A force-and-slippage control strategy for a poliarticulated prosthetic hand

Cordella, Francesca; Gentile, Chiara; Zollo, Loredana; Barone, Roberto; Sacchetti, Rinaldo; Davalli, Angelo; Siciliano, Bruno; Guglielmelli, Eugenio

doi:10.1109/icra.2016.7487533

Cited by 26 publications

(23 citation statements)

References 18 publications

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“…At the slip moment, significant changes occur in the tactile signal; when the derivative of a force signal overcomes a given empirical threshold, slip might be identified. Successful application was shown on normal force component [134], [137], [135],. ( [136], on tangential force component [138] and on hydroacoustic pressure [88].…”

Section: Alternative Approachesmentioning

confidence: 98%

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Methods and Sensors for Slip Detection in Robotics: A Survey

Romeo

Zollo

2020

IEEE Access

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The perception of slip is one of the distinctive abilities of human tactile sensing. The sense of touch allows recognizing a wide set of properties of a grasped object, such as shape, weight and dimension. Based on such properties, the applied force can be accordingly regulated avoiding slip of the grasped object. Despite the great importance of tactile sensing for humans, mechatronic hands (robotic manipulators, prosthetic hands etc.) are rarely endowed with tactile feedback. The necessity to grasp objects relying on robust slip prevention algorithms is not yet corresponded in existing artificial manipulators, which are relegated to structured environments then. Numerous approaches regarding the problem of slip detection and correction have been developed especially in the last decade, resorting to a number of sensor typologies. However, no impact on the industrial market has been achieved. This paper reviews the sensors and methods so far proposed for slip prevention in artificial tactile perception, starting from more classical techniques until the latest solutions tested on robotic systems. The strengths and weaknesses of each described technique are discussed, also in relation to the sensing technologies employed. The result is a summary exploring the whole state of art and providing a perspective towards the future research directions in the sector.

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Section: Alternative Approachesmentioning

confidence: 98%

“…10 plots the results of a grasping trial. In [137] the derivative of the normal force square root was computed and then evaluated through an empirical threshold. Force was measured by FSRs attached on the thumb and index of a prosthetic hand (IH2 Azzurra).…”

Section: A Differentiationmentioning

confidence: 99%

Methods and Sensors for Slip Detection in Robotics: A Survey

Romeo

Zollo

2020

IEEE Access

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“…Measures of system robustness and reliability will be performed testing the proposed approach during the control of prosthetic devices. Advanced control strategies (Ciancio et al, 2015; Barone et al, 2016) will be adopted to allow force regulation and slippage management during grasping (Cordella et al, 2016).…”

Section: Discussionmentioning

confidence: 99%

Simultaneous sEMG Classification of Hand/Wrist Gestures and Forces

et al. 2019

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Surface electromyography (sEMG) signals represent a promising approach for decoding the motor intention of amputees to control a multifunctional prosthetic hand in a non-invasive way. Several approaches based on proportional amplitude methods or simple thresholds on sEMG signals have been proposed to control a single degree of freedom at time, without the possibility of increasing the number of controllable multiple DoFs in a natural manner. Myoelectric control based on PR techniques have been introduced to add multiple DoFs by keeping low the number of electrodes and allowing the discrimination of different muscular patterns for each class of motion. However, the use of PR algorithms to simultaneously decode both gestures and forces has never been studied deeply. This paper introduces a hierarchical classification approach with the aim to assess the desired hand/wrist gestures, as well as the desired force levels to exert during grasping tasks. A Finite State Machine was introduced to manage and coordinate three classifiers based on the Non-Linear Logistic Regression algorithm. The classification architecture was evaluated across 31 healthy subjects. The “hand/wrist gestures classifier,” introduced for the discrimination of seven hand/wrist gestures, presented a mean classification accuracy of 98.78%, while the “Spherical and Tip force classifier,” created for the identification of three force levels, reached an average accuracy of 98.80 and 96.09%, respectively. These results were confirmed by Linear Discriminant Analysis (LDA) with time domain features extraction, considered as ground truth for the final validation of the performed analysis. A Wilcoxon Signed-Rank test was carried out for the statistical analysis of comparison between NLR and LDA and statistical significance was considered at p < 0.05. The comparative analysis reports not statistically significant differences in terms of F1Score performance between NLR and LDA. Thus, this study reveals that the use of non-linear classification algorithm, as NLR, is as much suitable as the benchmark LDA classifier for implementing an EMG pattern recognition system, able both to decode hand/wrist gestures and to associate different performed force levels to grasping actions.

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“…Nevertheless, we use low-cost tactile sensors unable to measure the tangential component of the force. A similar approach to slipping avoidance is based on machine learning and is proposed in [26], which was specifically tested on a prosthetic hand and uses demonstrations from humans. In our case, we do not exploit human demonstrations, but we use executions from the robotic system to train our slipping model.…”

Section: Reactive Control Modulementioning

confidence: 99%

On Policy Learning Robust to Irreversible Events: An Application to Robotic In-Hand Manipulation

Falco

Attawia

Saveriano

et al. 2018

IEEE Robot. Autom. Lett.

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Abstract-In this paper, we present an approach for learning in-hand manipulation skills with a low-cost, underactuated prosthetic hand in presence of irreversible events. Our approach combines reinforcement learning based on visual perception with low-level reactive control based on tactile perception, which aims to avoid slipping. The objective of the reinforcement learning level consists not only in fulfilling the in-hand manipulation goal, but also in minimizing the intervention of the tactile reactive control. This way, the occurrence of object slipping during the learning procedure, which we consider an irreversible event, is significantly reduced. When an irreversible event occurs, the learning process is considered failed. We show the performance in two tasks, which consist in re-orienting a cup and a bottle only using the fingers. The experimental results show that the proposed architecture allows reaching the goal in the Cartesian space and reduces significantly the occurrence of object slipping during the learning procedure. Moreover, without the proposed synergy between reactive control and reinforcement learning it was not possible to avoid irreversible events and, therefore, to learn the task.

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A force-and-slippage control strategy for a poliarticulated prosthetic hand

Cited by 26 publications

References 18 publications

Methods and Sensors for Slip Detection in Robotics: A Survey

Methods and Sensors for Slip Detection in Robotics: A Survey

Simultaneous sEMG Classification of Hand/Wrist Gestures and Forces

On Policy Learning Robust to Irreversible Events: An Application to Robotic In-Hand Manipulation

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