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...
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2007
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| Acceso en línea: | https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_NIS12835_v216_n_p161_Pedrc http://hdl.handle.net/20.500.12110/paper_NIS12835_v216_n_p161_Pedrc |
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paper:paper_NIS12835_v216_n_p161_Pedrc2023-06-08T16:39:34Z Reinforcement learning for vision based mobile robots using the Hough Transform De Cristoforis, Pablo 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. 2007 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_NIS12835_v216_n_p161_Pedrc http://hdl.handle.net/20.500.12110/paper_NIS12835_v216_n_p161_Pedrc |
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Universidad de Buenos Aires |
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I-28 |
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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 De Cristoforis, Pablo 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. |
| author |
De Cristoforis, Pablo |
| author_facet |
De Cristoforis, Pablo |
| author_sort |
De Cristoforis, Pablo |
| 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 |
| publishDate |
2007 |
| url |
https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_NIS12835_v216_n_p161_Pedrc http://hdl.handle.net/20.500.12110/paper_NIS12835_v216_n_p161_Pedrc |
| work_keys_str_mv |
AT decristoforispablo reinforcementlearningforvisionbasedmobilerobotsusingthehoughtransform |
| _version_ |
1769175848056782848 |