IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i7p1723-d1115872.html
   My bibliography  Save this article

Estimating Travel Time for Autonomous Mobile Robots through Long Short-Term Memory

Author

Listed:
  • Alexandru Matei

    (Computer Science and Electrical Engineering Department, Lucian Blaga University of Sibiu, 550025 Sibiu, Romania)

  • Stefan-Alexandru Precup

    (Computer Science and Electrical Engineering Department, Lucian Blaga University of Sibiu, 550025 Sibiu, Romania)

  • Dragos Circa

    (Computer Science and Electrical Engineering Department, Lucian Blaga University of Sibiu, 550025 Sibiu, Romania)

  • Arpad Gellert

    (Computer Science and Electrical Engineering Department, Lucian Blaga University of Sibiu, 550025 Sibiu, Romania)

  • Constantin-Bala Zamfirescu

    (Computer Science and Electrical Engineering Department, Lucian Blaga University of Sibiu, 550025 Sibiu, Romania)

Abstract

Autonomous mobile robots (AMRs) are gaining popularity in various applications such as logistics, manufacturing, and healthcare. One of the key challenges in deploying AMR is estimating their travel time accurately, which is crucial for efficient operation and planning. In this article, we propose a novel approach for estimating travel time for AMR using Long Short-Term Memory (LSTM) networks. Our approach involves training the network using synthetic data generated in a simulation environment using a digital twin of the AMR, which is a virtual representation of the physical robot. The results show that the proposed solution improves the travel time estimation when compared to a baseline, traditional mathematical model. While the baseline method has an error of 6.12%, the LSTM approach has only 2.13%.

Suggested Citation

  • Alexandru Matei & Stefan-Alexandru Precup & Dragos Circa & Arpad Gellert & Constantin-Bala Zamfirescu, 2023. "Estimating Travel Time for Autonomous Mobile Robots through Long Short-Term Memory," Mathematics, MDPI, vol. 11(7), pages 1-19, April.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:7:p:1723-:d:1115872
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/7/1723/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/7/1723/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lin, Yu & Lu, Qin & Tan, Bin & Yu, Yuanyuan, 2022. "Forecasting energy prices using a novel hybrid model with variational mode decomposition," Energy, Elsevier, vol. 246(C).
    2. Kun Wang & Yiming Yang & Ruixue Li, 2020. "Travel time models for the rack-moving mobile robot system," International Journal of Production Research, Taylor & Francis Journals, vol. 58(14), pages 4367-4385, July.
    3. Huang, Xiaoqiao & Li, Qiong & Tai, Yonghang & Chen, Zaiqing & Liu, Jun & Shi, Junsheng & Liu, Wuming, 2022. "Time series forecasting for hourly photovoltaic power using conditional generative adversarial network and Bi-LSTM," Energy, Elsevier, vol. 246(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lixiong Lin & Zhiping Xu & Jiachun Zheng, 2023. "Predefined Time Active Disturbance Rejection for Nonholonomic Mobile Robots," Mathematics, MDPI, vol. 11(12), pages 1-21, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Guici Chen & Tingting Zhang & Wenyu Qu & Wenbo Wang, 2023. "Photovoltaic Power Prediction Based on VMD-BRNN-TSP," Mathematics, MDPI, vol. 11(4), pages 1-14, February.
    2. Junhao Wu & Yuan Hu & Daqing Wu & Zhengyong Yang, 2022. "An Aquatic Product Price Forecast Model Using VMD-IBES-LSTM Hybrid Approach," Agriculture, MDPI, vol. 12(8), pages 1-26, August.
    3. Xiao, Zenan & Huang, Xiaoqiao & Liu, Jun & Li, Chengli & Tai, Yonghang, 2023. "A novel method based on time series ensemble model for hourly photovoltaic power prediction," Energy, Elsevier, vol. 276(C).
    4. Zhang, Yunfei & Zhou, Zhihua & Liu, Junwei & Yuan, Jianjuan, 2022. "Data augmentation for improving heating load prediction of heating substation based on TimeGAN," Energy, Elsevier, vol. 260(C).
    5. Zhuang, Yanling & Zhou, Yun & Yuan, Yufei & Hu, Xiangpei & Hassini, Elkafi, 2022. "Order picking optimization with rack-moving mobile robots and multiple workstations," European Journal of Operational Research, Elsevier, vol. 300(2), pages 527-544.
    6. Huang, Songtao & Zhou, Qingguo & Shen, Jun & Zhou, Heng & Yong, Binbin, 2024. "Multistage spatio-temporal attention network based on NODE for short-term PV power forecasting," Energy, Elsevier, vol. 290(C).
    7. Tian, Zhirui & Wang, Jiyang, 2023. "A wind speed prediction system based on new data preprocessing strategy and improved multi-objective optimizer," Renewable Energy, Elsevier, vol. 215(C).
    8. Stefenon, Stefano Frizzo & Seman, Laio Oriel & Aquino, Luiza Scapinello & Coelho, Leandro dos Santos, 2023. "Wavelet-Seq2Seq-LSTM with attention for time series forecasting of level of dams in hydroelectric power plants," Energy, Elsevier, vol. 274(C).
    9. Beibei Hu & Yunhe Cheng, 2023. "Prediction of Regional Carbon Price in China Based on Secondary Decomposition and Nonlinear Error Correction," Energies, MDPI, vol. 16(11), pages 1-22, May.
    10. Zang, Haixiang & Chen, Dianhao & Liu, Jingxuan & Cheng, Lilin & Sun, Guoqiang & Wei, Zhinong, 2024. "Improving ultra-short-term photovoltaic power forecasting using a novel sky-image-based framework considering spatial-temporal feature interaction," Energy, Elsevier, vol. 293(C).
    11. Mark Kipngetich Kiptoo & Oludamilare Bode Adewuyi & Masahiro Furukakoi & Paras Mandal & Tomonobu Senjyu, 2023. "Integrated Multi-Criteria Planning for Resilient Renewable Energy-Based Microgrid Considering Advanced Demand Response and Uncertainty," Energies, MDPI, vol. 16(19), pages 1-25, September.
    12. Haider A. Khan & Shahryar Ghorbani & Elham Shabani & Shahab S. Band, 2024. "Enhancement of Neural Networks Model’s Predictions of Currencies Exchange Rates by Phase Space Reconstruction and Harris Hawks’ Optimization," Computational Economics, Springer;Society for Computational Economics, vol. 63(2), pages 835-860, February.
    13. Liu, Jingxuan & Zang, Haixiang & Zhang, Fengchun & Cheng, Lilin & Ding, Tao & Wei, Zhinong & Sun, Guoqiang, 2023. "A hybrid meteorological data simulation framework based on time-series generative adversarial network for global daily solar radiation estimation," Renewable Energy, Elsevier, vol. 219(P1).
    14. Wang, Min & Rao, Congjun & Xiao, Xinping & Hu, Zhuo & Goh, Mark, 2024. "Efficient shrinkage temporal convolutional network model for photovoltaic power prediction," Energy, Elsevier, vol. 297(C).
    15. Xu, Shaozhen & Liu, Jun & Huang, Xiaoqiao & Li, Chengli & Chen, Zaiqing & Tai, Yonghang, 2024. "Minutely multi-step irradiance forecasting based on all-sky images using LSTM-InformerStack hybrid model with dual feature enhancement," Renewable Energy, Elsevier, vol. 224(C).
    16. Huang, Congzhi & Yang, Mengyuan, 2023. "Memory long and short term time series network for ultra-short-term photovoltaic power forecasting," Energy, Elsevier, vol. 279(C).
    17. Zhi, Yuan & Yang, Xudong, 2023. "Scenario-based multi-objective optimization strategy for rural PV-battery systems," Applied Energy, Elsevier, vol. 345(C).
    18. Chen, Zhiqiang & Li, Jianbin & Cheng, Long & Liu, Xiufeng, 2023. "Federated-WDCGAN: A federated smart meter data sharing framework for privacy preservation," Applied Energy, Elsevier, vol. 334(C).
    19. Jesús Molina‐Muñoz & Andrés Mora‐Valencia & Javier Perote, 2024. "Predicting carbon and oil price returns using hybrid models based on machine and deep learning," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 31(2), June.
    20. Wang, Jia & Wang, Xinyi & Wang, Xu, 2024. "International oil shocks and the volatility forecasting of Chinese stock market based on machine learning combination models," The North American Journal of Economics and Finance, Elsevier, vol. 70(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:11:y:2023:i:7:p:1723-:d:1115872. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.