Solving the optimal path planning of a mobile robot using improved Q-learning
Q-learning, a type of reinforcement learning, has gained increasing popularity in autonomous mobile robot path planning recently, due to its self-learning ability without requiring a priori model of the environment. Yet, despite such advantage, Q-learning exhibits slow convergence to the optimal sol...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2019
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/4217/ http://eprints.uthm.edu.my/4217/1/AJ%202019%20%28253%29.pdf |
| _version_ | 1848888227410214912 |
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| author | Low, Ee Soong Ong, Pauline Cheah, Kah Chun |
| author_facet | Low, Ee Soong Ong, Pauline Cheah, Kah Chun |
| author_sort | Low, Ee Soong |
| building | UTHM Institutional Repository |
| collection | Online Access |
| description | Q-learning, a type of reinforcement learning, has gained increasing popularity in autonomous mobile robot path planning recently, due to its self-learning ability without requiring a priori model of the environment. Yet, despite such advantage, Q-learning exhibits slow convergence to the optimal solution. In order to address this limitation, the concept of partially guided Q-learning is introduced wherein, the flower pollination algorithm (FPA) is utilized to improve the initialization of Q-learning. Experimental evaluation of the proposed improved Q-learning under the challenging environment with a different layout of obstacles shows that the convergence of Q-learning can be accelerated when Q-values are initialized appropriately using the FPA. Additionally, the effectiveness of the proposed algorithm is validated in a real-world experiment using a three-wheeled mobile robot. |
| first_indexed | 2025-11-15T20:06:56Z |
| format | Article |
| id | uthm-4217 |
| institution | Universiti Tun Hussein Onn Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T20:06:56Z |
| publishDate | 2019 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | uthm-42172021-12-01T05:41:23Z http://eprints.uthm.edu.my/4217/ Solving the optimal path planning of a mobile robot using improved Q-learning Low, Ee Soong Ong, Pauline Cheah, Kah Chun LB1050.9-1091 Educational psychology TJ210.2-211.47 Mechanical devices and figures. Automata. Ingenious mechanisms. Robots (General) Q-learning, a type of reinforcement learning, has gained increasing popularity in autonomous mobile robot path planning recently, due to its self-learning ability without requiring a priori model of the environment. Yet, despite such advantage, Q-learning exhibits slow convergence to the optimal solution. In order to address this limitation, the concept of partially guided Q-learning is introduced wherein, the flower pollination algorithm (FPA) is utilized to improve the initialization of Q-learning. Experimental evaluation of the proposed improved Q-learning under the challenging environment with a different layout of obstacles shows that the convergence of Q-learning can be accelerated when Q-values are initialized appropriately using the FPA. Additionally, the effectiveness of the proposed algorithm is validated in a real-world experiment using a three-wheeled mobile robot. Elsevier 2019 Article PeerReviewed text en http://eprints.uthm.edu.my/4217/1/AJ%202019%20%28253%29.pdf Low, Ee Soong and Ong, Pauline and Cheah, Kah Chun (2019) Solving the optimal path planning of a mobile robot using improved Q-learning. Robotics and Autonomous Systems, 115. pp. 143-161. ISSN 0921-8890 https://doi.org/10.1016/j.robot.2019.02.013 |
| spellingShingle | LB1050.9-1091 Educational psychology TJ210.2-211.47 Mechanical devices and figures. Automata. Ingenious mechanisms. Robots (General) Low, Ee Soong Ong, Pauline Cheah, Kah Chun Solving the optimal path planning of a mobile robot using improved Q-learning |
| title | Solving the optimal path planning of a mobile robot using improved Q-learning |
| title_full | Solving the optimal path planning of a mobile robot using improved Q-learning |
| title_fullStr | Solving the optimal path planning of a mobile robot using improved Q-learning |
| title_full_unstemmed | Solving the optimal path planning of a mobile robot using improved Q-learning |
| title_short | Solving the optimal path planning of a mobile robot using improved Q-learning |
| title_sort | solving the optimal path planning of a mobile robot using improved q-learning |
| topic | LB1050.9-1091 Educational psychology TJ210.2-211.47 Mechanical devices and figures. Automata. Ingenious mechanisms. Robots (General) |
| url | http://eprints.uthm.edu.my/4217/ http://eprints.uthm.edu.my/4217/ http://eprints.uthm.edu.my/4217/1/AJ%202019%20%28253%29.pdf |