Time Series Forecasting of Mobile Robot Motion Sensors Using LSTM Networks-Reference-Cited by-同舟云学术

Time Series Forecasting of Mobile Robot Motion Sensors Using LSTM Networks

Published:2021-12-01 Issue:2 Volume:26 Page:150-157
ISSN:2255-8691
Container-title:Applied Computer Systems
language:en
Short-container-title:

Author:

Vagale Anete¹^ORCID,Šteina Luīze²,Vēciņš Valters³

Affiliation:

1. Norwegian University of Science and Technology , Aalesund , Norway

2. LLC Robotic Solutions , Riga , Latvia

3. Riga Technical University , Riga , Latvia

Abstract

Abstract Deep neural networks are a tool for acquiring an approximation of the robot mathematical model without available information about its parameters. This paper compares the LSTM, stacked LSTM and phased LSTM architectures for time series forecasting. In this paper, motion sensor data from mobile robot driving episodes are used as the experimental data. From the experiment, the models show better results for short-term prediction, where the LSTM stacked model slightly outperforms the other two models. Finally, the predicted and actual trajectories of the robot are compared.

Publisher

Walter de Gruyter GmbH

Link

https://www.sciendo.com/pdf/10.2478/acss-2021-0018

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