Trajectory Planning for a Mobile Robot in a Dynamic Environment Using an LSTM Neural Network

Autonomous mobile robots are an important focus of current research due to the advantages they bring to the industry, such as performing dangerous tasks with greater precision than humans. An autonomous mobile robot must be able to generate a collision-free trajectory while avoiding static and dynam...

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Autores principales:	Alejandra Molina-Leal, Alfonso Gómez-Espinosa, Jesús Arturo Escobedo Cabello, Enrique Cuan-Urquizo, Sergio R. Cruz-Ramírez
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	mobile robot obstacle avoidance LSTM neural network dynamic path planning Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/3ccd13899eab42eabd62f274844e3983
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spelling	oai:doaj.org-article:3ccd13899eab42eabd62f274844e39832021-11-25T16:35:20ZTrajectory Planning for a Mobile Robot in a Dynamic Environment Using an LSTM Neural Network10.3390/app1122106892076-3417https://doaj.org/article/3ccd13899eab42eabd62f274844e39832021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/10689https://doaj.org/toc/2076-3417Autonomous mobile robots are an important focus of current research due to the advantages they bring to the industry, such as performing dangerous tasks with greater precision than humans. An autonomous mobile robot must be able to generate a collision-free trajectory while avoiding static and dynamic obstacles from the specified start location to the target location. Machine learning, a sub-field of artificial intelligence, is applied to create a Long Short-Term Memory (LSTM) neural network that is implemented and executed to allow a mobile robot to find the trajectory between two points and navigate while avoiding a dynamic obstacle. The input of the network is the distance between the mobile robot and the obstacles thrown by the LiDAR sensor, the desired target location, and the mobile robot’s location with respect to the odometry reference frame. Using the model to learn the mapping between input and output in the sample data, the linear and angular velocity of the mobile robot are obtained. The mobile robot and its dynamic environment are simulated in Gazebo, which is an open-source 3D robotics simulator. Gazebo can be synchronized with ROS (Robot Operating System). The computational experiments show that the network model can plan a safe navigation path in a dynamic environment. The best test accuracy obtained was 99.24%, where the model can generalize other trajectories for which it was not specifically trained within a <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>15</mn><mrow><mo> </mo><mi>cm</mi></mrow></mrow></semantics></math></inline-formula> radius of a trained destination position.Alejandra Molina-LealAlfonso Gómez-EspinosaJesús Arturo Escobedo CabelloEnrique Cuan-UrquizoSergio R. Cruz-RamírezMDPI AGarticlemobile robotobstacle avoidanceLSTM neural networkdynamic path planningTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10689, p 10689 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	mobile robot obstacle avoidance LSTM neural network dynamic path planning Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	mobile robot obstacle avoidance LSTM neural network dynamic path planning Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Alejandra Molina-Leal Alfonso Gómez-Espinosa Jesús Arturo Escobedo Cabello Enrique Cuan-Urquizo Sergio R. Cruz-Ramírez Trajectory Planning for a Mobile Robot in a Dynamic Environment Using an LSTM Neural Network
description	Autonomous mobile robots are an important focus of current research due to the advantages they bring to the industry, such as performing dangerous tasks with greater precision than humans. An autonomous mobile robot must be able to generate a collision-free trajectory while avoiding static and dynamic obstacles from the specified start location to the target location. Machine learning, a sub-field of artificial intelligence, is applied to create a Long Short-Term Memory (LSTM) neural network that is implemented and executed to allow a mobile robot to find the trajectory between two points and navigate while avoiding a dynamic obstacle. The input of the network is the distance between the mobile robot and the obstacles thrown by the LiDAR sensor, the desired target location, and the mobile robot’s location with respect to the odometry reference frame. Using the model to learn the mapping between input and output in the sample data, the linear and angular velocity of the mobile robot are obtained. The mobile robot and its dynamic environment are simulated in Gazebo, which is an open-source 3D robotics simulator. Gazebo can be synchronized with ROS (Robot Operating System). The computational experiments show that the network model can plan a safe navigation path in a dynamic environment. The best test accuracy obtained was 99.24%, where the model can generalize other trajectories for which it was not specifically trained within a <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>15</mn><mrow><mo> </mo><mi>cm</mi></mrow></mrow></semantics></math></inline-formula> radius of a trained destination position.
format	article
author	Alejandra Molina-Leal Alfonso Gómez-Espinosa Jesús Arturo Escobedo Cabello Enrique Cuan-Urquizo Sergio R. Cruz-Ramírez
author_facet	Alejandra Molina-Leal Alfonso Gómez-Espinosa Jesús Arturo Escobedo Cabello Enrique Cuan-Urquizo Sergio R. Cruz-Ramírez
author_sort	Alejandra Molina-Leal
title	Trajectory Planning for a Mobile Robot in a Dynamic Environment Using an LSTM Neural Network
title_short	Trajectory Planning for a Mobile Robot in a Dynamic Environment Using an LSTM Neural Network
title_full	Trajectory Planning for a Mobile Robot in a Dynamic Environment Using an LSTM Neural Network
title_fullStr	Trajectory Planning for a Mobile Robot in a Dynamic Environment Using an LSTM Neural Network
title_full_unstemmed	Trajectory Planning for a Mobile Robot in a Dynamic Environment Using an LSTM Neural Network
title_sort	trajectory planning for a mobile robot in a dynamic environment using an lstm neural network
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/3ccd13899eab42eabd62f274844e3983
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Trajectory Planning for a Mobile Robot in a Dynamic Environment Using an LSTM Neural Network

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