You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Finger design automation for industrial robot grippers: A review 
Chapter Title:  Uncertainty-based Metrics: 
1. Abstract of Cited Paper (BIB001):  0 ABSTRACT In this paper we propose a model for grasping with an industrial parallel-jaw gripper. Two points on the part are candidate ‘grip points’ if they are accessible and are resistant to frictional slip and torque. We formalize these conditions geometrically on a polygonal slice through the part's center of mass. We describe an efficient algorithm for computing and ranking all grip points for an n-sided polygonal slice. The algorithm is part of a design and simulation system that can rapidly provide feedback to designers; thus it must run quickly and reliably. We have implemented the algorithm in a Java applet with a graphical user interface that allows Internet users to define a part; the applet then computes, ranks, and displays the set of computed grip points. To try the applet, please visit: 
2. Abstract of Cited Paper (BIB002):  In this paper, we survey the field of robotic grasping and the work that has been done in this area over the last two decades, with a slight bias toward the development of the theoretical framework and analytical results in this area. 
3. Abstract of Cited Paper (BIB003):  An algorithm for automatically generating a common jaw design and planning grasps for a given set of polyhedral objects is presented. The algorithm is suitable for a parallel-jaw gripper equipped with three cylindrical fingers. The common jaw design eliminates the need for custom made grippers and tool changing. The proposed jaw configuration and planning approach reduces the search associated with locating the finger contacts from six degrees-of-freedom to one degree-of-freedom. Closed-form algorithms for checking force closure and for predicting jamming are developed. Three quality metrics are introduced to improve the quality of the planned grasps. The first is a measure of the sensitivity of the grasp to errors between the actual and planned finger locations. The second is a measure of the efficiency of the grasp in terms of the contact forces. The third is a measure of the dependence of force closure on friction. These quality metrics are not restricted to cylindrical fingers and can be applied to n finger grasps. Running on a standard PC, the algorithm generated a solution in less than five minutes for a set of five objects with a total of 456 triangular facets. © 2003 Wiley Periodicals, Inc. 
4. Abstract of Cited Paper (BIB004):  Automatic grasp planning for robotic hands is a difficult problem because of the huge number of possible hand configurations. However, humans simplify the problem by choosing an appropriate prehensile posture appropriate for the object and task to be performed. By modeling an object as a set of shape primitives, such as spheres, cylinders, cones and boxes, we can use a set of rules to generate a set of grasp starting positions and pregrasp shapes that can then be tested on the object model. Each grasp is tested and evaluated within our grasping simulator "GraspIt!", and the best grasps are presented to the user. The simulator can also plan grasps in a complex environment involving obstacles and the reachability constraints of a robot arm. 
5. Abstract of Cited Paper (BIB005):  Simulation is essential for different robotic research fields such as mobile robotics, motion planning and grasp planning. For grasping in particular, there are no software simulation packages, which provide a holistic environment that can deal with the variety of aspects associated with this problem. These aspects include development and testing of new algorithms, modeling of the environments and robots, including the modeling of actuators, sensors and contacts. In this paper, we present a new simulation toolkit for grasping and dexterous manipulation called OpenGRASP addressing those aspects in addition to extensibility, interoperability and public availability. OpenGRASP is based on a modular architecture, that supports the creation and addition of new functionality and the integration of existing and widely-used technologies and standards. In addition, a designated editor has been created for the generation and migration of such models. We demonstrate the current state of OpenGRASP's development and its application in a grasp evaluation environment. 
6. Abstract of Cited Paper (BIB006):  SynGrasp is a MATLAB toolbox developed for the analysis of grasping, suitable both for robotic and human hands. It includes functions for the definition of hand kinematic structure and of the contact points with a grasped object. The coupling between joints induced by an underactuated control can be modeled. The hand modeling allows to define compliance at the contact, joint and actuator levels. The provided analysis functions can be used to investigate the main grasp properties: controllable forces and object displacement, manipulability analysis, grasp quality measures. Functions for the graphical representation of the hand, the object and the main analysis results are provided. 
7. Abstract of Cited Paper (BIB007):  This paper presents an analysis of object grasping with a multifingered robot hand. Uncertainties on the contact orientation and location are investigated and taken into account to find a new grasp quality measure. This new metric allows to evaluate the controllability of the system and find the Reachable Wrench Space under Uncertainties, a new set introduced in this paper. It includes all the wrenches a grasp can apply whatever the uncertainty is, and gives information about the robustness of the grasp relative to disturbances. Two algorithms are proposed to estimate this new set. Several study cases are presented and tested in simulation to illustrate the new quality metric.

Literature Review:
None of the methods presented so far have considered unavoidable errors in manipulation. In reality, there are three major positioning errors that cause uncertainties on the contact point location. First, error on the contact surface due to workpiece modeling errors. Second, error on the object location. The last but not the least, error on the gripper robot and gripper kinematic and dynamic model. BIB003 highlight a gap in approaches toward grasp analysis that can handle positioning errors. BIB004 propose a metric that measures the sensitivity of a grasp to the positional error between the grasp locations and the actual finger locations. In their method, a circular area of expected error is defined at each contact point. The area of the intersection of the error circle and the contact surface provide the quality measure. The grasps with small intersection areas are more likely to fail in practice. A recent study by BIB008 proposes a new quality metric that considers the errors on workpiece geometry. They define a new wrench space, called Reachable Wrench Space under Uncertainties (RWSU), containing all the possible wrenches that the grasp can exert regardless of uncertainties. Two algorithms are provided to estimate the upper and lower bounds of the RWSU. They illustrate that the combination of these algorithms can provide a very good approximation of the RWSU. Pros: These metrics are able to predict the quality of robot grasp in reality. Cons: Similar to general metrics, these metrics do not take the task wrenches into consideration. Thus, they cannot accurately predict the grasp quality in reality The metrics that have been proposed to the grasp analysis research field have either limitations on the application area or defined for a specific criteria. As a result, some researchers BIB002 BIB004 have utilized several metrics, both in parallel and series, in their algorithm to ensure the accuracy of the quality measurement of the grasp. Some researchers have developed simulators for grasp synthesis and grasp analysis that are freely available to the public. Most popular open-source simulators are GraspIt!  , OpenGRASP BIB006 and SynGrasp BIB007 . GraspIt! is the result of Millers Ph.D. thesis on developing a grasping simulator for robot hands  . In this free access tool, the user can create, simulate and analyze grasps of a given 3D model of the object and an articulated hand/gripper model. The system allows the user to test the quality of a grasp configuration and compare it with other grasps. Their novel visualization method helps the user to understand the weak points and strong points of a grasp. The system uses the Coulomb friction model and the grasp quality metric presented by BIB001 to measure the quality of grasps. The detailed description of the analyzing method can be found in  and BIB005 . Another existing and publicly available software framework is OpenGRASP which is a result of the European Commission founded project GRASP BIB006 . This simulation toolkit is an improved version of OpenRAVE (OpenRAVE, 2015) with focus on robot grasping and manipulation. The main design principles of OpenGRASP are extensibility, interoperability and public availability (OpenGRASP, 2015) . BIB006 have proven the ability of their own developed tool, OpenGRASP, in grasping known and unknown objects. These open-source simulators help the research community to use them as references for evaluating and comparing their own approaches.