121 research outputs found

    Independent contact regions for discretized 3D objects with frictionless contacts

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    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

    Adaptive fuzzy Gaussian mixture models for shape approximation in Robot Grasping

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    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

    Autonomous Robotic Grasping in Unstructured Environments

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    A crucial problem in robotics is interacting with known or novel objects in unstructured environments. While the convergence of a multitude of research advances is required to address this problem, our goal is to describe a framework that employs the robot\u27s visual perception to identify and execute an appropriate grasp to pick and place novel objects. Analytical approaches explore for solutions through kinematic and dynamic formulations. On the other hand, data-driven methods retrieve grasps according to their prior knowledge of either the target object, human experience, or through information obtained from acquired data. In this dissertation, we propose a framework based on the supporting principle that potential contacting regions for a stable grasp can be found by searching for (i) sharp discontinuities and (ii) regions of locally maximal principal curvature in the depth map. In addition to suggestions from empirical evidence, we discuss this principle by applying the concept of force-closure and wrench convexes. The key point is that no prior knowledge of objects is utilized in the grasp planning process; however, the obtained results show that the approach is capable to deal successfully with objects of different shapes and sizes. We believe that the proposed work is novel because the description of the visible portion of objects by the aforementioned edges appearing in the depth map facilitates the process of grasp set-point extraction in the same way as image processing methods with the focus on small-size 2D image areas rather than clustering and analyzing huge sets of 3D point-cloud coordinates. In fact, this approach dismisses reconstruction of objects. These features result in low computational costs and make it possible to run the proposed algorithm in real-time. Finally, the performance of the approach is successfully validated by applying it to the scenes with both single and multiple objects, in both simulation and real-world experiment setups

    Grasping and Assembling with Modular Robots

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    A wide variety of problems, from manufacturing to disaster response and space exploration, can benefit from robotic systems that can firmly grasp objects or assemble various structures, particularly in difficult, dangerous environments. In this thesis, we study the two problems, robotic grasping and assembly, with a modular robotic approach that can facilitate the problems with versatility and robustness. First, this thesis develops a theoretical framework for grasping objects with customized effectors that have curved contact surfaces, with applications to modular robots. We present a collection of grasps and cages that can effectively restrain the mobility of a wide range of objects including polyhedra. Each of the grasps or cages is formed by at most three effectors. A stable grasp is obtained by simple motion planning and control. Based on the theory, we create a robotic system comprised of a modular manipulator equipped with customized end-effectors and a software suite for planning and control of the manipulator. Second, this thesis presents efficient assembly planning algorithms for constructing planar target structures collectively with a collection of homogeneous mobile modular robots. The algorithms are provably correct and address arbitrary target structures that may include internal holes. The resultant assembly plan supports parallel assembly and guarantees easy accessibility in the sense that a robot does not have to pass through a narrow gap while approaching its target position. Finally, we extend the algorithms to address various symmetric patterns formed by a collection of congruent rectangles on the plane. The basic ideas in this thesis have broad applications to manufacturing (restraint), humanitarian missions (forming airfields on the high seas), and service robotics (grasping and manipulation)

    Implementation and testing of point cloud based grasping algorithms for objetct picking

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    Treball de Final de Màster Universitari Erasmus Mundus en Robòtica Avançada. Codi: SJD024. Curs acadèmic 2016-2017The purpose of this study is to investigate the most effective methodologies for the grasping of items in an environment where success, robustness and time of the algorithmic computation and its implementation are a key constraint. The study originates from the Amazon Robotics Challenge 2017 (ARC’17) which addresses the problem of automating the picking process in online shopping warehouses. In a real warehouse environment the robot has to deal with restricted visibility and accessibility. The proposed solution to grasping was to retrieve a final position and orientation of the end effector given only sensory information without mesh reconstruction. Two grippers were used: a two finger gripper with a narrow opening width and a vacuum gripper. Antipodal Grasp Identification and Learning (AGILE) and Height Accumulated Features (HAF) methods were chosen for implementation on a two finger gripper due to their ease of applicability, same type of input, and reportedly high success rate. One major contribution of this work was the creation of the Centroid Normals Approach (CNA) method for the vacuum gripper that chooses the most central point cloud grasp location on the flattest part of the object. Since it does not include calculation of orientation, its computation time is faster than the other approaches. It was concluded that CNA should be used on as many objects as possible with both the vacuum gripper and the two finger gripper. A final scheme has been devised to pick up the maximum number of items by combining algorithms on the two different grippers, given the hardware restrictions, to cater to different objects in the challenge

    From Deployments Of Elder Care Service Robots To The Design Of Affordable Low-Complexity End-Effectors And Novel Manipulation Techniques

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    This thesis proposes an investigation on both behavioral and technical aspects of human-robot interaction (HRI) in elder care settings, in view of an affordable platform capable of executing desired tasks. The behavioral investigation combines a qualitative study with focus groups and surveys from not only the elders’ standpoint, but also from the standpoint of healthcare professionals to investigate suitable tasks to be accomplished by a service robot in such environments. Through multiple deployments of various robot embodiments at actual elder care facilities (such as at a low-income Supportive Apartment Living, SAL, and Program of All-Inclusive Care, PACE Centers) and interaction with older adults, design guidelines are developed to improve on both interaction and usability aspects. This need assessment informed the technical investigation of this work, where we initially propose picking and placing objects using end-effectors without internal mobility (or zero degrees-of-freedom, DOF), considering both quasi-static (tipping and regrasping as in-hand manipulation) and dynamic approaches. Maximizing grasping versatility by allowing robots to grasp multiple objects sequentially using a single end-effector and actuator is also proposed. These novel manipulation techniques and end-effector designs focus on minimizing robot hardware usage and cost, while still performing complex tasks and complying with safety constraints imposed by the elder care facilities
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