires experimental skills. For instance, choosing an experiment task that is not reliable makes it difficult to figure out whether an error that occurred was due to a system problem or task problem. In general, physical experiments are crucial to test an approach in reality. Recently, several works BIB007 BIB008 BIB009 demonstrate that classic grasp quality metrics do not have a good prediction capability in reality. BIB007 experimentally validate the grasps considered stable by virtual grasp synthesizers (e.g. GraspIt!). Their results show around 30% of the virtually approved grasps failed in physical experiments. BIB009 experiments. They physically test the widely used grasp quality measure presented by BIB001 and the results show the metric poorly evaluates planned grasps. While physical experiments are essential in the finger design process, most of the studies that present physical verifications in their work have not provided detailed information regarding the process of the experiment BIB002 BIB003 BIB004 BIB005 BIB009 BIB011 . Therefore, it is not possible to repeat their experiments and consequently to compare them. Among the works reviewed in this paper, only  and  give the details of their experiments. Some works BIB006 BIB010 BIB012 verified their method by both virtual and physical experiments. Only BIB010 provide the virtual experiment platform available to the public. In manufacturing industries, functional gripper fingers play a significant role on overall performance of the robot cell. Therefore, the physical experiments are crucial to examine the practicability of the designed fingers in reality. However, the gripper fingers design research area lacks a general experiment method for physically examining the designed fingers.