A biomimetic reach and grasp approach for mechanical hands

Touvet, François; Daoud, Naël; Gazeau, Jean Pierre; Zeghloul, S.; Maier, Marc A.; Eskiizmirliler, Sélim

doi:10.1016/j.robot.2011.07.017

Cited by 10 publications

(5 citation statements)

References 22 publications

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“…Achieving human level dexterity with fully actuated, high degrees of freedom prosthetic hands requires the use of complex control algorithms (Touvet et al, 2012 ). In the literature, there exists myoelectrically controlled hand prostheses that are capable of performing a large variety of manipulation tasks (Chu et al, 2007 ; Castellini et al, 2008 ; Li and Kuiken, 2009 ); however, the control of each finger joint in these systems is realized by means of sophisticated algorithms, whose complexity exposes amputees to long training periods.…”

Section: Introductionmentioning

confidence: 99%

Design, Implementation, and Evaluation of a Variable Stiffness Transradial Hand Prosthesis

Hocaoğlu

Patoğlu

2022

Front. Neurorobot.

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We present the design, implementation, and experimental evaluation of a low-cost, customizable, easy-to-use transradial hand prosthesis capable of adapting its compliance. Variable stiffness actuation (VSA) of the prosthesis is based on antagonistically arranged tendons coupled to nonlinear springs driven through a Bowden cable based power transmission. Bowden cable based antagonistic VSA can, not only regulate the stiffness and the position of the prosthetic hand but also enables a light-weight and low-cost design, by the opportunistic placement of motors, batteries, and controllers on any convenient location on the human body, while nonlinear springs are conveniently integrated inside the forearm. The transradial hand prosthesis also features tendon driven underactuated compliant fingers that allow natural adaption of the hand shape to wrap around a wide variety of object geometries, while the modulation of the stiffness of their drive tendons enables the prosthesis to perform various tasks with high dexterity. The compliant fingers of the prosthesis add inherent robustness and flexibility, even under impacts. The control of the variable stiffness transradial hand prosthesis is achieved by an sEMG based natural human-machine interface.

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Section: Introductionmentioning

confidence: 99%

Design, Implementation, and Evaluation of a Variable Stiffness Transradial Hand Prosthesis

Hocaoğlu

Patoğlu

2022

Front. Neurorobot.

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“…In general, problem is shrunk into hand grasping for certain types of robotic hands. Several examples of approaches adopted in the robotics fields can be found in [1][2][3][4]. In general, disadvantages of these methods are that they are all hardware dependent and main focus is daily used objects (such as bottles, cans and pans).…”

Section: Related Workmentioning

confidence: 99%

A Data-Driven Approach to Predict Hand Positions for Two-Hand Grasps of Industrial Objects

Arisoy

Ren

Ulu

et al. 2016

Volume 1A: 36th Computers and Information in Engineering Conference

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The wide spread use of 3D acquisition devices with high-performance processing tools has facilitated rapid generation of digital twin models for large production plants and factories for optimizing work cell layouts and improving human operator effectiveness, safety and ergonomics. Although recent advances in digital simulation tools have enabled users to analyze the workspace using virtual human and environment models, these tools are still highly dependent on user input to configure the simulation environment such as how humans are picking and moving different objects during manufacturing. As a step towards, alleviating user involvement in such analysis, we introduce a data-driven approach for estimating natural grasp point locations on objects that human interact with in industrial applications. Proposed system takes a CAD model as input and outputs a list of candidate natural grasping point locations. We start with generation of a crowdsourced grasping database that consists of CAD models and corresponding grasping point locations that are labeled as natural or not. Next, we employ a Bayesian network classifier to learn a mapping between object geometry and natural grasping locations using a set of geometrical features. Then, for a novel object, we create a list of candidate grasping positions and select a subset of these possible locations as natural grasping contacts using our machine learning model. We evaluate the advantages and limitations of our method by investigating the ergonomics of resulting grasp postures.

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“…This can be done using an algorithm such as that stated in Daoud et al (2012) and Touvet et al (2012). We then solved the inverse kinematics throughout the whole motion so that the fingertips drive the object.…”

Section: Dynamic Manipulation For a Robot Handmentioning

confidence: 99%

Using a motion capture system to identify pertinent design parameters of a bio-inspired mechanical hand

Rojas-Quintero

Seguin

Gazeau

et al. 2013

Computer Methods in Biomechanics and Biomedical Engineering

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A biomimetic reach and grasp approach for mechanical hands

Cited by 10 publications

References 22 publications

Design, Implementation, and Evaluation of a Variable Stiffness Transradial Hand Prosthesis

Design, Implementation, and Evaluation of a Variable Stiffness Transradial Hand Prosthesis

A Data-Driven Approach to Predict Hand Positions for Two-Hand Grasps of Industrial Objects

Using a motion capture system to identify pertinent design parameters of a bio-inspired mechanical hand

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