Driving Mobile Robots using a Deep LSTM Architecture: An Experimental Approach

Driving a mobile robot consists in moving the robot to its goal position quickly, without colliding with obstacles, and with no deviations from the planned path. This process requires a skilled pilot, with expertise and well trained. Among the various techniques available to help pilots to drive mob...

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Bibliographic Details
Published in:	Revista IEEE América Latina Vol. 19; no. 3; pp. 481 - 489
Main Authors:	S. Santos, Thulio Noslen, Vinicius Lamar, Marcus, Koike, Carla M., De Barros Vidal, Flavio
Format:	Journal Article
Language:	English
Published:	Los Alamitos IEEE 01-03-2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Data integration Deep learning Graphics processing units IEEE transactions LSTM Mobile robots Proposals Robot sensing systems Robots Root mean square Short term
Online Access:	Get full text
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Summary:	Driving a mobile robot consists in moving the robot to its goal position quickly, without colliding with obstacles, and with no deviations from the planned path. This process requires a skilled pilot, with expertise and well trained. Among the various techniques available to help pilots to drive mobile robots, many of them are based on automatic learning approaches, as deep learning. This work presents the proposal of a new approaching for applications using a deep Long-Short Term Memory (LSTM) architecture, to assist the driving activities in mobile robots, using data extracted from an expert pilot as the main source of learning data. The main contributions of this proposal are i) a new application of the architecture for deep LSTM; ii) a new information data fusion strategy in the guidance command stage; and iii) a large number of tests in scenarios using a real mobile robot.
ISSN:	1548-0992 1548-0992
DOI:	10.1109/TLA.2021.9447698