<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In order to adapt to navigation in unknown environment, the mobile robot must have intelligent abilities, such as environment cognition, behavior decision and learning. The navigation control algorithm is researched based on Q learning method in this paper. Firstly, the corresponding environment state space is divided. The action sets mapping with states are set. And the reward function is designed which combines discrete reward returns and continuous reward. The feasibility of this algorithm is verified by computer simulation.</p>
</abstract>

In order to adapt to navigation in unknown environment, the mobile robot must have intelligent abilities, such as environment cognition, behavior decision and learning. The navigation control algorithm is researched based on Q learning method in this paper. Firstly, the corresponding environment state space is divided. The action sets mapping with states are set. And the reward function is designed which combines discrete reward returns and continuous reward. The feasibility of this algorithm is verified by computer simulation.

Behavior Control Algorithm for Mobile Robot Based on Q-Learning

International Journal of Advanced Network, Monitoring and Controls

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Behavior Control Algorithm for Mobile Robot Based on Q-Learning"}

In order to adapt to navigation in unknown environment, the mobile robot must have intelligent abilities, such as environment cognition, behavior decision and...

Behavior Control Algorithm for Mobile Robot Based on Q-Learning

Online veröffentlicht: 07. Apr. 2018

Seitenbereich: 108 - 114

DOI: https://doi.org/10.21307/iccnea.2017.26

Schlüsselwörter
Q-learning, navigation, behavior control, simulation

© 2017 Shiqiang Yang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Behavior Control Algorithm for Mobile Robot Based on Q-Learning

Online veröffentlicht: 07. Apr. 2018

Seitenbereich: 108 - 114

DOI: https://doi.org/10.21307/iccnea.2017.26

SchlüsselwörterQ-learning, navigation, behavior control, simulation

© 2017 Shiqiang Yang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
Q-learning, navigation, behavior control, simulation