1. bookTom 26 (2021): Zeszyt 2 (December 2021)
Informacje o czasopiśmie
License
Format
Czasopismo
eISSN
2255-8691
Pierwsze wydanie
08 Nov 2012
Częstotliwość wydawania
2 razy w roku
Języki
Angielski
Otwarty dostęp

Time Series Forecasting of Mobile Robot Motion Sensors Using LSTM Networks

Data publikacji: 30 Dec 2021
Tom & Zeszyt: Tom 26 (2021) - Zeszyt 2 (December 2021)
Zakres stron: 150 - 157
Informacje o czasopiśmie
License
Format
Czasopismo
eISSN
2255-8691
Pierwsze wydanie
08 Nov 2012
Częstotliwość wydawania
2 razy w roku
Języki
Angielski
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.

Keywords

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