Precision Grasp Planning for Multi-Finger Hand to Grasp Unknown Objects

Yan, Wenyu; Deng, Zhen; Chen, Jinbao; Nie, Hong; Zhang, Jianwei

doi:10.1017/s0263574719000031

Cited by 12 publications

(9 citation statements)

References 24 publications

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“…When grasping in the upper direction of the object, that is, when horizontal grasping, the grasp point on the z-axis reference was formed at the fingertips, and when robot hand depth was not large enough, the fingertips could not reach the grasping points needed for horizontal grasping. 19 Since gravity is the main factor causing grasp instability, it is better to select for a horizontal grasp than a vertical grasp, if possible, and the horizontal grasp condition was represented as follows:…”

Section: Centre Of Mass C Mmentioning

confidence: 99%

Stable Robotic Grasping of Multiple Objects using Deep Neural Networks

Kim¹,

Li²,

Lee³

2020

Robotica

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SUMMARY Optimal grasping points for a robotic gripper were derived, based on object and hand geometry, using deep neural networks (DNNs). The optimal grasping cost functions were derived using probability density functions for each local cost function of the normal distribution. Using the DNN, the optimum height and width were set for the robot hand to grasp objects, whose geometric and mass centre points were also considered in obtaining the optimum grasping positions for the robot fingers and the object. The proposed algorithm was tested on 10 differently shaped objects and showed improved grip performance compared to conventional methods.

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Section: Centre Of Mass C Mmentioning

confidence: 99%

Stable Robotic Grasping of Multiple Objects using Deep Neural Networks

Kim¹,

Li²,

Lee³

2020

Robotica

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“…In general, the end-effector is the main interface between the object to be manipulated and the user, as pointed out in several applications for the mechanical harvesting of horticultural products, such as reported for example in [8,9]. The literature reports multiple attempts of robotic hands for grasping objects [10][11][12][13][14], with some specific end-effectors designed for robotic harvesting such as reported in [15,16]. The main limitations of the existing solutions are that they are mostly designed for singleproduct applications [17,18], like tomatoes [19], strawberries [20] or cucumbers [21] while they result in being unsuitable for the mechanical harvesting of saffron, in particular, for the high fragility of saffron flowers where excessive applied forces will be strongly detrimental to the final quality of the spice.…”

Section: Introductionmentioning

confidence: 99%

Designing a Low-Cost Mechatronic Device for Semi-Automatic Saffron Harvesting

Denarda

Bertetto²,

Carbone

2021

Machines

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This paper addresses the design of a novel mechatronic device for saffron harvesting. The main proposed challenge consists of proposing a new paradigm for semi-automatic harvesting of saffron flowers. The proposed novel solution is designed for being easily portable with user-friendly and cost-oriented features and with a fully electric battery-powered actuation. A preliminary concept design has been proposed as based on a specific novel cam mechanism in combination with an elastic spring for fulfilling the detachment of the flowers from their stems. Numerical calculations and simulations have been carried out to complete the full design of a proof-of-concept prototype. Preliminary experimental tests have been carried out to demonstrate the engineering feasibility and effectiveness of the proposed design solutions, whose concept has been submitted for patenting.

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“…Recently, the deep learning-based methods are becoming increasingly popular for detecting graspable regions [9,10,11,12,13]. Most of the existing methods for vision-based grasping can be broadly classified into two categories: one that relies on the availability of accurate geometric information about the object (or a CAD model) [14,15,16] making them impractical in several real-world use cases, and the other that computes grasping affordances directly from a RGBD point cloud by harnessing local geometric features without knowing the object identity or its accurate 3D geometry [6,17,18,19].…”

Section: Introductionmentioning

confidence: 99%

A novel method for finding grasping handles in a clutter using RGBD Gaussian mixture models

2021

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The paper proposes a novel method to detect graspable handles for picking objects from a confined and cluttered space, such as the bins of a rack in a retail warehouse. The proposed method combines color and depth curvature information to create a Gaussian mixture model that can segment the target object from its background and imposes the geometrical constraints of a two-finger gripper to localize the graspable regions. This helps in overcoming the limitations of a poorly trained deep network object detector and provides a simple and efficient method for grasp pose detection that does not require a priori knowledge about object geometry and can be implemented online with near real-time performance. The efficacy of the proposed approach is demonstrated through simulation as well as real-world experiment.

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Precision Grasp Planning for Multi-Finger Hand to Grasp Unknown Objects

Cited by 12 publications

References 24 publications

Stable Robotic Grasping of Multiple Objects using Deep Neural Networks

Stable Robotic Grasping of Multiple Objects using Deep Neural Networks

Designing a Low-Cost Mechatronic Device for Semi-Automatic Saffron Harvesting

A novel method for finding grasping handles in a clutter using RGBD Gaussian mixture models

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