Reinforcement learning for vision based mobile robots using the Hough Transform

Vision-based perception gives autonomous robots the ability to perform a varied set of tasks, due to the great amount and quality of information it procures. Although Reinforcement Learning (RL) is a learning model that has made a great impact in the autonomous robots field, its application to visio...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autores principales: Pedrc, S., De Cristóforis, P., Bendersky, D., Santos, J.
Formato: CONF
Materias:
Hough transform
Large state space size
Reinforcement learning
Vision-based mobile robots
Feature extraction
Hough transforms
Mobile robots
Navigation
Exploration techniques
ITS applications
Learning for vision
Quality of information
State representation
Vision-based perception
Acceso en línea:http://hdl.handle.net/20.500.12110/paper_NIS12835_v216_n_p161_Pedrc
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id todo:paper_NIS12835_v216_n_p161_Pedrc
record_format dspace
spelling todo:paper_NIS12835_v216_n_p161_Pedrc2023-10-03T16:45:50Z Reinforcement learning for vision based mobile robots using the Hough Transform Pedrc, S. De Cristóforis, P. Bendersky, D. Santos, J. Hough transform Large state space size Reinforcement learning Vision-based mobile robots Feature extraction Hough transforms Mobile robots Navigation Exploration techniques ITS applications Large state space size Learning for vision Quality of information State representation Vision-based mobile robots Vision-based perception Reinforcement learning Vision-based perception gives autonomous robots the ability to perform a varied set of tasks, due to the great amount and quality of information it procures. Although Reinforcement Learning (RL) is a learning model that has made a great impact in the autonomous robots field, its application to vision-based perception has been limited. One of the main reasons for this fact is the size of the state space: raw images are usually simply too big to be used as states for the direct application of RL techniques. In this work, we present a method that uses the linear Hough Transform to detect straight lines in captured images. Using a state representation based on small number of straight lines inferred from images, we can reduce the size of state space, making it possible to use standard RL algorithms, such as Q-Learning. As a part of the method, we also present a. model-free exploration technique based on e-greedy action selection strategy. We carry out a series of experiments in order to verify the method for the task of navigating through a corridor with a vision-based mobile robot, either on a robot simulator and on a real vision-based minirobot called FenBot. Fil:De Cristóforis, P. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. CONF info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_NIS12835_v216_n_p161_Pedrc
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic Hough transform
Large state space size
Reinforcement learning
Vision-based mobile robots
Feature extraction
Hough transforms
Mobile robots
Navigation
Exploration techniques
ITS applications
Large state space size
Learning for vision
Quality of information
State representation
Vision-based mobile robots
Vision-based perception
Reinforcement learning
spellingShingle Hough transform
Large state space size
Reinforcement learning
Vision-based mobile robots
Feature extraction
Hough transforms
Mobile robots
Navigation
Exploration techniques
ITS applications
Large state space size
Learning for vision
Quality of information
State representation
Vision-based mobile robots
Vision-based perception
Reinforcement learning
Pedrc, S.
De Cristóforis, P.
Bendersky, D.
Santos, J.
Reinforcement learning for vision based mobile robots using the Hough Transform
topic_facet Hough transform
Large state space size
Reinforcement learning
Vision-based mobile robots
Feature extraction
Hough transforms
Mobile robots
Navigation
Exploration techniques
ITS applications
Large state space size
Learning for vision
Quality of information
State representation
Vision-based mobile robots
Vision-based perception
Reinforcement learning
description Vision-based perception gives autonomous robots the ability to perform a varied set of tasks, due to the great amount and quality of information it procures. Although Reinforcement Learning (RL) is a learning model that has made a great impact in the autonomous robots field, its application to vision-based perception has been limited. One of the main reasons for this fact is the size of the state space: raw images are usually simply too big to be used as states for the direct application of RL techniques. In this work, we present a method that uses the linear Hough Transform to detect straight lines in captured images. Using a state representation based on small number of straight lines inferred from images, we can reduce the size of state space, making it possible to use standard RL algorithms, such as Q-Learning. As a part of the method, we also present a. model-free exploration technique based on e-greedy action selection strategy. We carry out a series of experiments in order to verify the method for the task of navigating through a corridor with a vision-based mobile robot, either on a robot simulator and on a real vision-based minirobot called FenBot.
format CONF
author Pedrc, S.
De Cristóforis, P.
Bendersky, D.
Santos, J.
author_facet Pedrc, S.
De Cristóforis, P.
Bendersky, D.
Santos, J.
author_sort Pedrc, S.
title Reinforcement learning for vision based mobile robots using the Hough Transform
title_short Reinforcement learning for vision based mobile robots using the Hough Transform
title_full Reinforcement learning for vision based mobile robots using the Hough Transform
title_fullStr Reinforcement learning for vision based mobile robots using the Hough Transform
title_full_unstemmed Reinforcement learning for vision based mobile robots using the Hough Transform
title_sort reinforcement learning for vision based mobile robots using the hough transform
url http://hdl.handle.net/20.500.12110/paper_NIS12835_v216_n_p161_Pedrc
work_keys_str_mv AT pedrcs reinforcementlearningforvisionbasedmobilerobotsusingthehoughtransform
AT decristoforisp reinforcementlearningforvisionbasedmobilerobotsusingthehoughtransform
AT benderskyd reinforcementlearningforvisionbasedmobilerobotsusingthehoughtransform
AT santosj reinforcementlearningforvisionbasedmobilerobotsusingthehoughtransform
_version_ 1782031048635318272

Ejemplares similares