Social Robot Navigation Tasks: Combining Machine Learning Techniques and Social Force Model

Social robot navigation in public spaces, buildings or private houses is a difficult problem that is not well solved due to environmental constraints (buildings, static objects etc.), pedestrians and other mobile vehicles. Moreover, robots have to move in a human-aware manner—that is, robots have to...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Óscar Gil, Anaís Garrell, Alberto Sanfeliu
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	social robot navigation Social Force Model Reinforcement Learning Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/d468d70c534c4954a818b4f587901ee4
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:d468d70c534c4954a818b4f587901ee4
record_format	dspace
spelling	oai:doaj.org-article:d468d70c534c4954a818b4f587901ee42021-11-11T19:06:15ZSocial Robot Navigation Tasks: Combining Machine Learning Techniques and Social Force Model10.3390/s212170871424-8220https://doaj.org/article/d468d70c534c4954a818b4f587901ee42021-10-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7087https://doaj.org/toc/1424-8220Social robot navigation in public spaces, buildings or private houses is a difficult problem that is not well solved due to environmental constraints (buildings, static objects etc.), pedestrians and other mobile vehicles. Moreover, robots have to move in a human-aware manner—that is, robots have to navigate in such a way that people feel safe and comfortable. In this work, we present two navigation tasks, social robot navigation and robot accompaniment, which combine machine learning techniques with the Social Force Model (SFM) allowing human-aware social navigation. The robots in both approaches use data from different sensors to capture the environment knowledge as well as information from pedestrian motion. The two navigation tasks make use of the SFM, which is a general framework in which human motion behaviors can be expressed through a set of functions depending on the pedestrians’ relative and absolute positions and velocities. Additionally, in both social navigation tasks, the robot’s motion behavior is learned using machine learning techniques: in the first case using supervised deep learning techniques and, in the second case, using Reinforcement Learning (RL). The machine learning techniques are combined with the SFM to create navigation models that behave in a social manner when the robot is navigating in an environment with pedestrians or accompanying a person. The validation of the systems was performed with a large set of simulations and real-life experiments with a new humanoid robot denominated IVO and with an aerial robot. The experiments show that the combination of SFM and machine learning can solve human-aware robot navigation in complex dynamic environments.Óscar GilAnaís GarrellAlberto SanfeliuMDPI AGarticlesocial robot navigationSocial Force ModelReinforcement LearningChemical technologyTP1-1185ENSensors, Vol 21, Iss 7087, p 7087 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	social robot navigation Social Force Model Reinforcement Learning Chemical technology TP1-1185
spellingShingle	social robot navigation Social Force Model Reinforcement Learning Chemical technology TP1-1185 Óscar Gil Anaís Garrell Alberto Sanfeliu Social Robot Navigation Tasks: Combining Machine Learning Techniques and Social Force Model
description	Social robot navigation in public spaces, buildings or private houses is a difficult problem that is not well solved due to environmental constraints (buildings, static objects etc.), pedestrians and other mobile vehicles. Moreover, robots have to move in a human-aware manner—that is, robots have to navigate in such a way that people feel safe and comfortable. In this work, we present two navigation tasks, social robot navigation and robot accompaniment, which combine machine learning techniques with the Social Force Model (SFM) allowing human-aware social navigation. The robots in both approaches use data from different sensors to capture the environment knowledge as well as information from pedestrian motion. The two navigation tasks make use of the SFM, which is a general framework in which human motion behaviors can be expressed through a set of functions depending on the pedestrians’ relative and absolute positions and velocities. Additionally, in both social navigation tasks, the robot’s motion behavior is learned using machine learning techniques: in the first case using supervised deep learning techniques and, in the second case, using Reinforcement Learning (RL). The machine learning techniques are combined with the SFM to create navigation models that behave in a social manner when the robot is navigating in an environment with pedestrians or accompanying a person. The validation of the systems was performed with a large set of simulations and real-life experiments with a new humanoid robot denominated IVO and with an aerial robot. The experiments show that the combination of SFM and machine learning can solve human-aware robot navigation in complex dynamic environments.
format	article
author	Óscar Gil Anaís Garrell Alberto Sanfeliu
author_facet	Óscar Gil Anaís Garrell Alberto Sanfeliu
author_sort	Óscar Gil
title	Social Robot Navigation Tasks: Combining Machine Learning Techniques and Social Force Model
title_short	Social Robot Navigation Tasks: Combining Machine Learning Techniques and Social Force Model
title_full	Social Robot Navigation Tasks: Combining Machine Learning Techniques and Social Force Model
title_fullStr	Social Robot Navigation Tasks: Combining Machine Learning Techniques and Social Force Model
title_full_unstemmed	Social Robot Navigation Tasks: Combining Machine Learning Techniques and Social Force Model
title_sort	social robot navigation tasks: combining machine learning techniques and social force model
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/d468d70c534c4954a818b4f587901ee4
work_keys_str_mv	AT oscargil socialrobotnavigationtaskscombiningmachinelearningtechniquesandsocialforcemodel AT anaisgarrell socialrobotnavigationtaskscombiningmachinelearningtechniquesandsocialforcemodel AT albertosanfeliu socialrobotnavigationtaskscombiningmachinelearningtechniquesandsocialforcemodel
_version_	1718431612785393664

Social Robot Navigation Tasks: Combining Machine Learning Techniques and Social Force Model

Ejemplares similares