Optimization Model for Planning Precision Grasps with Multi-Fingered Hands

Precision grasps with multi-fingered hands are important for precise placement and in-hand manipulation tasks. Searching precision grasps on the object represented by point cloud, is challenging due to the complex object shape, high-dimensionality, collision and undesired properties of the sensing and positioning. This paper proposes an optimization model to search for precision grasps with multi-fingered hands. The model takes noisy point cloud of the object as input and optimizes the grasp quality by iteratively searching for the palm pose and finger joints positions. The collision between the hand and the object is approximated and penalized by a series of least-squares. The collision approximation is able to handle the point cloud representation of the objects with complex shapes. The proposed optimization model is able to locate collision-free optimal precision grasps efficiently. The average computation time is 0.50 sec/grasp. The searching is robust to the incompleteness and noise of the point cloud. The effectiveness of the algorithm is demonstrated by experiments.Comment: Submitted to IROS2019, experiment on BarrettHand, 8 page

Stable Object Grasping With Dextrous Hand In Three-Dimension

This paper considers a grasp planning scheme for dextrous hands. The grasp is assumed to be a precise one, which means that only the fingertips of the hand are in contact. The most important algorithm of the grasp planner is the placement of contact points in the presence of friction. Based on a heuristic search, a number of grasp configurations are generated. A proposed method for evaluation of the configurations and determination whether a grasp is a force closure, is introduced. These algorithms are used in the experimental control system of an industrial robot, which the dextrous hand is attached to. A two-level robot programming language, which was written for the robot-hand system, is briefly introduced

Determining force-closure grasps reachable by a given hand

The paper presents an approach to find contact points on an object surface that are reachable by a given hand and such that the resulting grasp satisfies the force-closure condition. This is a very common problem that still requires a practical solution. The proposed method is based on the computation of a set of independent contact regions on the object boundary such that a finger contact on each region produces a force-closure grasp, and then this set of regions is iteratively recomputed while looking for a set of contact points that are reachable by a given hand. The search is done guided by a cost function that indicates the proximity of the hand fingertips to a candidate set of grasping contact points. The approach has been implemented for the Schunk Anthropomorphic Hand and planar objects,and application examples are included to illustrate its performance.Postprint (published version

Searching a valid hand configuration to perform a given grasp

Peer ReviewedPostprint (published version

Data-Driven Grasp Synthesis - A Survey

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.Comment: 20 pages, 30 Figures, submitted to IEEE Transactions on Robotic

Manipulation of unknown objects to improve the grasp quality using tactile information

This work presents a novel and simple approach in the area of manipulation of unknown objects considering both geometric and mechanical constraints of the robotic hand. Starting with an initial blind grasp, our method improves the grasp quality through manipulation considering the three common goals of the manipulation process: improving the hand configuration, the grasp quality and the object positioning, and, at the same time, prevents the object from falling. Tactile feedback is used to obtain local information of the contacts between the fingertips and the object, and no additional exteroceptive feedback sources are considered in the approach. The main novelty of this work lies in the fact that the grasp optimization is performed on-line as a reactive procedure using the tactile and kinematic information obtained during the manipulation. Experimental results are shown to illustrate the efficiency of the approachPeer ReviewedPostprint (published version