Stability of soft-finger grasp under gravity

Harada, Kensuke; Tsuji, Tokuo; Uto, Soichiro; Yamanobe, Natsuki; Nagata, Kazuyuki; Kitagaki, K.

doi:10.1109/icra.2014.6906958

Cited by 35 publications

(24 citation statements)

References 20 publications

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“…Ciocarlie et al [3] took into account the static friction torque. Harada et al [4] took into account the effect of gravity. These methods do not take into account the change of forces which can be caused by elasticity of fingertips.…”

Section: Related Workmentioning

confidence: 99%

Grasp stability analysis for elastic fingertips by using potential energy

Tsuji

Baba

Tahara

et al. 2014

2014 IEEE/SICE International Symposium on System Integration

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We propose a grasp stability index which takes into account the elastic deformation of fingertips. In this index, an evaluation value of grasp stability is derived as the minimum energy which causes slippage of a fingertip on a grasped object surface. Elastic potential energy of the fingertips and gravitational potential energy of the object are considered in this index. It is ensured that any fingertip does not slip on a grasped object surface if the external energy added to the object is less than the evaluation value. This index has good features as follows. (i) Contact forces corresponding to the grasping state are taken into account. (ii) It is possible to derive a condition of stable grasp including the kinetic energy of a grasped object. The effectiveness of this index is verified through several numerical examples.

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Section: Related Workmentioning

confidence: 99%

Grasp stability analysis for elastic fingertips by using potential energy

Tsuji

Baba

Tahara

et al. 2014

2014 IEEE/SICE International Symposium on System Integration

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“…For a sequence of assembly tasks, we consider determining the grasping configuration. We first disregard the transit path and try to find grasping configurations with maximum grasping stability index [16]. Our grasp stability index proposed in [16] evaluates the contact area and can be applied for the soft-finger contact model.…”

Section: Initial Nodementioning

confidence: 99%

Tool Exchangeable Grasp/Assembly Planner

Harada

Nakayama

Wan

et al. 2018

Intelligent Autonomous Systems 15

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This paper proposes a novel assembly planner for a manipulator which can simultaneously plan assembly sequence, robot motion, grasping configuration, and exchange of grippers. Our assembly planner assumes multiple grippers and can automatically selects a feasible one to assemble a part. For a given AND/OR graph of an assembly task, we consider generating the assembly graph from which assembly motion of a robot can be planned. The edges of the assembly graph are composed of three kinds of paths, i.e., transfer/assembly paths, transit paths and tool exchange paths. In this paper, we first explain the proposed method for planning assembly motion sequence including the function of gripper exchange. Finally, the effectiveness of the proposed method is confirmed through some numerical examples and a physical experiment.

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“…where o p i / o R i denote the position/orientation of the wrist with respect to the object coordinate system, and θ i and I i denote the finger joint angle vector and an index for evaluating the grasp stability such as [18], respectively.…”

Section: Picking Task Executionmentioning

confidence: 99%

Initial experiments on learning-based randomized bin-picking allowing finger contact with neighboring objects

Harada

Wan

Tsuji

et al. 2016

2016 IEEE International Conference on Automation Science and Engineering (CASE)

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This paper proposes a novel method for randomized bin-picking based on learning. When a two-fingered gripper tries to pick an object from the pile, a finger often contacts a neighboring object. Even if a finger contacts a neighboring object, the target object will be successfully picked depending on the configuration of neighboring objects. In our proposed method, we use the visual information on neighboring objects to train the discriminator. Corresponding to a grasping posture of an object, the discriminator predicts whether or not the pick will be successful even if a finger contacts a neighboring object. We examine two learning algorithms, the linear support vector machine (SVM) and the random forest (RF) approaches. By using both methods, we demonstrate that the picking success rate is significantly higher than with conventional methods without learning.

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Stability of soft-finger grasp under gravity

Cited by 35 publications

References 20 publications

Grasp stability analysis for elastic fingertips by using potential energy

Grasp stability analysis for elastic fingertips by using potential energy

Tool Exchangeable Grasp/Assembly Planner

Initial experiments on learning-based randomized bin-picking allowing finger contact with neighboring objects

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