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

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...

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Bibliographic Details
Published in:Mathematics (Basel) 2023-04, Vol.11 (7), p.1723
Main Authors: Matei, Alexandru, Precup, Stefan-Alexandru, Circa, Dragos, Gellert, Arpad, Zamfirescu, Constantin-Bala
Format: Article
Language:English
Subjects:
AMR
Artificial intelligence
Automation
Datasets
digital twin
Digital twins
Energy consumption
Estimation
Forecasts and trends
Global positioning systems
GPS
Localization
LSTM
Manufacturing
Mathematics
Methods
Mobile robots
Neural networks
Robots
Sensors
simulation
Synthetic data
Travel
Travel time
Travel time (Traffic engineering)
travel time estimation
Vehicles
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Summary: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%.
ISSN:2227-7390
2227-7390
DOI:10.3390/math11071723