Time Series Forecasting of Mobile Robot Motion Sensors Using LSTM Networks

2021
Anete Vagale, Luīze Šteina, Valters Vēciņš

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.

Keywords
Deep neural networks, Long short-term memory (LSTM), Mobile robot, Time series forecasting
DOI
10.2478/acss-2021-0018
Hyperlink
https://www.sciendo.com/article/10.2478/acss-2021-0018

Vagale, A., Šteina, L., Vēciņš, V. Time Series Forecasting of Mobile Robot Motion Sensors Using LSTM Networks. Applied Computer Systems, 2021, Vol. 26, No. 2, pp.150-157. ISSN 2255-8683. e-ISSN 2255-8691. Available from: doi:10.2478/acss-2021-0018

Publication language
English (en)