Performing predefined tasks using the human–robot interaction on speech recognition for an industrial robot

Bingöl, Mustafa Can; Aydoğmuş, Ömür

doi:10.1016/j.engappai.2020.103903

Cited by 72 publications

(29 citation statements)

References 42 publications

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“…Recently, Artificial Intelligence (AI) has become more and more popular and important in real life and it consists of many research fields, such as language recognition, 18,19 image recognition, 20,21 natural language processing, 22–24 and so forth. From an AI point of view, one of the most important parts is how to collect and represent natural languages correctly.…”

Section: Dhlts and Its Extensionsmentioning

confidence: 99%

Double hierarchy linguistic term set and its extensions: The state‐of‐the‐art survey

Gou

Zhang

2020

Int J Intell Syst

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Section: Dhlts and Its Extensionsmentioning

confidence: 99%

Double hierarchy linguistic term set and its extensions: The state‐of‐the‐art survey

Gou

Zhang

2020

Int J Intell Syst

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“…The complicated processing of the voice and pictures empowered the manipulator with much more intelligence and helped the robot interact with people naturally. Sensors fusion teleoperation is based on tactile and non-tactile sensors [21][22]. The tactile sensors in the fusion teleoperation were usually small but were unable to provide high-dimension control directly.…”

Section: Introductionmentioning

confidence: 99%

A Novel 6-DOF Force-Sensed Human-Robot Interface for an Intuitive Bilateral Teleoperation

Liu

Xie

et al. 2022

Preprint

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The teleoperation of a 6 degrees-of-freedom (DOF) manipulator is one of the basic methods to extend people’s capabilities in the wide variety of applications. The master interface based on the force/torque (FT) sensor could provide the full-dimension intuitive bilateral teleoperation of a 6-DOF robot since it has the ability to trigger 6-DOF command input. However, due to the force coupling, noise disturbance and unlimited input values of the FT sensor, this forces-sensed interface could not be widely used in practice. In this paper, we present an intuitive teleoperation method based on the force-sensed sensor from mechanical design and input force signal process to overcome these challenges. In this method, the input signals from the force-sensed joystick were filtered and then processed to the force commands by force limit algorithm, with the merits of anti-interference, output limitation, and online velocity adjustment. Furthermore, based on the admittance control and position controller, the manipulator could be teleoperated by the force commands. Three experiments were conducted on our self-designed robotic system. The result of the first experiment shows that the interfered force from the force coupling could be effectively suppressed with the limitation of the input force through force limit algorithm. Then, a parameter was introduced in the other two experiments to adjust the velocity online practically with force limit algorithm. The proposed method could give a practical solution to the bilateral teleoperation based on the FT sensor.

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“…Speech recognition is widely employed to identify the operator [7] [10] and extract the emotions of human beings [8] [9] to communicate with social robots and service robots. Speech instructions are also recognized to program industrial robots [6]. However, the aforementioned methods either contain a limited number of speech instructions or have relatively low accuracy in speech recognition, which is not sufficient for fast and accurate control of both the position and orientation of the robotic EE.…”

Section: Introductionmentioning

confidence: 99%

An Online Robot Teaching Method using Static Hand Gestures and Poses

Sun¹,

Zhang²,

Chen³

et al. 2021

Preprint

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With an increasing number of robots are employed in manufacturing, a human-robot interaction method that can teach robots in a natural, accurate, and rapid manner is needed. In this paper, we propose a novel human-robot interface based on the combination of static hand gestures and hand poses. In our proposed interface, the pointing direction of the index finger and the orientation of the whole hand are extracted to indicate the moving direction and orientation of the robot in a fast-teaching mode. A set of hand gestures are designed according to their usage in humans' daily life and recognized to control the position and orientation of the robot in a fine-teaching mode. We employ the feature extraction ability of the hand pose estimation network via transfer learning and utilize attention mechanisms to improve the performance of the hand gesture recognition network. The inputs of hand pose estimation and hand gesture recognition networks are monocular RGB images, making our method independent of depth information input and applicable to more scenarios. In the regular shape reconstruction experiments on the UR3 robot, the mean error of the reconstructed shape is less than 1 mm, which demonstrates the effectiveness and efficiency of our method.

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Performing predefined tasks using the human–robot interaction on speech recognition for an industrial robot

Cited by 72 publications

References 42 publications

Double hierarchy linguistic term set and its extensions: The state‐of‐the‐art survey

Double hierarchy linguistic term set and its extensions: The state‐of‐the‐art survey

A Novel 6-DOF Force-Sensed Human-Robot Interface for an Intuitive Bilateral Teleoperation

An Online Robot Teaching Method using Static Hand Gestures and Poses

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