Independent contact regions for discretized 3D objects with frictionless contacts

This paper deals with the problem of determining independent contact regions on a 3D object boundary such that a seven finger frictionless grasp with a contact point in each region assures a force-closure grasp on the object, independently of the exact position of the contact points. These regions provide robustness in front of finger positioning errors in grasp and fixturing applications. The object’s structure is discretized in a cloud of points, so the procedure is applicable to objects of any arbitrary shape. The procedure finds an initial form-closure grasp that is iteratively improved through an oriented search procedure: once a locally optimum grasp has been reached, the independent contact regions are computed. The procedure has been implemented, and application examples are included in the paper

Examples of 3D grasp quality computations

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

Computation of independent contact regions for grasping 3-D objects

Precision grasp synthesis has received a lot of attention in past few last years. However, real mechanical hands can hardly assure that the fingers will precisely touch the object at the computed contact points. The concept of independent contact regions (ICRs) was introduced to provide robustness to finger positioning errors during an object grasping: A finger contact anywhere inside each of these regions assures a force-closure grasp, despite the exact contact position. This paper presents an efficient algorithm to compute ICRs with any number of frictionless or frictional contacts on the surface of any 3-D object. The proposed approach generates the independent regions by growing them around the contact points of a given starting grasp. A two-phase approach is provided to find a locally optimal force-closure grasp that serves as the starting grasp, considering as grasp quality measure the largest perturbation wrench that the grasp can resist, independently of the perturbation direction. The proposed method can also be applied to compute ICRs when several contacts are fixed beforehand. The approach has been implemented, and application examples are included to illustrate its performance.Peer Reviewe

Multifingered grasping for robotic manipulation

Robotic hand increases the adaptability of grasping and manipulating objects with its system.But this added adaptability of grasping convolute the process of grasping the object. The analysis of the grasp is very much complicated and large number of configuration for grasping is to be investigated. Handling of objects with irregular shapes and that of flexible/soft objects by ordinary robot grippers is difficult. It is required that various objects with different shapes or sizes could be grasped and manipulated by one robot hand mechanism for the sake of factory automation and labour saving. Dexterous grippers will be the appropriate solution to such problems. Corresponding to such needs, the present work is towards the design and development of an articulated mechanical hand with five fingers and twenty five degrees-of-freedom having an improved grasp capability. In the work, the distance between the Thumb and Finger and the workspace generated by the hand is calculated so as to know about the size and shape of the object that could be grasped.Further the Force applied by the Fingers and there point of application is also being calculated so as to have a stable force closure grasp. The method introduced in present study reduces the complexity and computational burden of grasp synthesis by examining grasps at the finger level. A detailed study on the force closure grasping capability and quality has been carried out. The workspace of the five fingered hand has been used as the maximum spatial envelope. The problem has been considered with positive grips constructed as non-negative linear combinations of primitive and pure wrenches. The attention has been restricted to systems of wrenches generated by the hand fingers assuming Coulomb friction. In order to validate the algorithm vis-a-vis the designed five fingered dexterous hand, example problems have been solved with multiple sets of contact points on various shaped objects.Since the designed hand is capable of enveloping and grasping an object mechanically, it can be used conveniently and widely in manufacturing automation and for medical rehabilitation purpose. This work presents the kinematic design and the grasping analysis of such a hand

Adaptive fuzzy Gaussian mixture models for shape approximation in Robot Grasping

Robotic grasping has always been a challenging task for both service and industrial robots. The ability of grasp planning for novel objects is necessary for a robot to autonomously perform grasps under unknown environments.In this work, we consider the task of grasp planning for a parallel gripper to grasp a novel object, given an RGB image and its corresponding depth image taken from a single view. In this paper, we show that this problem can be simplified by modeling a novel object as a set of simple shape primitives, such as ellipses. We adopt fuzzy Gaussian mixture models (GMMs) for novel objects’ shape approximation. With the obtained GMM, we decompose the object into several ellipses, while each ellipse is corresponding to a grasping rectangle. After comparing the grasp quality among these rectangles, we will obtain the most proper part for a gripper to grasp. Extensive experiments on a real robotic platform demonstrate that our algorithm assists the robot to grasp a variety of novel objects with good grasp quality and computational efficiency