533 research outputs found

    Mobility of bodies in contact. I. A 2nd-order mobility index formultiple-finger grasps

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    Using a configuration-space approach, the paper develops a 2nd-order mobility theory for rigid bodies in contact. A major component of this theory is a coordinate invariant 2nd-order mobility index for a body, B, in frictionless contact with finger bodies A1,...A k. The index is an integer that captures the inherent mobility of B in an equilibrium grasp due to second order, or surface curvature, effects. It differentiates between grasps which are deemed equivalent by classical 1st-order theories, but are physically different. We further show that 2nd-order effects can be used to lower the effective mobility of a grasped object, and discuss implications of this result for achieving new lower bounds on the number of contacting finger bodies needed to immobilize an object. Physical interpretation and stability analysis of 2nd-order effects are taken up in the companion pape

    Prioritized independent contact regions for form closure grasps

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    Proceedings of: 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'11), September 25-30, 2011, San Francisco, USAThe concept of independent contact regions on a target object’s surface, in order to compensate for shortcomings in the positioning accuracy of robotic grasping devices, is well known. However, the numbers and distributions of contact points forming such regions is not unique and depends on the underlying computational method. In this work we present a computation scheme allowing to prioritize contact points for inclusion in the independent regions. This enables a user to affect their shape in order to meet the demands of the targeted application. The introduced method utilizes frictionless contact constraints and is able to efficiently approximate the space of disturbances resistible by all grasps comprising contacts within the independent regions.European Community's Seventh Framework ProgramThis research has been partially supported by the HANDLE project, funded by the European Community’s Seventh Framework Programme (FP7/2007-2013) under grant agreement ICT 231640

    Grasp Planning for a Humanoid Hand

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    Universal Robotic Gripper based on the Jamming of Granular Material

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    Gripping and holding of objects are key tasks for robotic manipulators. The development of universal grippers able to pick up unfamiliar objects of widely varying shape and surface properties remains, however, challenging. Most current designs are based on the multi-fingered hand, but this approach introduces hardware and software complexities. These include large numbers of controllable joints, the need for force sensing if objects are to be handled securely without crushing them, and the computational overhead to decide how much stress each finger should apply and where. Here we demonstrate a completely different approach to a universal gripper. Individual fingers are replaced by a single mass of granular material that, when pressed onto a target object, flows around it and conforms to its shape. Upon application of a vacuum the granular material contracts and hardens quickly to pinch and hold the object without requiring sensory feedback. We find that volume changes of less than 0.5% suffice to grip objects reliably and hold them with forces exceeding many times their weight. We show that the operating principle is the ability of granular materials to transition between an unjammed, deformable state and a jammed state with solid-like rigidity. We delineate three separate mechanisms, friction, suction and interlocking, that contribute to the gripping force. Using a simple model we relate each of them to the mechanical strength of the jammed state. This opens up new possibilities for the design of simple, yet highly adaptive systems that excel at fast gripping of complex objects.Comment: 10 pages, 7 figure

    Grasping Points Determination Using Visual Features

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    This paper discusses some issues for generating point of contact using visual features. To address these issues, the paper is divided into two sections: visual features extraction and grasp planning. In order to provide a suitable description of object contour, a method for grouping visual features is proposed. A very important aspect of this method is the wa

    Ground Robotic Hand Applications for the Space Program study (GRASP)

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    This document reports on a NASA-STDP effort to address research interests of the NASA Kennedy Space Center (KSC) through a study entitled, Ground Robotic-Hand Applications for the Space Program (GRASP). The primary objective of the GRASP study was to identify beneficial applications of specialized end-effectors and robotic hand devices for automating any ground operations which are performed at the Kennedy Space Center. Thus, operations for expendable vehicles, the Space Shuttle and its components, and all payloads were included in the study. Typical benefits of automating operations, or augmenting human operators performing physical tasks, include: reduced costs; enhanced safety and reliability; and reduced processing turnaround time

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

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    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

    Learning-based robotic manipulation for dynamic object handling : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Mechatronic Engineering at the School of Food and Advanced Technology, Massey University, Turitea Campus, Palmerston North, New Zealand

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    Figures are re-used in this thesis with permission of their respective publishers or under a Creative Commons licence.Recent trends have shown that the lifecycles and production volumes of modern products are shortening. Consequently, many manufacturers subject to frequent change prefer flexible and reconfigurable production systems. Such schemes are often achieved by means of manual assembly, as conventional automated systems are perceived as lacking flexibility. Production lines that incorporate human workers are particularly common within consumer electronics and small appliances. Artificial intelligence (AI) is a possible avenue to achieve smart robotic automation in this context. In this research it is argued that a robust, autonomous object handling process plays a crucial role in future manufacturing systems that incorporate robotics—key to further closing the gap between manual and fully automated production. Novel object grasping is a difficult task, confounded by many factors including object geometry, weight distribution, friction coefficients and deformation characteristics. Sensing and actuation accuracy can also significantly impact manipulation quality. Another challenge is understanding the relationship between these factors, a specific grasping strategy, the robotic arm and the employed end-effector. Manipulation has been a central research topic within robotics for many years. Some works focus on design, i.e. specifying a gripper-object interface such that the effects of imprecise gripper placement and other confounding control-related factors are mitigated. Many universal robotic gripper designs have been considered, including 3-fingered gripper designs, anthropomorphic grippers, granular jamming end-effectors and underactuated mechanisms. While such approaches have maintained some interest, contemporary works predominantly utilise machine learning in conjunction with imaging technologies and generic force-closure end-effectors. Neural networks that utilise supervised and unsupervised learning schemes with an RGB or RGB-D input make up the bulk of publications within this field. Though many solutions have been studied, automatically generating a robust grasp configuration for objects not known a priori, remains an open-ended problem. An element of this issue relates to a lack of objective performance metrics to quantify the effectiveness of a solution—which has traditionally driven the direction of community focus by highlighting gaps in the state-of-the-art. This research employs monocular vision and deep learning to generate—and select from—a set of hypothesis grasps. A significant portion of this research relates to the process by which a final grasp is selected. Grasp synthesis is achieved by sampling the workspace using convolutional neural networks trained to recognise prospective grasp areas. Each potential pose is evaluated by the proposed method in conjunction with other input modalities—such as load-cells and an alternate perspective. To overcome human bias and build upon traditional metrics, scores are established to objectively quantify the quality of an executed grasp trial. Learning frameworks that aim to maximise for these scores are employed in the selection process to improve performance. The proposed methodology and associated metrics are empirically evaluated. A physical prototype system was constructed, employing a Dobot Magician robotic manipulator, vision enclosure, imaging system, conveyor, sensing unit and control system. Over 4,000 trials were conducted utilising 100 objects. Experimentation showed that robotic manipulation quality could be improved by 10.3% when selecting to optimise for the proposed metrics—quantified by a metric related to translational error. Trials further demonstrated a grasp success rate of 99.3% for known objects and 98.9% for objects for which a priori information is unavailable. For unknown objects, this equated to an improvement of approximately 10% relative to other similar methodologies in literature. A 5.3% reduction in grasp rate was observed when removing the metrics as selection criteria for the prototype system. The system operated at approximately 1 Hz when contemporary hardware was employed. Experimentation demonstrated that selecting a grasp pose based on the proposed metrics improved grasp rates by up to 4.6% for known objects and 2.5% for unknown objects—compared to selecting for grasp rate alone. This project was sponsored by the Richard and Mary Earle Technology Trust, the Ken and Elizabeth Powell Bursary and the Massey University Foundation. Without the financial support provided by these entities, it would not have been possible to construct the physical robotic system used for testing and experimentation. This research adds to the field of robotic manipulation, contributing to topics on grasp-induced error analysis, post-grasp error minimisation, grasp synthesis framework design and general grasp synthesis. Three journal publications and one IEEE Xplore paper have been published as a result of this research
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