Human–Robot Communication System for an Isolated Environment

Diddeniya, Isanka; Wanniarachchi, Indika; Gunasinghe, Hansi; Premachandra, Chinthaka; Kawanaka, Hiroharu

doi:10.1109/access.2022.3183110

Cited by 6 publications

(4 citation statements)

References 36 publications

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“…While reference [16] pointed out that signal processing and machine learning techniques must be used to recognize speech and that traditional systems have low performance. The approach of [37] describes that using mobile applications for speech recognition is possible as an alternative that allows processing tasks remotely or in contexts of high criticality on the user's part.…”

Section: Resultsmentioning

confidence: 99%

Recognition of Human Actions through Speech or Voice Using Machine Learning Techniques

Peña-Cáceres,

Silva-Marchan,

Albert

et al. 2023

CMC

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The development of artificial intelligence (AI) and smart home technologies has driven the need for speech recognition-based solutions. This demand stems from the quest for more intuitive and natural interaction between users and smart devices in their homes. Speech recognition allows users to control devices and perform everyday actions through spoken commands, eliminating the need for physical interfaces or touch screens and enabling specific tasks such as turning on or off the light, heating, or lowering the blinds. The purpose of this study is to develop a speech-based classification model for recognizing human actions in the smart home. It seeks to demonstrate the effectiveness and feasibility of using machine learning techniques in predicting categories, subcategories, and actions from sentences. A dataset labeled with relevant information about categories, subcategories, and actions related to human actions in the smart home is used. The methodology uses machine learning techniques implemented in Python, extracting features using CountVectorizer to convert sentences into numerical representations. The results show that the classification model is able to accurately predict categories, subcategories, and actions based on sentences, with 82.99% accuracy for category, 76.19% accuracy for subcategory, and 90.28% accuracy for action. The study concludes that using machine learning techniques is effective for recognizing and classifying human actions in the smart home, supporting its feasibility in various scenarios and opening new possibilities for advanced natural language processing systems in the field of AI and smart homes.

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Section: Resultsmentioning

confidence: 99%

Recognition of Human Actions through Speech or Voice Using Machine Learning Techniques

Peña-Cáceres,

Silva-Marchan,

Albert

et al. 2023

CMC

View full text Add to dashboard Cite

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“…The performance of the MQTT and CoAP communication protocols in the context of catastrophic multi-robot situations is compared in the research work by Yamin et al [37]. The results show that, when compared to CoAP in the Within the Ultra Narrowband Radio Pulse Function (UNR-PF) data transmission environment, the MQTT Protocol demonstrates a pronounced advantage in real-time communication, attributable to its elevated data transfer rate.…”

Section: Related Workmentioning

confidence: 99%

Dual Mode PID Controller for Path Planning of Encoder Less Mobile Robots in Warehouse Environment

Karthik,

Menaka,

Kishore

et al. 2024

IEEE Access

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Mobile robots have emerged as versatile substitutes for human labor across diverse domains, offering promising applications in surveillance, healthcare, and beyond. Fundamental to their autonomy are the core capabilities of movement, perception, cognition, and navigation. This research introduces a novel approach known as the Dual PID based low-cost navigation system (DPLNS), designed specifically for indoor warehouse-like environments. The primary objective of this technique is to enable seamless point-to-point traversal. This is achieved through a fusion of gyroscope correction and visual PID control mechanisms. Leveraging a strategically positioned eagle-eye perspective camera, the system gained crucial insights for navigation. To ensure the uninterrupted execution of planned trajectories, the system employs the Message Queuing Telemetry Transport (MQTT) protocol. This technology ensures smooth communication and coordination of actions. The experimental validation of the proposed strategy highlights its efficacy, positioning it as a promising solution for modern warehouse automation needs. By advancing the capabilities of mobile robots within warehouse contexts, this research not only addresses immediate operational demands, but also paves the way for broader applications across various other fields. The developed system enables robust detection and successful path traversal to the designated spot through a position control operating at average of 12.3 rad/s.

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“…It is well known that wireless communication systems often have time delays and loss of information [16]. However, in recent years, different communication protocols have been developed [17,18] and have presented improvements in sending/receiving data as well as minimal information loss [19,20].…”

Section: Related Workmentioning

confidence: 99%

Active Disturbance Rejection Strategy for Distance and Formation Angle Decentralized Control in Differential-Drive Mobile Robots

et al. 2022

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The important practical problem of robust synchronization in distance and orientation for a class of differential-drive mobile robots is tackled in this work as an active disturbance rejection control (ADRC) problem. To solve it, a kinematic model of the governed system is first developed based on the distance and formation angle between the agents. Then, a special high-order extended state observer is designed to collectively estimate the perturbations (formed by longitudinal and lateral slipping parameters) that affect the kinematic model. Finally, a custom error-based ADRC approach is designed and applied assuming that the distance and orientation between the agents are the only available measurements. The proposed control strategy does not need time-derivatives of the reference trajectory, which increases the practical appeal of the proposed solution. The experimental results, obtained in laboratory conditions with a set of differential-drive mobile robots operating in a leader–follower configuration, show the effectiveness of the proposed governing scheme in terms of trajectory tracking and disturbance rejection.

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Human–Robot Communication System for an Isolated Environment

Cited by 6 publications

References 36 publications

Recognition of Human Actions through Speech or Voice Using Machine Learning Techniques

Recognition of Human Actions through Speech or Voice Using Machine Learning Techniques

Dual Mode PID Controller for Path Planning of Encoder Less Mobile Robots in Warehouse Environment

Active Disturbance Rejection Strategy for Distance and Formation Angle Decentralized Control in Differential-Drive Mobile Robots

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