Raúl Suárez

Bio: Raúl Suárez is an academic researcher from Polytechnic University of Catalonia. The author has contributed to research in topics: GRASP & Motion planning. The author has an hindex of 19, co-authored 126 publications receiving 1666 citations. Previous affiliations of Raúl Suárez include Alcatel-Lucent & Spanish National Research Council.

Grasp quality measures: review and performance

Abstract: The correct grasp of objects is a key aspect for the right fulfillment of a given task. Obtaining a good grasp requires algorithms to automatically determine proper contact points on the object as well as proper hand configurations, especially when dexterous manipulation is desired, and the quantification of a good grasp requires the definition of suitable grasp quality measures. This article reviews the quality measures proposed in the literature to evaluate grasp quality. The quality measures are classified into two groups according to the main aspect they evaluate: location of contact points on the object and hand configuration. The approaches that combine different measures from the two previous groups to obtain a global quality measure are also reviewed, as well as some measures related to human hand studies and grasp performance. Several examples are presented to illustrate and compare the performance of the reviewed measures.

Computation of Independent Contact Regions for Grasping 3-D Objects

Abstract: 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.

Synthesizing grasp configurations with specified contact regions

Abstract: In this paper we present a new method to solve the configuration problem on robotic hands: determining how a hand should be configured so as to grasp a given object in a specific way, characterized by a number of hand-object contacts to be satisfied. In contrast to previous algorithms given for the same purpose, the method presented here allows such contacts to be specified between free-form regions on the hand and object surfaces, and always returns a solution whenever one exists. The method is based on formulating the problem as a system of polynomial equations of special form, and then exploiting this form to isolate the solutions, using a numerical technique based on linear relaxations. The approach is general, in the sense that it can be applied to any grasping mechanism involving lower-pair joints, and it can accommodate as many hand-object contacts as required. Experiments are included that illustrate the performance of the method in the particular case of the Schunk Anthropomorphic hand.

Finding locally optimum force-closure grasps

Abstract: This paper presents an iterative procedure to find locally optimum force-closure grasps on 3D objects, with or without friction and with any number of fingers. The object surface is discretized in a cloud of points, so the approach is applicable to objects with any arbitrary shape. The approach finds an initial force-closure grasp that is then iteratively improved through an oriented search procedure. The grasp quality is measured considering the largest perturbation wrench that the grasp can resist with independence of the direction of perturbation. The efficiency of the algorithm is illustrated through numerical examples.

Autonomous motion planning of a hand-arm robotic system based on captured human-like hand postures

Abstract: The paper deals with the problem of motion planning of anthropomorphic mechanical hands avoiding collisions and trying to mimic real human hand postures. The approach uses the concept of "principal motion directions" to reduce the dimension of the search space in order to obtain results with a compromise between motion optimality and planning complexity (time). Basically, the work includes the following phases: capturing the human hand workspace using a sensorized glove and mapping it to the mechanical hand workspace, reducing the space dimension by looking for the most relevant principal motion directions, and planning the hand movements using a probabilistic roadmap planner. The approach has been implemented for a four finger anthropomorphic mechanical hand (17 joints with 13 independent degrees of freedom) assembled on an industrial robot (6 independent degrees of freedom), and experimental examples are included to illustrate its validity.

Data-Driven Grasp Synthesis—A Survey

Abstract: We review the work on data-driven grasp synthesis and the methodologies for sampling and ranking candidate grasps. We divide the approaches into three groups based on whether they synthesize grasps for known, familiar, or unknown objects. This structure allows us to identify common object representations and perceptual processes that facilitate the employed data-driven grasp synthesis technique. In the case of known objects, we concentrate on the approaches that are based on object recognition and pose estimation. In the case of familiar objects, the techniques use some form of a similarity matching to a set of previously encountered objects. Finally, for the approaches dealing with unknown objects, the core part is the extraction of specific features that are indicative of good grasps. Our survey provides an overview of the different methodologies and discusses open problems in the area of robot grasping. We also draw a parallel to the classical approaches that rely on analytic formulations.