A supervisory control system for a multi-fingered robotic hand using datagloves and a haptic device

Yoshimura, Youtaro; Ozawa, Ryuta

doi:10.1109/iros.2012.6385486

Cited by 11 publications

(4 citation statements)

References 16 publications

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“…Interestingly, in [108], the measurements from a dataglove-based SE are exploited to recognize the object to be grasped, and then a predefined grasping motion of the TH is activated on the basis of a compatibility index. Differently, in [110] and [105], the result of the PH posture recognition based on machine learning is used to drive the transitions of finite state machines in order to identify the action to be executed by the TH. Representative hand posture recognition mappings are reported in Table IX.…”

Section: B Literature Reviewmentioning

confidence: 99%

Human to Robot Hand Motion Mapping Methods: Review and Classification

Meattini

Suárez²,

Palli

et al. 2023

IEEE Trans. Robot.

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Section: B Literature Reviewmentioning

confidence: 99%

Human to Robot Hand Motion Mapping Methods: Review and Classification

Meattini

Suárez²,

Palli

et al. 2023

IEEE Trans. Robot.

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“…the Inertial Measurement Unit (IMU), flex sensors, and the Arduino Uno microcontroller. A -16 -similar concept of this methodology can also be found in the following work of [95][96][97][98][99]. In addition to the IMU, three flex sensors are needed to capture the movements of the operator's thumb and first two fingers.…”

Section: Real-time Glove Controlmentioning

confidence: 99%

Deployment Of Robot Arms With A Leader-Follower Approach For Bomb Disposal

Chau¹

2021

Preprint

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<div>Improvised Explosive Devices (IEDs) have been developed over the years across many nations around the world. IEDs used by terrorist actions and in warfare cause devastating death, injuries and damage. To protect the public, many emergency responders have to risk their lives by performing extremely hazardous tasks such as interacting with suspected IEDs. To prevent the emergency response teams from being negatively impacted by IEDs, many different kinds of response robots have been deployed in many locations worldwide – allowing first responders a safe way to interact with these menaces from a distance. This thesis contributes to the understanding of using robot arms with a Leader–Follower (LF) approach to help humans with performing dexterous operations like those which are inevitably required for manipulating IEDs remotely. The LF approach allows operators to remotely manipulate a robot arm without putting operators’ lives in danger. By physically controlling one arm from a safe distance, operators can successfully copy its movements to a second arm. As a result, we argue, this approach can be helpful for minimizing operator risk when interacting with suspicious devices while at the same time facilitating more intuitive remote control.</div>

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“…Other types of mapping are based on recognizing the PH posture in order to reproduce it on the TH ( Ekvall and Kragic, 2004 ; Pedro et al, 2012 ; Ficuciello et al, 2021 ). In this kind of approach, the functions of the TH are limited to a discrete set of predefined grasps/motions and, thereafter, based on the output of the PH posture recognition process (mostly based on machine learning techniques ( Dillmann et al, 2000 ; Yoshimura and Ozawa, 2012 )), one of the available TH motions is selected and executed. In this case, the operator can easily learn how to configure ones own hand in order to activate specific actions of the robot hand, whereas the lack of continuous control, and the fact that the number of predefined TH grasps/motions increases with the complexity of the applications, de facto make this mapping method difficult to be applied for several applications (e.g., precision grasps and gestures/grasps not included in the predefined TH motions.)…”

Section: Introductionmentioning

confidence: 99%

Simulative Evaluation of a Joint-Cartesian Hybrid Motion Mapping for Robot Hands Based on Spatial In-Hand Information

et al. 2022

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Two sub-problems are typically identified for the replication of human finger motions on artificial hands: the measurement of the motions on the human side and the mapping method of human hand movements (primary hand) on the robotic hand (target hand). In this study, we focus on the second sub-problem. During human to robot hand mapping, ensuring natural motions and predictability for the operator is a difficult task, since it requires the preservation of the Cartesian position of the fingertips and the finger shapes given by the joint values. Several approaches have been presented to deal with this problem, which is still unresolved in general. In this work, we exploit the spatial information available in-hand, in particular, related to the thumb-finger relative position, for combining joint and Cartesian mappings. In this way, it is possible to perform a large range of both volar grasps (where the preservation of finger shapes is more important) and precision grips (where the preservation of fingertip positions is more important) during primary-to-target hand mappings, even if kinematic dissimilarities are present. We therefore report on two specific realizations of this approach: a distance-based hybrid mapping, in which the transition between joint and Cartesian mapping is driven by the approaching of the fingers to the current thumb fingertip position, and a workspace-based hybrid mapping, in which the joint–Cartesian transition is defined on the areas of the workspace in which thumb and fingertips can get in contact. The general mapping approach is presented, and the two realizations are tested. In order to report the results of an evaluation of the proposed mappings for multiple robotic hand kinematic structures (both industrial grippers and anthropomorphic hands, with a variable number of fingers), a simulative evaluation was performed.

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A supervisory control system for a multi-fingered robotic hand using datagloves and a haptic device

Cited by 11 publications

References 16 publications

Human to Robot Hand Motion Mapping Methods: Review and Classification

Human to Robot Hand Motion Mapping Methods: Review and Classification

Deployment Of Robot Arms With A Leader-Follower Approach For Bomb Disposal

Simulative Evaluation of a Joint-Cartesian Hybrid Motion Mapping for Robot Hands Based on Spatial In-Hand Information

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