Theoretical model of the grasp with vacuum gripper

Mantriota, Giacomo

doi:10.1016/j.mechmachtheory.2006.03.003

Cited by 56 publications

(22 citation statements)

References 19 publications

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“…Bahr et al [2] augmented this model with the ability to resist moments about the center of suction to determine the amount of vacuum pressure necessary to keep a climbing robot attached to a vertical wall. Mantriota [23] modeled torsional friction due to a contact area between the cup and object similar to the soft finger contact model used in grasping [14]. Our model extends these methods by combining models of torsional friction [23] and contact moments [2] in a compliant model of the ring of contact between the cup and object.…”

Section: A Suction Modelsmentioning

confidence: 99%

“…2) Suction Contact Model: Many suction contact models acknowledge normal forces, vacuum forces, tangential friction, and torsional friction [2], [17], [23], [29] similar to a point contact with friction or soft finger model [24]. However, under this model, a single suction cup cannot resist torques about axes in the contact tangent plane, implying that any torque about such axes would cause the suction cup to drop an object (see the supplementary material for a detailed proof).…”

Section: B Wrench Space Analysismentioning

confidence: 99%

“…vacuum seal has already been formed and that the state (e.g. shape and pose) of the object is perfectly known [2], [17], [23]. In practice a robot may need to form seals on nonplanar surfaces while being robust to external wrenches (e.g.…”

Section: Introductionmentioning

confidence: 99%

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Dex-Net 3.0: Computing Robust Vacuum Suction Grasp Targets in Point Clouds Using a New Analytic Model and Deep Learning

Mahler¹,

Matl²,

Liu³

et al. 2018

2018 IEEE International Conference on Robotics and Automation (ICRA)

261

199

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Vacuum-based end effectors are widely used in industry and are often preferred over parallel-jaw and multifinger grippers due to their ability to lift objects with a single point of contact. Suction grasp planners often target planar surfaces on point clouds near the estimated centroid of an object. In this paper, we propose a compliant suction contact model that computes the quality of the seal between the suction cup and local target surface and a measure of the ability of the suction grasp to resist an external gravity wrench. To characterize grasps, we estimate robustness to perturbations in end-effector and object pose, material properties, and external wrenches. We analyze grasps across 1,500 3D object models to generate Dex-Net 3.0, a dataset of 2.8 million point clouds, suction grasps, and grasp robustness labels. We use Dex-Net 3.0 to train a Grasp Quality Convolutional Neural Network (GQ-CNN) to classify robust suction targets in point clouds containing a single object. We evaluate the resulting system in 350 physical trials on an ABB YuMi fitted with a pneumatic suction gripper. When evaluated on novel objects that we categorize as Basic (prismatic or cylindrical), Typical (more complex geometry), and Adversarial (with few available suction-grasp points) Dex-Net 3.0 achieves success rates of 98%, 82%, and 58% respectively, improving to 81% in the latter case when the training set includes only adversarial objects. Code, datasets, and supplemental material can be found at http://berkeleyautomation.github.io/dex-net.

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Section: A Suction Modelsmentioning

confidence: 99%

Section: B Wrench Space Analysismentioning

confidence: 99%

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Dex-Net 3.0: Computing Robust Vacuum Suction Grasp Targets in Point Clouds Using a New Analytic Model and Deep Learning

Mahler¹,

Matl²,

Liu³

et al. 2018

2018 IEEE International Conference on Robotics and Automation (ICRA)

261

199

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“…Some of the earliest astrictive grippers were used for sheet metal fabrication [41]. A theoretical model of a suction gripper is found in [42]. This model is then expanded to multi-link manipulators in [43].…”

Section: Grippers For Known Environmentsmentioning

confidence: 99%

State of the Art Robotic Grippers and Applications

et al. 2016

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Abstract:In this paper, we present a recent survey on robotic grippers. In many cases, modern grippers outperform their older counterparts which are now stronger, more repeatable, and faster. Technological advancements have also attributed to the development of gripping various objects. This includes soft fabrics, microelectromechanical systems, and synthetic sheets. In addition, newer materials are being used to improve functionality of grippers, which include piezoelectric, shape memory alloys, smart fluids, carbon fiber, and many more. This paper covers the very first robotic gripper to the newest developments in grasping methods. Unlike other survey papers, we focus on the applications of robotic grippers in industrial, medical, for fragile objects and soft fabrics grippers. We report on new advancements on grasping mechanisms and discuss their behavior for different purposes. Finally, we present the future trends of grippers in terms of flexibility and performance and their vital applications in emerging areas of robotic surgery, industrial assembly, space exploration, and micromanipulation. These advancements will provide a future outlook on the new trends in robotic grippers.

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“…The reliability of functioning of such devices significantly depends on the compressed air expenses to preserve the balance of the object of manipulation as to the gripping device while performing the manipulative functions by the industrial robot. In papers [7][8] the formulae for defining the allowable acceleration of the gripping device while its vertical movement under the conditions of uninterrupted transportation of the objects of manipulation have been derived. That is why the issue of energy efficiency of the process of manipulation with the use of optimal orientation while gripping by means of different methods has arisen.…”

Section: Introductionmentioning

confidence: 99%

Energy efficiency analysis of the manipulation process by the industrial objects with the use of Bernoulli gripping devices

Savkiv

Mykhailyshyn

Duchoň

et al. 2017

Journal of Electrical Engineering

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The article deals with the topical issue of reducing energy consumption for transportation of industrial objects. The energy efficiency of the process of objects manipulation with the use of the orientation optimization method while gripping with the help of different methods has been studied. The analysis of the influence of the constituent parts of inertial forces, that affect the object of manipulation, on the necessary force characteristics and energy consumption of Bernoulli gripping device has been proposed. The economic efficiency of the use of the optimal orientation of Bernoulli gripping device while transporting the object of manipulation in comparison to the transportation without re-orientation has been proved.

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Theoretical model of the grasp with vacuum gripper

Cited by 56 publications

References 19 publications

Dex-Net 3.0: Computing Robust Vacuum Suction Grasp Targets in Point Clouds Using a New Analytic Model and Deep Learning

Dex-Net 3.0: Computing Robust Vacuum Suction Grasp Targets in Point Clouds Using a New Analytic Model and Deep Learning

State of the Art Robotic Grippers and Applications

Energy efficiency analysis of the manipulation process by the industrial objects with the use of Bernoulli gripping devices

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