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A LSTM Neural Network applied to Mobile Robots Path Planning
http://hdl.handle.net/10131/00012500
http://hdl.handle.net/10131/00012500424e6cea-7ee1-4619-8ad9-7267c4689993
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
4_paper_final_version_proposal_FioratoNicola (480.3 kB)
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| アイテムタイプ | D_学術雑誌論文 / Journal Article_default(1) | |||||
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| 公開日 | 2019-05-23 | |||||
| タイトル | ||||||
| タイトル | A LSTM Neural Network applied to Mobile Robots Path Planning | |||||
| 言語 | en | |||||
| 言語 | ||||||
| 言語 | eng | |||||
| キーワード | ||||||
| 言語 | en | |||||
| 主題Scheme | Other | |||||
| 主題 | Logic gates, Training, Path planning, Recurrent neural networks, Mobile robots, Analytical models | |||||
| 資源タイプ | ||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||
| 資源タイプ | journal article | |||||
| アクセス権 | ||||||
| アクセス権 | open access | |||||
| アクセス権URI | http://purl.org/coar/access_right/c_abf2 | |||||
| 著者 |
Nicola, Fiorato
× Nicola, Fiorato× Fujimoto, Yasutaka× Oboe, Roberto |
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| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | Mobile robots path planning is a central problem in every situation where human intervention is not desired or not possible to accept: full automated industrial warehouses or general stocking areas and every domestic application that involves a mobile robot and special cases where environment is prohibited for human accessing like toxic wastes and bombs defusing [1]. Currently, neural networks are applied to problems related to mobile robot navigation. However, they are not as popular as in applications like image processing, speech recognition or machine translation, where they are commercially relevant. In this paper we propose a Long Short-Term Memory (LSTM) neural network as an online search agent to tackle the problem of mobile robots path planning in unknown environments, meaning that the agent relies only on local map awareness realized with a LRF sensor and relative information between robot and goal position. Specifically, a final structure of LSTM network is analyzed and its performance is compared with the A* algorithm, a widely known method that follows the best-first search approach. Subsequently, an analysis of the method developed on a real robot is described. | |||||
| 書誌情報 |
2018 IEEE 16th International Conference on Industrial Informatics (INDIN) 発行日 2018-09-27 |
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| 出版者 | ||||||
| 出版者 | IEEE | |||||
| 言語 | en | |||||
| ISSN | ||||||
| 収録物識別子タイプ | EISSN | |||||
| 収録物識別子 | 2378363X | |||||
| DOI | ||||||
| 関連タイプ | isVersionOf | |||||
| 識別子タイプ | DOI | |||||
| 関連識別子 | https://doi.org/10.1109/INDIN.2018.8472028 | |||||
| 権利 | ||||||
| 権利情報 | © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | |||||
| 著者版フラグ | ||||||
| 出版タイプ | AM | |||||
| 出版タイプResource | http://purl.org/coar/version/c_ab4af688f83e57aa | |||||
