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:  Heuristic Methods 
1. Abstract of Cited Paper (BIB001):  Previous grasp quality research is mainly theoretical, and has assumed that contact types and positions are given, in order to preserve the generality of the proposed quality measures. The example results provided by these works either ignore hand geometry and kinematics entirely or involve only the simplest of grippers. We present a unique grasp analysis system that, when given a 3D object, hand, and pose for the hand, can accurately determine the types of contacts that will occur between the links of the hand and the object, and compute two measures of quality for the grasp. Using models of two articulated robotic hands, we analyze several grasps of a polyhedral model of a telephone handset, and we use a novel technique to visualize the 6D space used in these computations. In addition, we demonstrate the possibility of using this system for synthesizing high quality grasps by performing a search over a subset of possible hand configurations. 
2. Abstract of Cited Paper (BIB002):  Many methods for generating and analyzing grasps have been developed in the recent years. They gave insight and comprehension of grasping with robot hands but many of them are rather complicated to implement and of high computational complexity. In this paper we study if the basic quality criterion for grasps, the force-closure property, is in principle easy or difficult to reach. We show that it is not necessary to generate optimal grasps, due to a certain quality measure, for real robot grasping tasks where an average quality grasp is acceptable. We present statistical data that confirm our opinion that a randomized grasp generation algorithm is fast and suitable for the planning of robot grasping tasks. 
3. Abstract of Cited Paper (BIB003):  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.

Literature Review:
Heuristic methods start by generating a large number of grasp candidates randomly BIB003 based on a predefined procedure BIB001 or by defining a set of rules that can be tested on the workpiece model BIB002 BIB004 . Then unfeasible grasp candidates that do not fulfill the force-closure condition are filtered out.  present an algorithm to compute the positions for contact points to form a force-closure grasp from an initial random grasp. The algorithm starts with selecting randomly a grasp on a given facet of the polyhedral workpiece. If the origin of the wrench space lies outside the initial wrenches convex hull, the algorithm moves each contact position by using the linear parametrization of a point on an object face. The method moves the contact point location so that the convex hull moves toward the origin until the force-closure property is fulfilled. Pros: These approaches can be utilized for unlimited number of contacts low computational effort in compare with unlimited contact methods for polyhedral objects. Cons: Since these methods do not take all the possible grasp sets into account in their searching procedures, they are limited to determining the local optima.