A dynamic and uncalibrated method to visually servo-control elastic deformations by fully-constrained robotic grippers

Navarro-Alarcon, David; Liu, Yunhui

doi:10.1109/icra.2014.6907509

Cited by 13 publications

(11 citation statements)

References 21 publications

(27 reference statements)

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“…They also considered curvature-based deformation features. In [99], they developed a controller which is able to make use of all 6 degrees of freedom available to their manipulator, which was not the case with their previous experiments. This larger range of motion allows for more flexibility in the deformation tasks, in terms of both simultaneously controllable features and the number of reachable configurations.…”

Section: D Objectsmentioning

confidence: 97%

Multi-Modal Sensing and Robotic Manipulation of Non-Rigid Objects: A Survey

2018

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This paper aims to provide a comprehensive survey of recent advancements in modelling and autonomous manipulation of non-rigid objects. It first summarizes the recent advances in sensing and modelling of such objects with a focus on describing the methods and technologies used to measure their shape and estimate their material and physical properties. Formal representations considered to predict the deformation resulting from manipulation of non-rigid objects are then investigated. The third part provides a survey of planning and control strategies exploited to operate dexterous robotic systems while performing various tasks on objects made of different non-rigid materials.

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Section: D Objectsmentioning

confidence: 97%

Multi-Modal Sensing and Robotic Manipulation of Non-Rigid Objects: A Survey

2018

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“…In Navarro-Alarcon et al (2014), they proposed a new online Jacobian estimator that uses views from multiple cameras. As these previous approaches only controlled three degrees of freedom of the manipulator’s gripper, namely its position; in Navarro-Alarcon and Liu (2014), they extended their approach to incorporate the control of the gripper’s orientation to have a six-degree-of-freedom controller. Another limitation of these approaches is that the control is performed on a two-dimensional projection based on the cameras’ images, because the control points are described in pixel coordinates.…”

Section: Solid Objectsmentioning

confidence: 99%

Robotic manipulation and sensing of deformable objects in domestic and industrial applications: a survey

Sánchez

Ramón

Bouzgarrou

et al. 2018

The International Journal of Robotics Research

374

228

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We present a survey of recent work on robot manipulation and sensing of deformable objects, a field with relevant applications in diverse industries such as medical (e.g. surgery assistance), food handling, manufacturing, and domestic chores (e.g. folding clothes). We classify the reviewed approaches into four categories based on the type of object they manipulate. Furthermore, within this object classification we divide the approaches based on the particular task they perform on the deformable object. Finally, we conclude this survey with a discussion of the current state of the art and propose future directions within the proposed classification.

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“…To imitate the soft object manipulation with human hands, and validate the effectiveness of realtime feedback in a robotic soft object manipulation task, a hand gesture-based teleoperation using Leap Motion [35] is an appropriate technique to extract the control signals from hand gestures to teleoperate the soft object in a real-time manner, and the corresponding experimental setup is shown in Fig. 8, where the robot grippers are fully constrained [36] as during the data collection stage. The related shape dataset of this manipulation task is shown in the Table I (dataset #2).…”

Section: Latent Shape Spacementioning

confidence: 99%

LaSeSOM: A Latent and Semantic Representation Framework for Soft Object Manipulation

Zhou

Zhu

Huo

et al. 2021

IEEE Robot. Autom. Lett.

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Soft object manipulation has recently gained popularity within the robotics community due to its potential applications in many economically important areas. Although great progress has been recently achieved in these types of tasks, most state-of-the-art methods are case-specific; They can only be used to perform a single deformation task (e.g. bending), as their shape representation algorithms typically rely on "hard-coded" features. In this paper, we present LaSeSOM, a new feedback latent representation framework for semantic soft object manipulation. Our new method introduces internal latent representation layers between low-level geometric feature extraction and high-level semantic shape analysis; This allows the identification of each compressed semantic function and the formation of a valid shape classifier from different feature extraction levels. The proposed latent framework makes soft object representation more generic (independent from the object's geometry and its mechanical properties) and scalable (it can work with 1D/2D/3D tasks). Its high-level semantic layer enables to perform (quasi) shape planning tasks with soft objects, a valuable and underexplored capability in many soft manipulation tasks. To validate this new methodology, we report a detailed experimental study with robotic manipulators.

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A dynamic and uncalibrated method to visually servo-control elastic deformations by fully-constrained robotic grippers

Cited by 13 publications

References 21 publications

Multi-Modal Sensing and Robotic Manipulation of Non-Rigid Objects: A Survey

Multi-Modal Sensing and Robotic Manipulation of Non-Rigid Objects: A Survey

Robotic manipulation and sensing of deformable objects in domestic and industrial applications: a survey

LaSeSOM: A Latent and Semantic Representation Framework for Soft Object Manipulation

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