Simplified human hand models based on grasping analysis

Cobos, Salvador; Ferré, Manuel; Aracil, Rafaél

doi:10.1109/iros.2010.5651479

Cited by 19 publications

(13 citation statements)

References 20 publications

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“…Therefore, their movement set is constrained and defined mathematically regarding other joints. With this in mind, different human hands' DoF can be hierarchized according to the relevance of its actions when performing particular tasks, such as grasping [25]. Table 2 lists the most relevant joints of the human hand, where aa and fe states the movements of the MCP and TMC joints for the front and thumb fingers, respectively.…”

Section: Hand Designmentioning

confidence: 99%

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Development of a Robotic Hand Using Bioinspired Optimization for Mechanical and Control Design: UnB-Hand

2021

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For the last four decades, the development of robotic hands has been the focus of several works. However, a small part of those approaches consider the exploitation of parallelism of FPGAbased (Field Programmable Gate Arrays) systems or discuss how using bioinspired optimization algorithms could improve the mechanical and controller components. This work considers developing a bioinspired robotic hand that achieves motion and force control with a logic hardware architecture implemented in FPGA intended to be replicated and executed with suitable parallelism, fitting a single device. The developed robotic hand prototype has five fingers and seven DoF (Degrees of Freedom). Using bioinspired optimization, such as PSO (Particle Swarm Optimization), both the rigid finger mechanism and the impedance controller were optimized and incorporated the results in several practical grasping experiments. The validation of this work is done with the Cutkosky grasping taxonomy and some grasping experiments with interference. The tests proved the proficiency of this works for a wide range of power and some precision grasp. The reader can see the experiments in the attached videos.

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Section: Hand Designmentioning

confidence: 99%

“…Interphalangeal constraints for the actuated joints. Adapted from Cobos[25] Ten more relevant human hand joints when performing grasping tasks. Refer to Fig.1for the positions of the joints.…”

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confidence: 99%

Development of a Robotic Hand Using Bioinspired Optimization for Mechanical and Control Design: UnB-Hand

2021

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“…The Dexmo is under-instrumented, in that the distal linkage rotations are unknown. When a hand adopts a circular power grasp however, the fingers follow a predictable trajectory [28]. The angles of the Proximal Interphalangeal (PIP) and Distal Interphalangeal (DIP) joints can be modelled as linear functions of each other and Metacarpal (MCP) flex [29]- [31].…”

Section: Pose Estimationmentioning

confidence: 99%

Position-Based Control of Under-Constrained Haptics: A System for the Dexmo Glove

Friston

Griffith

Swapp

et al. 2019

IEEE Robot. Autom. Lett.

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The Dexmo glove is a haptic exoskeleton that provides kinesthetic feedback in virtual reality. Unlike many other gloves based on string-pulleys, the Dexmo uses a free-hinged link-bar to transfer forces from a crank to the fingertips. It also uses an admittance-based controller parameterized by position, as opposed to an impedance-based controller parameterized by force. When setting the controller's target position, developers must use its native angular coordinate system. The Dexmo has a number of uninstrumented degrees of freedom. Mature forward models can reliably predict the hand pose, even with these unknowns. When it comes to computing angular controller parameters from a target pose in Cartesian space however, things become more difficult. Complex models that provide attractive visuals from a small number of sensors can be non-trivial or even impossible to invert. In this letter, we suggest side-stepping this issue. We sample the forward model in order to build a lookup table. This is embedded in three-dimensional space as a curve, on which traditional queries against world geometry can be performed. Controller parameters are stored as attributes of the sample points. To compute the driver parameters for a target position, the application constrains the position to the geometry, and interpolates them. This technique is generalizable, stable, simple, and fast. We validate our approach by implementing it in Unity 2017.3 and integrating it with a Dexmo glove. Index Terms-Data gloves, force feedback, virtual reality, robot control. I. INTRODUCTION H APTIC feedback extends the immersion of Virtual Environments (VEs) to new sensory modalities. Tactile feedback reproduces surface texture, while force feedback reproduces strain [1]. Haptic interface design is challenging due to the degrees-of-freedom and range-of-motion that must be supported. These lead to conflicting requirements: devices must be stiff but also lightweight; able to apply large forces on demand, but otherwise offer no impedance [2]. Tool-based devices, such as the Phantom Omni, provide high fidelity interaction,

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“…The aim of this article is to describe the preliminary development of a virtual reality system for simulating musculoskeletal assessment and treatment of the hand, where at least two contact points are required. This setup is based on the MasterFinger-2 [24] device that allows both recording of movements and forces applied by two points of contact during performance of real manipulations; and force feedback during simulations of the recorded scenarios which can then be performed on a virtual hand model [25]. Authors have cooperated at distance in defining and evaluating the manipulation patterns.…”

Section: Haptic Device For Capturing and Simulating Hand Manipulationmentioning

confidence: 99%

Haptic Device for Capturing and Simulating Hand Manipulation Rehabilitation

Ferré

Galiana

Wirz

et al. 2011

IEEE/ASME Trans. Mechatron.

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This article describes the preliminary development of a haptic setup for capturing and simulating musculoskeletal assessment and manipulation of the hand. A haptic device, called MasterFinger-2, is used for capturing one massage technique and one joint manipulation technique, and also for simulating this manipulation technique that can be used in both assessment and treatment of the hand. First, works developed demonstrate that application of haptic devices enable quantitative characterization of forces and positions used in manipulation of musculoskeletal structures. Secondly, an application for simulation is developed using the MasterFinger-2 to display (both visually and haptically) manipulations of one joint of the hand around three axes. The novel aspects of this approach are the use of a multifinger device for capture, simulation and modeling the movement of a biological joint for haptic simulation across three axes, each with non-linear behavior.

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Simplified human hand models based on grasping analysis

Cited by 19 publications

References 20 publications

Development of a Robotic Hand Using Bioinspired Optimization for Mechanical and Control Design: UnB-Hand

Development of a Robotic Hand Using Bioinspired Optimization for Mechanical and Control Design: UnB-Hand

Position-Based Control of Under-Constrained Haptics: A System for the Dexmo Glove

Haptic Device for Capturing and Simulating Hand Manipulation Rehabilitation

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