Mobile robots autonomous exploration through deep reinforcement learning

The mobile robot autonomous exploration problem has been a hot research topic among scholars. The traditional robot autonomous exploration problem usually contains steps such as frontier detection, frontier clustering, navigation point assignment and so on. For robots, the quality and speed of front...

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Main Author: Yin, Hanqiu
Other Authors: Chau Yuen
Format: Thesis-Master by Coursework
Language:English
Published: Nanyang Technological University 2024
Subjects:
DQN
RL
Online Access:https://hdl.handle.net/10356/176603
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1766032024-05-17T15:49:00Z Mobile robots autonomous exploration through deep reinforcement learning Yin, Hanqiu Chau Yuen School of Electrical and Electronic Engineering chau.yuen@ntu.edu.sg Computer and Information Science Engineering Autonomous exploration DQN Frontier detection RL The mobile robot autonomous exploration problem has been a hot research topic among scholars. The traditional robot autonomous exploration problem usually contains steps such as frontier detection, frontier clustering, navigation point assignment and so on. For robots, the quality and speed of frontier detection and the speed of decision-making algorithms will have a large impact on the exploration efficiency of robots. Typically, traditional algorithms need to be designed in conjunction with expert experience, and when the exploration environment changes, the algorithms usually need to be redesigned. In order to improve the transferability and generalizability of robot autonomous exploration algorithms, the use of deep learning methods as decision-making modules is quite promising. Existing deep learning-based methods suffer from problems such as low learning efficiency and might fall into local minima. In this thesis, we propose a decision-making algorithm based on deep reinforcement learning. The algorithm adopts a deep learning method to effectively extract the environment features and automatically updates the strategy by interacting with the environment. The algorithm outputs the next target position of the mobile robot according to the current position of the mobile robot and the environment information. Through the existing proven navigation algorithms, the robot is navigated to the target point outputted by the decision module to realize the exploration of the unknown environment. Experiments show that the algorithm has good learning efficiency and adaptability to different environments. Master's degree 2024-05-17T07:56:31Z 2024-05-17T07:56:31Z 2024 Thesis-Master by Coursework Yin, H. (2024). Mobile robots autonomous exploration through deep reinforcement learning. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/176603 https://hdl.handle.net/10356/176603 en application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Computer and Information Science
Engineering
Autonomous exploration
DQN
Frontier detection
RL
spellingShingle Computer and Information Science
Engineering
Autonomous exploration
DQN
Frontier detection
RL
Yin, Hanqiu
Mobile robots autonomous exploration through deep reinforcement learning
description The mobile robot autonomous exploration problem has been a hot research topic among scholars. The traditional robot autonomous exploration problem usually contains steps such as frontier detection, frontier clustering, navigation point assignment and so on. For robots, the quality and speed of frontier detection and the speed of decision-making algorithms will have a large impact on the exploration efficiency of robots. Typically, traditional algorithms need to be designed in conjunction with expert experience, and when the exploration environment changes, the algorithms usually need to be redesigned. In order to improve the transferability and generalizability of robot autonomous exploration algorithms, the use of deep learning methods as decision-making modules is quite promising. Existing deep learning-based methods suffer from problems such as low learning efficiency and might fall into local minima. In this thesis, we propose a decision-making algorithm based on deep reinforcement learning. The algorithm adopts a deep learning method to effectively extract the environment features and automatically updates the strategy by interacting with the environment. The algorithm outputs the next target position of the mobile robot according to the current position of the mobile robot and the environment information. Through the existing proven navigation algorithms, the robot is navigated to the target point outputted by the decision module to realize the exploration of the unknown environment. Experiments show that the algorithm has good learning efficiency and adaptability to different environments.
author2 Chau Yuen
author_facet Chau Yuen
Yin, Hanqiu
format Thesis-Master by Coursework
author Yin, Hanqiu
author_sort Yin, Hanqiu
title Mobile robots autonomous exploration through deep reinforcement learning
title_short Mobile robots autonomous exploration through deep reinforcement learning
title_full Mobile robots autonomous exploration through deep reinforcement learning
title_fullStr Mobile robots autonomous exploration through deep reinforcement learning
title_full_unstemmed Mobile robots autonomous exploration through deep reinforcement learning
title_sort mobile robots autonomous exploration through deep reinforcement learning
publisher Nanyang Technological University
publishDate 2024
url https://hdl.handle.net/10356/176603
_version_ 1814047280780541952