Searching force-closure optimal grasps of articulated 2D objects with n links

This paper proposes a method that finds a locally optimal grasp of an articulated 2D object with n links considering frictionless contacts. The surface of each link of the object is represented by a finite set of points, thus it may have any shape. The proposed approach finds, first, an initial force-closure grasp and from it starts an iterative search of a local optimum grasp. The quality measure considered in this work is the largest perturbation wrench that a grasp can resist with independence of the direction of the perturbation. The approach has been implemented and some illustrative examples are included in the article.Postprint (published version

Grasping bulky objects with two anthropomorphic hands

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksThis paper presents an algorithm to compute precision grasps for bulky objects using two anthropomorphic hands. We use objects modeled as point clouds obtained from a sensor camera or from a CAD model. We then process the point clouds dividing them into two set of slices where we look for sets of triplets of points. Each triplet must accomplish some physical conditions based on the structure of the hands. Then, the triplets of points from each set of slices are evaluated to find a combination that satisfies the force closure condition (FC). Once one valid couple of triplets have been found the inverse kinematics of the system is computed in order to know if the corresponding points are reachable by the hands, if so, motion planning and a collision check are performed to asses if the final grasp configuration of the system is suitable. The paper inclu des some application examples of the proposed approachAccepted versio

A New Approach for Grasp Quality Calculation using Continuous Boundary Formulation of Grasp Wrench Space

In this paper, we aim to use a continuous formulation to efficiently calculate the well-known wrench-based grasp metric proposed by Ferrari and Canny which is the minimum distance from the wrench space origin to the boundary of the grasp wrench space. Considering the L∞ role= presentation style= box-sizing: border-box; margin: 0px; padding: 0px; display: inline-block; line-height: normal; font-size: 16.200000762939453px; word-spacing: normal; word-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border: 0px; position: relative; \u3e metric and the nonlinear friction cone model, the challenge of calculating this metric is to determine the boundary of the grasp wrench space. Instead of relying on convex hull construction, we propose to formulate the boundary of the grasp wrench space as continuous functions. By doing so, the problem of grasp quality calculation can be efficiently solved as typical least-square problems and it can be easily implemented by employing off-the-shelf optimization algorithms. Numerical tests will demonstrate the advantages of the proposed formulation compared to the conventional convex hull-based methods

Integrated Grasp and Motion Planning using Independent Contact Regions

Traditionally, grasp and arm motion planning are considered as separate tasks. This might lead to problems such as limitations in the grasp possibilities, or unnecessary long times in the solution of the planning problem. This thesis presents an integrated approach that only requires the initial con guration of the robotic arm and the pose of the target object to simultaneously plan a good hand pose and arm trajectory to grasp the object. The planner exploits the concept of independent contact regions to look for the best possible grasp. In this document, two di erent methods have been considered to search for good end-e ector poses. One biases a sampling approach towards favorable regions using principal component analysis, and the other one considers the capabilities of the robotic arm to decide the most promising hand poses. The performance of the methods is evaluated using di erent scenarios for the humanoid robot SpaceJustin. In order to validate the paths, some scenarios were replicated in the laboratory and the generated paths were executed on the real robot

Reachable independent contact regions for precision grasps

Independent Contact Regions allow a robust finger placement on the object, despite of potential errors in finger position. They are computed without considering the kinematics of the end-effector, and are usually applied to off-line grasp planners. This paper presents an approach to obtain Reachable Independent Contact Regions by including the hand kinematics in the computational loop. The regions are computed in a short time, which allows real-time applications in virtual grasping. Potential applications of the proposed approach include regrasp planning, and dual-hand manipulation of objects

Precision Grasp Planning for Integrated Arm-Hand Systems

The demographic shift has caused labor shortages across the world, and it seems inevitable to rely on robots more than ever to fill the widening gap in the workforce. The robotic replacement of human workers necessitates the ability of autonomous grasping as the most natural but rather a vital part of almost all activities. Among different types of grasping, fingertip grasping attracts much attention because of its superior performance for dexterous manipulation. This thesis contributes to autonomous fingertip grasping in four areas including hand-eye calibration, grasp quality evaluation, inverse kinematics (IK) solution of robotic arm-hand systems, and simultaneous achievement of grasp planning and IK solution. To initiate autonomous grasping, object perception is the first needed step. Stereo cameras are well-embraced for obtaining an object\u27s 3D model. However, the data acquired through a camera is expressed in the camera frame while robots only accept the commands encoded in the robot frame. This dilemma necessitates the calibration between the robot (hand) and the camera (eye) with the main goal is of estimating the camera\u27s relative pose to the robot end-effector so that the camera-acquired measurements can be converted into the robot frame. We first study the hand-eye calibration problem and achieve accurate results through a point set matching formulation. With the object\u27s 3D measurements expressed in the robot frame, the next step is finding an appropriate grasp configuration (contact points + contact normals) on the object\u27s surface. To this end, we present an efficient grasp quality evaluation method to calculate a popular wrench-based quality metric which measures the minimum distance between the wrench space origin ($\vec{0}_{6\times 1}$) to the boundary of grasp wrench space (GWS). The proposed method mathematically expresses the exact boundary of GWS, which allows to evaluate the quality of the grasp with the speed that is desirable in most robotic applications. Having obtained a suitable grasp configuration, an accurate IK solution of the arm-hand system is required to perform the planned grasp. Conventionally, the IK of the robotic hand and arm are solved sequentially, which often affects the efficiency and accuracy of the IK solutions. To overcome this problem, we kinematically integrate the robotic arm and hand and propose a human-inspired Thumb-First strategy to narrow down the search space of the IK solution. Based on the Thumb-First strategy, we propose two IK solutions. Our first solution follows a hierarchical IK strategy, while our second solution formulates the arm-hand system as a hybrid parallel-serial system to achieve a higher success rate. Using these results, we propose an approach to integrate the process of grasp planning and IK solution by following a special-designed coarse-to-fine strategy to improve the overall efficiency of our approach