Solving the optimal path planning of a mobile robot using improved Q-learning
| Autor: | Pauline Ong, Ee Soong Low, Kah Chun Cheah |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
Mathematical optimization Pollination Computer science General Mathematics Q-learning Initialization Mobile robot 02 engineering and technology Computer Science Applications 03 medical and health sciences 020901 industrial engineering & automation 0302 clinical medicine Control and Systems Engineering 030220 oncology & carcinogenesis Reinforcement learning Motion planning Software |
| Zdroj: | Robotics and Autonomous Systems. 115:143-161 |
| ISSN: | 0921-8890 |
| DOI: | 10.1016/j.robot.2019.02.013 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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