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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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MYU K.K.
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 37, Number 7(3) (2025)
Copyright(C) MYU K.K.
Copyright(C) MYU K.K.
|
pp. 3119-3133
S&M4107 Technical Paper of Special Issue https://doi.org/10.18494/SAM5629 Published: July 28, 2025 Six-degrees-of-freedom Redundant Task Trajectory Planning for Green Converter Station Robotic Arm Based on Multisensor Detection [PDF] Yang Li, Pengwang Zhang, Jinyun Yu, Keying Zou, Xianguang Jia, Junwei Yang, and Jing Bao (Received October 7, 2024; Accepted May 24, 2025) Keywords: trajectory planning, polynomial interpolation, arithmetic optimization algorithm, Latin hypercube sampling
In recent years, robotic arms have become increasingly common in green converter stations. In this paper, we introduce a time-optimal trajectory planning method based on the arithmetic optimization algorithm (AOA). The method utilizes sensor data as constraints and generates trajectories using 3-5-3 polynomial interpolation. To enhance the performance of the AOA, we propose an improved version, the improved AOA, which incorporates the Latin hypercube sampling and Gaussian variation. Simulation results demonstrate that the proposed algorithm effectively plans trajectories for industrial robotic arms, offering improved efficiency and accuracy in motion planning.
Corresponding author: Xianguang Jia  This work is licensed under a Creative Commons Attribution 4.0 International License. Cite this article Yang Li, Pengwang Zhang, Jinyun Yu, Keying Zou, Xianguang Jia, Junwei Yang, and Jing Bao, Six-degrees-of-freedom Redundant Task Trajectory Planning for Green Converter Station Robotic Arm Based on Multisensor Detection, Sens. Mater., Vol. 37, No. 7, 2025, p. 3119-3133. |
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Special Issue on Advanced Materials and Technologies for Sensor and Artificial- Intelligence-of-Things Applications (Selected Papers from ICASI 2025)
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- Flood Susceptibility Mapping Using Machine Learning Models with Novel Flood Inventory Sampling Strategies
Gen Long, Sarintip Tantanee, Korakod Nusit, and Pitikhate Sooraksad
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