A Speech Recognition Client-Server Model for Control of Multiple Robots

Shrivastava, Kunal; Singhal, Nishant; Das, Pradip K.; Nair, Shivashankar B.

doi:10.1145/2506095.2506153

Cited by 3 publications

(7 citation statements)

References 2 publications

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“…A better approach was proposed in [12] where a clientserver architecture for controlling multiple robots using voice commands was used. But the problem with these kind of systems is that they are not scalable and cannot handle high loads.…”

Section: Related Workmentioning

confidence: 99%

A mobile agents based distributed speech recognition engine for controlling multiple robots

Gupta

Verma

Bhattacharya

et al. 2015

Proceedings of the 2015 Conference on Advances in Robotics

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Interaction with a robot has been an active area of research since the inception of robotics. Talking to a robot has always been considered the most natural way to communicate with it. But it is not always possible to have a full-fledged, standalone speech processing engine to be present on a robot or on a single machine. A dedicated system to convert the commands from audio to text is needed. However, as the number of commands and robots increases, it becomes necessary to eliminate all the single-point failure points in the system. Thus, distributed speech engine comes into picture. Also users may want to talk to the robot in different languages.The approach proposed in this paper is distributed, fault tolerant and scalable, such that any new recognition algorithm or language support can be added and used without any changes to the existing system.The work has been demonstrated on a freely available mobile agents based Internet of Things platform. However, any platform can be used.

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Section: Related Workmentioning

confidence: 99%

A mobile agents based distributed speech recognition engine for controlling multiple robots

Gupta

Verma

Bhattacharya

et al. 2015

Proceedings of the 2015 Conference on Advances in Robotics

Self Cite

View full text Add to dashboard Cite

show abstract

“…It is obvious that interacting with speech is more natural and convenient than using hand gestures, eye motion, etc. With the development of automatic speech recognition (ASR) and Natural Language Processing (NLP) technologies, researchers gain the opportunity to design and implement speech based HRI [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

“…However, VoiceBot does not recognize natural spoken language and only supports a set of pre-defined voice command to control the arm, where the user has to be well trained before using the system properly. In [2], a client-server based architecture for controlling multiple robots simultaneously through predefined voice commands is developed. While the application of speech recognition allows to interacting with spoken language, this architecture only supports seven predefined commands to control the robots (directions and speed) and the voice command should follow the fixed grammars.…”

Section: Introductionmentioning

confidence: 99%

“…The limitation of existing voice control systems is that users' voice need to follow the pre-defined commands or grammars in order that the system can retrieve command from recognized voice. For instance, when a user says "Please go", the robot in [2] would not response for its failure to match "Please go" with any pre-set command, while the correct command "forward" can be easily implied by a human.…”

Section: Introductionmentioning

confidence: 99%

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An Attention-Based Command Detection Model to Allow Natural Language in Voice Control System

He¹,

Li²,

Yang³

2019

dtcse

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Interaction with machines using speech has been a popular research topic in recent years. Considerable amount of work focuses on the improvement of voice control systems which utilize automatic speech recognition (ASR) and natural language processing (NLP) technologies to recognize user's speech and extract executable commands. The existing voice control systems usually do not take into count the diversity and richness of natural language and require users to follow pre-defined keywords or grammar rules. To address this limitation, we designed and implemented a voice control system that supports natural language, utilizing an attention-based command detection model. Our system supports flexible voice instructions and the user does not need to follow any pre-defined rules. An Arduino 4WD robot car was also built in this paper to verify the system. In the experiments, the accuracy of command detection on natural language reaches 0.993 in our developed dataset. Besides, the realization of the 3-DOF (degree of freedom) motion control on our robot car suggests the feasibility of using our proposed system to control any functionality or behavior of the hardware system. Our work improves the flexibility and usability of voice control systems by applying technologies in the domain of NLP.

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“…Interacting with mobile robots using speech is of particular interest to researchers since verbal communication is the most natural way of communication for human beings. Speech has been previously used to perform different tasks, such as command based voice teleoperation of a mobile robot [17][18]. The paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

Wi-Fi Control of Mobile Robot Motion Types Based on Differential Drive Kinematics Modelling Approach

Taskiran¹,

Durna²,

Koçer³

2016

International Journal of Applied Mathematics, Electronics and Computers

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Abstract:Recently, utilization of mobile robots has increased substantially. Accordingly, wireless communication is preferred in mobile robots. QBot 2 is an autonomous ground robot which is a new product of Quarc. The QBot 2 utilizes an onboard data acquisition card and a wireless embedded computer to measure the onboard sensors and drive motors. In this study, QBot 2 mobile robot is evaluated in terms of its ability to maneuver. In this way, controlling QBot 2 mobile robot in real time is planned while a command recognition system is developed. Connection with the QBot 2 is carried out in a wireless environment. A Simulink model is developed in MATLAB® environment. The created model is built with Quarc control software. Compiled model is downloaded with TCP/IP connection to QBot 2 and the application is carried out on an embedded computer. The QBot 2 mobile platform consists of two central drive wheels mounted on a common axis. This drive configuration is known as differential drive. The two drive wheels are independently driven forward and backward in order to actuate the robot. Motion of the wheels is realized using high performance DC motors. When the results are analyzed, 13 different motion types are observed in total. The observed motion types could be used as references in future works since many practical applications, such as the remote control of QBot 2 mobile robot via the human voice, require the availability of different motion types.

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A Speech Recognition Client-Server Model for Control of Multiple Robots

Cited by 3 publications

References 2 publications

A mobile agents based distributed speech recognition engine for controlling multiple robots

A mobile agents based distributed speech recognition engine for controlling multiple robots

An Attention-Based Command Detection Model to Allow Natural Language in Voice Control System

Wi-Fi Control of Mobile Robot Motion Types Based on Differential Drive Kinematics Modelling Approach

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