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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
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    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. 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).
    3. 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.
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    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.

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