Young Researcher Paper Award 2023
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Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
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Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.
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Sensors and Materials
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is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.
Instructions to authors
English 日本語
Instructions for manuscript preparation
English 日本語
Template
English
Publisher
MYU K.K.
Sensors and Materials
1-23-3-303 Sendagi,
Bunkyo-ku, Tokyo 113-0022, Japan
Tel: 81-3-3827-8549
Fax: 81-3-3827-8547
MYU Research, a scientific publisher, seeks a native English-speaking proofreader with a scientific background. B.Sc. or higher degree is desirable. In-office position; work hours negotiable. Call 03-3827-8549 for further information.
MYU Research
(proofreading and recording)
MYU K.K.
(translation service)
The Art of Writing Scientific Papers
(How to write scientific papers)
(Japanese Only)
Sensors and Materials, Volume 35, Number 4(3) (2023)
Copyright(C) MYU K.K.
Copyright(C) MYU K.K.
|
pp. 1497-1508
S&M3263 Research Paper of Special Issue https://doi.org/10.18494/SAM4263 Published: April 27, 2023 Energy Prediction of Cleanroom-type Differential Drive Mobile Robot Based on Recurrent Neural Network [PDF] Sarucha Yanyong, Poom Konghuayrob, Punyavee Chaisiri, and Somyot Kaitwanidvilai (Received November 29, 2022; Accepted February 21, 2023) Keywords: energy sensing, energy prediction, machine learning, recurrent neural network, autonomous mobile robot
The battery charger time is a major issue for mobile robots. The study of the power usage of each component is important for optimizing the overall power consumption. Additionally, knowing the total energy consumption before commanding a robot to execute a task is essential for effective queue management and determining which robots are ready to execute tasks or move to the charging station. In this paper, we propose an energy modeling system consisting of an energy sensing technique, logging, and a recurrent neural network prediction model. The model is configured to recognize the dynamic system of the drive unit with the support of the robot operating system. The proposed model has a prediction error of only 3.58%. The simulation and experimental results demonstrate the effectiveness of the proposed system.
Corresponding author: Somyot Kaitwanidvilai  This work is licensed under a Creative Commons Attribution 4.0 International License. Cite this article Sarucha Yanyong, Poom Konghuayrob, Punyavee Chaisiri, and Somyot Kaitwanidvilai, Energy Prediction of Cleanroom-type Differential Drive Mobile Robot Based on Recurrent Neural Network, Sens. Mater., Vol. 35, No. 4, 2023, p. 1497-1508. |
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