Remote human–robot collaboration: A cyber–physical system application for hazard manufacturing environment

Liu, Hongyi; Wang, Lihui

doi:10.1016/j.jmsy.2019.11.001

Cited by 107 publications

(41 citation statements)

References 32 publications

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“…Innovative solutions have been recently proposed, e.g., a completely remote human-robot collaboration system in [15] modes with the use of a collaborative robot and an industrial manipulator for hazardous tasks or through the use of vibrotactile rings. In [16], such a type of device is used to send acknowledgments to the user during critical phases of a collaborative assembly task.…”

Section: From Isolated To Coworking Robotsmentioning

confidence: 99%

Robots in Industry: The Past, Present, and Future of a Growing Collaboration With Humans

Grau

Indri

Bello

et al. 2021

EEE Ind. Electron. Mag.

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Section: From Isolated To Coworking Robotsmentioning

confidence: 99%

Robots in Industry: The Past, Present, and Future of a Growing Collaboration With Humans

Grau

Indri

Bello

et al. 2021

EEE Ind. Electron. Mag.

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“…With the development and advancement of the CPSs and Industry 4.0, more and more industries are adopting remote working solutions for security reasons, where human operators cooperate with robots remotely, reprogramming, and controlling them from a safe environment. In such scenarios, HRI assumes a new and more important meaning, as the need for advances in technology and applications can be extremely helpful in allowing human operators to remotely lead-through industrial robots [ 134 ]. Advancements in IoRT technologies could be the answer for a new, better way to manage industrial operations.…”

Section: Issues and Challengesmentioning

confidence: 99%

Internet of Robotic Things in Smart Domains: Applications and Challenges

Romeo

Petitti

Marani

et al. 2020

Sensors

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With the advent of the Fourth Industrial Revolution, Internet of Things (IoT) and robotic systems are closely cooperating, reshaping their relations and managing to develop new-generation devices. Such disruptive technology corresponds to the backbone of the so-called Industry 4.0. The integration of robotic agents and IoT leads to the concept of the Internet of Robotic Things, in which innovation in digital systems is drawing new possibilities in both industrial and research fields, covering several domains such as manufacturing, agriculture, health, surveillance, and education, to name but a few. In this manuscript, the state-of-the-art of IoRT applications is outlined, aiming to mark their impact on several research fields, and focusing on the main open challenges of the integration of robotic technologies into smart spaces. IoRT technologies and applications are also discussed to underline their influence in everyday life, inducing the need for more research into remote and automated applications.

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“…Currently, several applications, such as teleoperation [ 1 ], intelligent vehicles and aircraft [ 2 ], assistive and rehabilitation technology [ 3 ], and robot-assisted surgery [ 4 ] require the collaboration of human and robot partners to accomplish a shared task. To implement efficient communication in a human–robot collaboration system, both humans and robots need to understand the current state of their partner and be able to predict what they will do next [ 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

EMG-Based 3D Hand Motor Intention Prediction for Information Transfer from Human to Robot

Feleke

Fei

2021

Sensors

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(1) Background: Three-dimensional (3-D) hand position is one of the kinematic parameters that can be inferred from Electromyography (EMG) signals. The inferred parameter is used as a communication channel in human–robot collaboration applications. Although its application from the perspective of rehabilitation and assistive technologies are widely studied, there are few papers on its application involving healthy subjects such as intelligent manufacturing and skill transfer. In this regard, for tasks associated with complex hand trajectories without the consideration of the degree of freedom (DOF), the prediction of 3-D hand position from EMG signal alone has not been addressed. (2) Objective: The primary aim of this study is to propose a model to predict human motor intention that can be used as information from human to robot. Therefore, the prediction of a 3-D hand position directly from the EMG signal for complex trajectories of hand movement, without the direct consideration of joint movements, is studied. In addition, the effects of slow and fast motions on the accuracy of the prediction model are analyzed. (3) Methods: This study used the EMG signal that is collected from the upper limb of healthy subjects, and the position signal of the hand while the subjects manipulate complex trajectories. We considered and analyzed two types of tasks with complex trajectories, each with quick and slow motions. A recurrent fuzzy neural network (RFNN) model was constructed to predict the 3-D position of the hand from the features of EMG signals alone. We used the Pearson correlation coefficient (CC) and normalized root mean square error (NRMSE) as performance metrics. (4) Results: We found that 3-D hand positions of the complex movement can be predicted with the mean performance of CC = 0.85 and NRMSE = 0.105. The 3-D hand position can be predicted well within a future time of 250 ms, from the EMG signal alone. Even though tasks performed under quick motion had a better prediction performance; the statistical difference in the accuracy of prediction between quick and slow motion was insignificant. Concerning the prediction model, we found that RFNN has a good performance in decoding for the time-varying system. (5) Conclusions: In this paper, irrespective of the speed of the motion, the 3-D hand position is predicted from the EMG signal alone. The proposed approach can be used in human–robot collaboration applications to enhance the natural interaction between a human and a robot.

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Remote human–robot collaboration: A cyber–physical system application for hazard manufacturing environment

Cited by 107 publications

References 32 publications

Robots in Industry: The Past, Present, and Future of a Growing Collaboration With Humans

Robots in Industry: The Past, Present, and Future of a Growing Collaboration With Humans

Internet of Robotic Things in Smart Domains: Applications and Challenges

EMG-Based 3D Hand Motor Intention Prediction for Information Transfer from Human to Robot

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