<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</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.

Time Series Forecasting of Mobile Robot Motion Sensors Using LSTM Networks

Norwegian University of Science and Technology

Applied Computer Systems

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Time Series Forecasting of Mobile Robot Motion Sensors Using LSTM Networks"}

Deep neural networks are a tool for acquiring an approximation of the robot mathematical model without available information about its parameters. This paper...

Time Series Forecasting of Mobile Robot Motion Sensors Using LSTM Networks

Online veröffentlicht: 30. Dez. 2021

Seitenbereich: 150 - 157

DOI: https://doi.org/10.2478/acss-2021-0018

Schlüsselwörter
Deep neural networks, long short-term memory (LSTM), mobile robot, time series forecasting

© 2021 Anete Vagale et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Time Series Forecasting of Mobile Robot Motion Sensors Using LSTM Networks

Online veröffentlicht: 30. Dez. 2021

Seitenbereich: 150 - 157

DOI: https://doi.org/10.2478/acss-2021-0018

SchlüsselwörterDeep neural networks, long short-term memory (LSTM), mobile robot, time series forecasting

© 2021 Anete Vagale et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Deep neural networks, long short-term memory (LSTM), mobile robot, time series forecasting