Tactile-Based Blind Grasping: A Discrete-Time Object Manipulation Controller for Robotic Hands

Cortez, Wenceslao Shaw; Oetomo, Denny; Manzie, Chris; Choong, Peter F. M.

doi:10.1109/lra.2018.2794612

Cited by 28 publications

(44 citation statements)

References 17 publications

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“…The nominal manipulation control used here is the manipulation control presented from [3] with the internal force control [50] which is re-defined here as:…”

Section: A Simulation Resultsmentioning

confidence: 99%

“…The associated box constraints to define the fingertip workspace are: −π/2 < a fi < π/2, −π < b fi < 0. The joint angle limits for each finger are q maxi = (3π/4, π/3, 3π/4), q mini = (0, −π/3, 0), for i ∈ [1,3]. The control gains used in the simulation are K p = 0.26I 6×6 , K i = 0.1I 6×6 , K d = 0.125I 6×6 , and k f = 1.0.…”

Section: A Simulation Resultsmentioning

confidence: 99%

“…In the following section, the control input u k is proposed to ensure no slip, no over-extension, and no excessive rolling are satisfied. Satisfaction of no over-extension and no excessive rolling constraints is addressed using the zeroing control barrier functions from Section II, while satisfaction of the no slip condition is developed as an extension of [3]. The input constraints are then combined to define the proposed controller to address Problem 1.…”

Section: Proposed Grasp Constraint Satisfying Controlmentioning

confidence: 99%

“…is the conservative friction coefficient to address inter-sampling behavior as discussed in [3]. In practice, the designer should chooseμ for the specified task where a smallμ could be chosen to grasp a larger range of objects, including slippery objects such as ice, however more control effort will be required to do so.…”

Section: Proposed Grasp Constraint Satisfying Controlmentioning

confidence: 99%

“…Furthermore, existing methods of addressing grasping constraints are not robust to model uncertainties and/or unknown disturbances that may act on the system. The literature contains numerous controllers that aim to extend manipulation capabilities outside of a laboratory setting by restricting the required sensors to on-board sensing modalities [3], [27]. A common theme in related work is referred to as "blind grasping" or similarly "tactile-based blind grasping" where the robotic hand only has access to sensors that can be physically integrated into the hand (i.e.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Control Barrier Functions for Mechanical Systems: Theory and Application to Robotic Grasping

Cortez

Oetomo

Manzie

et al. 2021

IEEE Trans. Contr. Syst. Technol.

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111

101

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Control barrier functions have been demonstrated to be a useful method of ensuring constraint satisfaction for a wide class of controllers, however existing results are mostly restricted to continuous time systems of relative degree one. Mechanical systems, including robots, are typically second-order systems in which the control occurs at the force/torque level. These systems have velocity and position constraints (i.e. relative degree two) that are vital for safety and/or task execution. Additionally, mechanical systems are typically controlled digitally as sampleddata systems. The contribution of this work is two-fold. First, is the development of novel, robust control barrier functions that ensure constraint satisfaction for relative degree two, sampleddata systems in the presence of model uncertainty. Second, is the application of the proposed method to the challenging problem of robotic grasping in which a robotic hand must ensure an object remains inside the grasp while manipulating it to a desired reference trajectory. A grasp constraint satisfying controller is proposed that can admit existing nominal manipulation controllers from the literature, while simultaneously ensuring no slip, no over-extension (e.g. singular configurations), and no rolling off of the fingertips. Simulation and experimental results validate the proposed control for the robotic hand application.

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“…The nominal manipulation control used here is the manipulation control presented from [3] with the internal force control [50] which is re-defined here as:…”

Section: A Simulation Resultsmentioning

confidence: 99%

Section: A Simulation Resultsmentioning

confidence: 99%

Section: Proposed Grasp Constraint Satisfying Controlmentioning

confidence: 99%

Section: Proposed Grasp Constraint Satisfying Controlmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Control Barrier Functions for Mechanical Systems: Theory and Application to Robotic Grasping

Cortez

Oetomo

Manzie

et al. 2021

IEEE Trans. Contr. Syst. Technol.

Self Cite

111

101

View full text Add to dashboard Cite

show abstract

Enhancement ofreal‐timegrasp detection by cascaded deep convolutional neural networks

Weng

Sun

et al. 2020

Concurrency and Computation

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Robot grasping technology is a hot spot in robotics research. In relatively fixed industrialized scenarios, using robots to perform grabbing tasks is efficient and lasts a long time. However, in an unstructured environment, the items are diverse, the placement posture is random, and multiple objects are stacked and occluded each other, which makes it difficult for the robot to recognize the target when it is grasped and the grasp method is complicated. Therefore, we propose an accurate, real‐time robot grasp detection method based on convolutional neural networks. A cascaded two‐stage convolutional neural network model with course to fine position and attitude was established. The R‐FCN model was used as the extraction of the candidate frame of the picking position for screening and rough angle estimation, and aiming at the insufficient accuracy of the previous methods in pose detection, an Angle‐Net model is proposed to finely estimate the picking angle. Tests on the Cornell dataset and online robot experiment results show that the method can quickly calculate the optimal gripping point and posture for irregular objects with arbitrary poses and different shapes. The accuracy and real‐time performance of the detection have been improved compared to previous methods.

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A Force Control Architecture for Grasping with a Robotic Finger

Padron

Cruz-Miguel

Jáuregui-Correa

et al. 2020

Mechanisms and Machine Science

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Tactile-Based Blind Grasping: A Discrete-Time Object Manipulation Controller for Robotic Hands

Cited by 28 publications

References 17 publications

Control Barrier Functions for Mechanical Systems: Theory and Application to Robotic Grasping

Control Barrier Functions for Mechanical Systems: Theory and Application to Robotic Grasping

Enhancement ofreal‐timegrasp detection by cascaded deep convolutional neural networks

A Force Control Architecture for Grasping with a Robotic Finger

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