Sensor-Based Mobile Robot Navigation via Deep Reinforcement Learning
Navigation tasks for mobile robots have been widely studied over past several years. More recently, there have been many attempts to introduce the usage of machine learning algorithms. Deep learning techniques are of special importance because they have achieved excellent performance in various fiel...
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| Veröffentlicht in: | International Conference on Big Data and Smart Computing S. 147 - 154 |
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| Format: | Tagungsbericht |
| Sprache: | Englisch |
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IEEE
01.01.2018
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| ISSN: | 2375-9356 |
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| Abstract | Navigation tasks for mobile robots have been widely studied over past several years. More recently, there have been many attempts to introduce the usage of machine learning algorithms. Deep learning techniques are of special importance because they have achieved excellent performance in various fields, including robot navigation. Deep learning methods, however, require considerable amount of data for training deep learning models and their results may be difficult to interpret for researchers. To address this issue, we propose a novel model for mobile robot navigation using deep reinforcement learning. In our navigation tasks, no information about the environment is given to the robot beforehand. Additionally, the positions of obstacles and goal change in every episode. In order to succeed under these conditions, we combine several Q-learning techniques that are considered to be state-of-the-art. We first provide a description of our model and then verify it through a series of experiments. |
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| AbstractList | Navigation tasks for mobile robots have been widely studied over past several years. More recently, there have been many attempts to introduce the usage of machine learning algorithms. Deep learning techniques are of special importance because they have achieved excellent performance in various fields, including robot navigation. Deep learning methods, however, require considerable amount of data for training deep learning models and their results may be difficult to interpret for researchers. To address this issue, we propose a novel model for mobile robot navigation using deep reinforcement learning. In our navigation tasks, no information about the environment is given to the robot beforehand. Additionally, the positions of obstacles and goal change in every episode. In order to succeed under these conditions, we combine several Q-learning techniques that are considered to be state-of-the-art. We first provide a description of our model and then verify it through a series of experiments. |
| Author | Loaiciga, Jorge Benz, Philipp Han, Seung-Ho Choi, Ho-Jin |
| Author_xml | – sequence: 1 givenname: Seung-Ho surname: Han fullname: Han, Seung-Ho – sequence: 2 givenname: Ho-Jin surname: Choi fullname: Choi, Ho-Jin – sequence: 3 givenname: Philipp surname: Benz fullname: Benz, Philipp – sequence: 4 givenname: Jorge surname: Loaiciga fullname: Loaiciga, Jorge |
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| Snippet | Navigation tasks for mobile robots have been widely studied over past several years. More recently, there have been many attempts to introduce the usage of... |
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| StartPage | 147 |
| SubjectTerms | Data models deep reinforcement learning mobile robot navigation Mobile robots Navigation Q-learning Robot sensing systems sensor based navigation Task analysis Training |
| Title | Sensor-Based Mobile Robot Navigation via Deep Reinforcement Learning |
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